Optimized Information Hiding using Discrete Wavelet Transform and Genetic Algorithm
Research Paper | Journal Paper
Vol.6 , Issue.8 , pp.930-938, Aug-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i8.930938
Abstract
With rapid advances in the field of communication and information sharing, Steganography – the art of secret communication has gained much attention in recent years. Communicating sensitive information through media like text, sound image and video without being noticed by intruder has become a challenge. Steganography deals with the development of efficient algorithms by a combination of variety of techniques to achieve imperceptibility. This article analyzes the effect of using the Discrete Wavelet Transform for hiding secret data and optimizing it using the genetic algorithm to achieve better results.
Key-Words / Index Term
Spatial Domain, Transform Domain, Discrete Wavelet Transform (DWT), Genetic Algorithm, Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR)
References
[1] Jasleen Kour, Deepankar Verma, “Steganography Techniques – A Review Paper”, International Journal of Emerging Research in Management & Technology, Vol.3, Issue.5, pp.132-135, May 2014.
[2] Kodge B.G, “Information Security: A Review on Steganography with Cryptography for Secured Data Transaction”, International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue-6, p.p 1-4, Dec 2017.
[3] Samer Atawneh, Ammar Almomani, Putra Sumari, “Steganography in Digital Images: Common Approaches and Tools”, IETE TECHNICAL REVIEW, Vol.30, Issue.4, pp.344-358, July – Aug 2013.
[4] Seyyed Amin Seyyedi, Vasili Sadai, Nick Ivanov, “A Secure Steganography Method Based on Integer Lifting Wavelet Transform”, International Journal of Network Security, Vol.18, No.1, PP. 124-132, Jan 2016.
[5] S.Mudassar, M.Jamal, F.S.Mehmood, M.R.Rehman, “Resistance of Watermarked Image against Solidity Attacks”, International Journal of Scientific Research in Computer Science and Engineering, Vol 5, Issue 4, PP. 31-35, August 2017.
[6] Y.Taouil, E.B.Ameur, A.Souhar, A.El Harraj, M.T.Belghiti, “High Imperceptibility Image Steganography Methods based on HAAR DWT”, International Journal of Computer Applications, Vol.138, No.10, pp.38-43, March 2016.
[7] Hamad A.Al-Korbi, Ali Al-Ataby, Majid A. Al-Taee, Waleed Al-Nuaimy, “Highly Efficient Image Steganography Using HAAR DWT for Hiding Miscellaneous Data”, Jordanian Journal of Computers and Information Technology (JJCIT), Vol.2, No.1, pp.17-36, April 2016.
[8] Elham Ghasemi, Jamshid Shanbehzadeh, Nima Fassihi, “High Capacity Image Steganography using Wavelet Transform and Genetic Algorithm”, Proceedings of the International MultiConference of Engineers and Computer Scientists 2011, Vol.I, IMECS 2011, March 16-18 , 2011, Hong Kong.
[9] Prabakaran G, Dr.Bhavani .R, Sankaran.S, “Dual Transform Color Image Steganography”, International Journal of Innovative Research in Science, Engineering and Technology, Vol.3, Special Issue 1, pp.128-135, January 2014.
[10] Shamimunnisabi, Cauvery N.K, “Empirical Computation of RS-Analysis for Building Robust Steganography Using Integer Wavelet Transform and Genetic Algorithm ”, International Journal of Engineering Trends and Technology, Vol.3, Issue 3, pp.448-454, 2012.
[11] Usha .B.A, N.K.Srinath, Pulkita Sarthak, “High Capacity Data Embedding Method in Image Steganography using Genetic Algorithm”, International Journal of Computer Applications, Vol.121- No.14, p.p 30-33, July 2015.
[12] M.Vijay, V.Vignesh Kumar, “Image Steganography Method Using Integer Wavelet Transform”, International Journal of Innovative Research in Science, Engineering and Technology, Vol.3, Special Issue 3, p.p 1207-1210, March 2014.
[13] Hemalatha.S, U.Dinesh Acharya, Renuka.A, Priya R.Kamath, “A Secure and High Capacity Image Steganography Technique”, Signal & Image Processing: An International Journal (SIPIJ), Vol.4, No.1, p.p 83-89, February 2013.
[14] B.L.Lai, L.W.Chang, “Adaptive data hiding for images based on haar discrete wavelet transform ”, Advances in Image and Video Technology, Lecture Notes in Computer Science, p.p 1085-1093, Hsinchu, Taiwan, December 2006.
[15] H.S.M.Reddy, K.B.Raja, “Wavelet based non LSB steganography”, International Conference on Networking and Media Convergence , p.p 111-117, Cario, Egypt, March 2009.
[16] S. A. Seyyedi, V.Sadau, N.Ivanov, “A Secure Steganography Method Based on Integer Lifting Wavelet Transform”, International Journal of Network Security, Vol.18, No.1, pp.124-132, Jan 2016.
Citation
G. Umamaheswari, C.P. Sumathi, "Optimized Information Hiding using Discrete Wavelet Transform and Genetic Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.930-938, 2018.
Interference Aware, Topology, Power and Flow Control Channel Assignment Algorithm for Multi-Radio Multi-Channel Wireless Mesh Networks
Research Paper | Journal Paper
Vol.6 , Issue.8 , pp.939-947, Aug-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i8.939947
Abstract
Multi-Radio Multi-Channel Wireless Mesh Network (MRMC-WMN) is a multi-hop wireless network that consists of a large number of mesh nodes. The availability of cost-effective multi-radio wireless network interface cards makes it possible to achieve higher throughput based on simultaneous transmission and reception. Each network interface card is equipped with different radio channels. But due to limited non-overlapping channels, MRMC-WMN suffers from the co-channel interference. Co-channel interference limits the capacity of each link and degrades the performance of the whole network. Efficient performance can be achieved from MRMC-WMN only by intelligent channel assignment algorithm. In this paper, we propose the Interference aware, Topology, Power and Flow Control Channel Assignment (ITPFC) algorithm based on the joint contribution of important factors such as topology control, co-channel interference, power control and flow control. The aim of this channel assignment algorithm is to minimize the co-channel interference and provide the appropriate link capacity to each node so that the network performance can be maximized. We have used NS-3 based WiMesh tool for simulation and evaluated the network performance in terms of throughput, delay, packet loss and number of channels used. Results have been compared with existing channel assignment algorithms and it is found that ITPFC Channel Assignment algorithm achieves better results.
Key-Words / Index Term
Multi-Radio Multi-Channel Wireless Mesh Network, Channel Assignment, Network Interface Card, Power Control, Topology Control, Flow Control, WiMesh
References
[1] V.C. Gungor, E. Natalizio, P. Pace, S. Avallone, “Mesh Networks: Architectures and Protocols”, Springer Science Business Media Publisher, New York, USA, chap1, pp. 1-27, 2008.
[2] M. K. Marina, S.R. Das, A.P. Subramanian, “A topology control approach for utilizing multiple channels in multi-radio wireless mesh networks”, Computer Networks, Vol. 54, No. 2, pp. 241-256, 2010.
[3] S. Avallone, I.F. Akyildiz, “A channel assignment algorithm for multi-radio wireless mesh networks”, Computer Communications, Vol. 31, No. 7, pp. 1343–1353, 2008.
[4] N. Kaur, J.S. Saini, “Performance enhancement of 802.11 based wireless Mesh Network by using multi-radio multi-channel”, In the Proceeding of the 2013 IEEE International Conference on Green Computing, Communication and Conversation of Energy (ICGEC), Chennai, India, pp. 71–76, 2014.
[5] F. Bokhari, G. Zaruba, “Partially Overlapping Channel Assignments in Wireless Mesh Networks”, Intech Publisher, United Kingdom, Chap 5, pp. 103-130, 2012.
[6] J.S. Saini, B.S. Sohi B, “A survey on channel assignment techniques of multi-radio multi-channel wireless mesh networks”, Indian Journal of Science and Technology, Vol. 9, No. 42, pp. 1–8, 2016.
[7] W. Jihong, S. Wenxiao, J. Feng, “On channel assignment for multicast in multi-radio multi-channel wireless mesh networks: A survey”, China Communications, Vol. 12, No. 1, pp.122-135, 2015.
[8] P. Santi, “Topology control in wireless ad hoc and sensor networks”, ACM computing surveys, Vol. 37, No. 2, pp.164-194, 2005.
[9] X. Bao, W. Tan, J. Nie, C. Lu, G. Jin, “Design of logical topology with K-connected constraints and channel assignment for multi-radio wireless mesh networks”, International Journal of Communication Systems, Vol. 30, No. 1, pp. 1-18, 2014.
[10] Y. Shi, Y.T. Hou, H. Zhou, “Per-node based optimal power control for multi-hop cognitive radio networks”, IEEE Transactions on Wireless Communications, Vol. 8, No. 10, pp. 5290–5299, 2009.
[11] A. Ouni, H. Rivano, F. Valois, C. Rosenberg, “Energy and throughput optimization of wireless mesh networks with continuous power control”, IEEE Transactions on Wireless Communications, Vol. 14, No. 2, pp. 1131–1142, 2015.
[12] J.J. Galvez, P.M. Ruiz, A.F.G. Skarmeta, “TCP flow-aware Channel Re-Assignment in Multi-Radio Multi-Channel Wireless Mesh Networks”, In the Proceeding of Eighth IEEE International Conference on Mobile Ad-Hoc and Sensor Systems, Valencia, Spain pp. 262–271, 2011.
[13] S. Avallone, F.P. D’Elia, G. Ventre, “A new channel, power and rate assignment algorithm for multi-radio wireless mesh networks”, Telecommunication Systems, Vol. 51, No. 1, pp.73–80, 2012.
[14] F. Liu, Y. Bai, “An overview of topology control and channel assignment in multi-radio multi-channel wireless mesh networks”, Wireless Communications and Networking, Vol. 2012, No. 1, pp. 1–12, 2012.
[15] J.J. Galvez, P.M. Ruiz, “Efficient rate allocation, routing and channel assignment in wireless mesh networks supporting dynamic traffic flows”, Ad Hoc Networks, Vol. 11, No. 6, pp. 1765–1781, 2013.
[16] A.U. Chaudhry, R.H.M. Hafez, J.W. Chinneck , “On the impact of interference models on channel assignment in multi-radio multi-channel wireless mesh networks”, Ad Hoc Networks, Vol. 27, pp. 68–80, 2015.
[17] M. Shojafar, S. Abolfazli, H. Mostafaei, M. Singhal M, “Improving channel assignment in multi-radio wireless mesh networks with learning automata”, Wireless Personal Communications, Vol. 82, No. 1, pp. 61–80, 2015.
[18] A.T. Hoang, Y. Liang, M.H. Islam, “Power control and channel allocation in cognitive radio networks with primary users’ cooperation”, IEEE Transactions on Mobile Computing, Vol. 9, No. 3, pp. 348–360, 2010.
[19] A.R. Ulucinar, I. Korpeoglu, “Distributed joint flow-radio and channel assignment using partially overlapping channels in multi-radio wireless mesh networks”, Wireless Networks, Vol. 22, No. 1, pp. 83–104, 2016.
[20] A. Raniwala, K. Gopalan, T. Chiueh, “Centralized channel assignment and routing algorithms for multi-channel wireless mesh networks”, ACM SIGMOBILE Mobile Computing and Communications Review, Vol. 8, No. 2, pp. 50–65, 2004.
[21] J. Tang, G. Xue, W. Zhang, “Interference-aware topology control and QoS routing in multi-channel wireless mesh networks”, In the Proceeding of 6th ACM international symposium on Mobile ad hoc networking and computing (MobiHoc ’05), IL, USA, pp. 68–77, 2005.
[22] S. Avallone, I.F. Akyildiz, G. Ventre, “A channel and rate assignment algorithm and a layer-2.5 forwarding paradigm for multi-radio wireless mesh networks”, IEEE/ACM Transactions on Networking, Vol. 17, No. 1, pp. 267–280, 2009.
[23] S. Avallone, G.D. Stasi, “Design and implementation of WiMesh: A tool for the performance evaluation of multi-radio wireless mesh networks”, Journal of Network and Computer Applications, Vol. 63, pp. 98–109, 2016.
[24] M. Doraghinejad, H. Nezamabadi-Pour, A. Mahani, “Channel assignment in multi-radio wireless mesh networks using an improved gravitational search algorithm”, Journal of Network and Computer Applications, Vol. 38, No.1, pp. 163–171, 2014.
[25] V. Sarasvathi, N.C.SN. Iyengar, S. Saha, “An efficient interference aware partially overlapping channel assignment and routing in wireless mesh networks”, International Journal of Communication Networks and Information Security, Vol. 6, No. 1, pp. 52–61, 2014.
[26] J.W. Lin, S.M. Lin, “A weight-aware channel assignment algorithm for mobile multicast in wireless mesh networks”, Journal of Systems and Software, Vol. 94, pp. 98–107, 2014.
[27] D. Wu, S.H. Yang, L. Bao, C.H. Liu, “Joint multi-radio multi-channel assignment, scheduling, and routing in wireless mesh networks”, Wireless Networks, Vol. 20, No. 1, pp. 11–24, 2014.
[28] A.U. Chaudhry, N. Ahmad, R.H.M. Hafez, “Improving throughput and fairness by improved channel assignment using topology control based on power control for multi-radio multi-channel wireless mesh networks”, EURASIP Journal on Wireless Communications and Networking, Vol. 2012, No. 1, pp. 1–25, 2012.
[29] Y. Huo, D. Han, X. Guo, T. Jing, D. Zhang, “Analysis of link traffic load-based channel assignment for multi-radio Mesh Network”, In the Proceeding of International Conference on Cyberspace Technology, (CCT 2013), Beijing, China, pp. 115–120, 2013.
[30] H.T. Friis, “A note on a simple transmission formula”, In the Proceeding of the I.R.E. and Waves and Electrons, Vol.34, No. 5, pp. 254–256, 1946.
[31] D. Tse, P. Viswanath, “Fundamental of wireless communication”, Cambridge University Press Publisher, New York, NY, USA, chap5, pp. 195-265, 2005.
Citation
J.S. Saini, B.S. Sohi, "Interference Aware, Topology, Power and Flow Control Channel Assignment Algorithm for Multi-Radio Multi-Channel Wireless Mesh Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.939-947, 2018.
Present Approaches for Detection of Design Pattern: A Survey
Survey Paper | Journal Paper
Vol.6 , Issue.8 , pp.948-958, Aug-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i8.948958
Abstract
Existing software’s are implemented by a third party and open source software may take a lot of time to understand, and patterns are applied without explicit class name, comments, or attached documents. If better reusability is required for an existing application where design patterns were used, then an approach that can detect the used design pattern in the existing application will be useful. Therefore, a reliable design pattern detection approach is required to promote software reusability. Design pattern detection is expected to improve the understandability and reusability of existing software. This paper represents the background work of design pattern detection. I review different approaches that have been documented so far in the literature and present the tools that have been developed. Pattern detection approaches are classified into structural analysis, behavioral analysis, and semantic analysis to mining the design pattern from the source code of different legacy application. Structural analysis approaches based on recovering the structural relationship from different artifacts available in the source code. Behavioral analysis approaches take in account the execution behavior of the program and this analysis is dynamic which execute run time behavior of the software. Semantic analysis approaches are combination of both, structure and behavioral analysis for verifying the accuracy of found result. In this paper I propose a survey of structural analysis approaches for design pattern detection.
Key-Words / Index Term
Design Pattern, UML, Ontology, Sub-Graph Isomorphism, Structural Analysis
References
[1] S. Khwaja and M. Alshayeb, “A framework for evaluating software design pattern specification languages”, In 12th International Conference on Computer and Information Science (ICIS), IEEE/ACIS, pp. 41-45, 2013.
[2] N.Nahar and K.Sakib, “ACDPR: A Recommendation System for the Creational Design Patterns Using Anti-patterns”, In 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER), IEEE, Vol. 4, pp. 4-7, 2016.
[3] A.K.Dwivedi, A.Tirkey, R. B. Ray and S.K.Rath, “Software design pattern recognition using machine learning techniques”, In Region 10 Conference (TENCON), IEEE, pp. 222-227, 2016.
[4] I.Issaoui, N.Bouassida and H. Ben-Abdallah, “A new approach for interactive design pattern recommendation”, Lecture Notes on Software Engineering, Vol.3.3, pp.173, 2015.
[5] S. Wenzel and U. Kelter, “Model-driven design pattern detection using difference calculation”, In Workshop on Pattern Detection for Reverse Engineering, 2006.
[6] G.Rasool and P.Mader, “Flexible Design Pattern Detection Based on Feature Types”, In Proceedings of the 26th IEEE/ACM International Conference on Automated Software Engineering, pp. 243-252, 2011.
[7] G.Rasool and H. akhtar, “Discovering Variants of Design Patterns”, In Journalof Basic and Applied Scientific Research, 3.1, pp. 139-147, 2013.
[8] A.Waheed, G.Rasool and S. Ubaid, “Discovery of Design Patterns Variants for Quality Software Development”, In International Conference on Intelligent Systems Engineering (ICISE), IEEE, pp. 185-191, 2016.
[9] R. Ferenc, A. Beszedes, L. Fulop, and J. Lele, “Design pattern mining enhanced by machine learning”, In Proceedings of the 21st IEEE International Conference on Software Maintenance, (ICSM`05), pp.295-304, 2005.
[10] J.Dong, Y. Sun and Y. Zhao, “Design pattern detection by template matching”, In Proceedings of the 2008 ACM Symposium on Applied Computing, pp. 765-769, 2008.
[11] Y.G. Guéhéneuc, H. Sahraoui and F. Zaidi, “Fingerprinting design patterns”, In 11th Working Conference on Reverse Engineering, Proceedings of IEEE, pp.172-181, 2004.
[12] S. Uchiyama, H. Washizaki, Y. Fukazawa, and A. Kubo. “Design pattern detection using software metrics and machine learning”, In First International Workshop on Model-Driven Software Migration (MDSM ), pp. 38 ,2011.
[13] W. Ren and W. Zaho, “An observer design-pattern detection technique”, In IEEE International Conference on Computer Science and Automation Engineering (CSAE), Vol.3, pp.544-547, 2012.
[14] D. Kirasić and D.Basch, “Ontology-based design pattern recognition”, In International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, pp. 384-39, Springer, Berlin, Heidelberg, 2008.
[15] M. Thongrak and W. Vatanawood, “Detection of design pattern in class diagram using ontology”, In International Conference on Computer Science and Engineering , IEEE, pp.97-102, 2014.
[16] F. Arcelli and L. Christina, “Enhancing software evolution through design pattern detection”, In Third International IEEE Workshop on Software Evolvability, pp. 7-14, 2007.
[17] J. Dong, S. Dushyant Lad and Z. Yajing, “DP-Miner: Design pattern discovery using matrix”, In 14th Annual IEEE International Conference and Workshops on the Engineering of Computer-Based Systems (ECBS`07), pp. 371-380, 2007.
[18] F.A. Fontana, M. Zanoni, and S. Maggioni, “Using Design Pattern Clues to Improve the Precision of Design Pattern Detection Tools”, In Journal of Object Technology, Vol.10.4, pp. 1-31, 2011.
[19] G. Antoniol, R. Fiutem and L. Cristoforetti, “Design pattern recovery in object-oriented software”, 6th International Workshop on Program Comprehension, (IWPC`98), Proceedings In IEEE, pp. 153-160,1998.
[20] Y. G. Gueheneuc, “Ptidej: Promoting patterns with patterns”, In Proceedings of the 1st ECOOP workshop on Building a System using Patterns. Springer-Verlag, July 2005.
[21] Fontana, A. Francesca and M. Zanoni, “A tool for design pattern detection and software architecture reconstruction”, In Information sciences, Vol. 181.7, pp.1306-1324, 2011.
[22] N. Shi, and R.A. Olsson, “Reverse engineering of design patterns from java source code”, In 21st IEEE/ACM International Conference on Automated Software Engineering, (ASE`06), pp. 123-134, 2006.
[23] J. M. Smith, and D. Stotts, “SPQR: Flexible automated design pattern extraction from source code”, In 18th IEEE International Conference on Automated Software Engineering, Proceedings.of IEEE, pp.215-224, 2003.
[24] R.K. Keller, R. Schauer, S. Robitaille and P. Page, “Pattern-based reverse-engineering of design components”, In Proceedings of the 21st international conference on Software engineering, ACM, pp. 226-235, 1999.
[25] A De Lucia, V. Deufemia, C. Gravino and M. Risi, “Behavioral pattern identification through visual language parsing and code instrumentation”, In 13th European Conference on Software Maintenance and Reengineering, (CSMR`09), IEEE, pp.99-108, 2009.
[26] Stefan Burger, Oliver Hummel. “Towards Automated Design Smell detection”, The Ninth International Conference on Software Engineering Advances (ICSEA2014), pp. 428, October 12 - 16, 2014
[27] P. S. Sandhu, P.P. Singh and A. K. Verma. “Evaluating quality of software systems by design patterns detection”, International Conference on Advanced Computer Theory and Engineering, (ICACTE`08) , IEEE, pp. 3-7, 2008
[28] A. Ampatzoglou, A. Kritikos, G. Kakarontzas and I. Stamelos. “An empirical investigation on the reusability of design patterns and software packages”, In Journal of Systems and Software, Vol. 84.12, pp. 2265-2283, 2011
[29] A. K. Gautam and T. Gayen, “Recovery of Design Pattern from source code”, 2010.
[30] H. Lee, H. Youn and E. Lee, “A design pattern detection technique that aids reverse engineering”, In International Journal of Security and its Applications, 2.1, pp. 1-12, 2008.
[31] D,Yu, Y. Zhang and Z. Chen, “A comprehensive approach to the recovery of design pattern instances based on sub-patterns and method signatures”, In Journal of Systems and Software, 103, pp.1-16, 2015.
[32] M. Oruc, F. Akal and H. sever, “Detecting Design Patterns in Object-Oriented Design Models by Using a Graph Mining Approach”, In 4th International Conference in Software Engineering Research and Innovation (CONISOFT), IEEE, pp.115-12, 2016.
[33] N. Tsantalis, A. Chatzigeorgiou, G. Stephanides and S. T. Halkidis, “Design pattern detection using similarity scoring”, In IEEE transactions on software engineering, Vol. 32.11, 2006.
[34] A.Pande, M. Gupta and A. K. Tripathi, “A decision tree approach for design patterns detection by subgraph isomorphism”, In International Conference on Advances in Information and Communication Technologies. Springer Berlin Heidelberg, 2010.
[35] A.Pande, M.Gupta and A.K.Tripathi, “DNIT--A new approach for design pattern detection”, In International Conference on Computer and Communication Technology (ICCCT), IEEE, pp. 545-550, 2010
[36] A. Pande, M. Gupta and A.K. Tripathi, “Design pattern mining for GIS application using graph matching techniques”, In 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), Vol. 3, pp. 477-482, 2010.
[37] M.Gupta and A. Pande, “Design Pattern Mining Using Sub-Graph Isomorphism: Relational View”, In International Journal of Software Engineering and its Application, 5.2, 2011
[38] M.Gupta, A. Pande, R.S. Rao and A.K. Tripathi, “Design pattern detection by normalized cross correlation”, In International Conference on Methods and Models in Computer Science (ICM2CS), pp. 81-84. IEEE, 2010.
[39] A.Pande, M. Gupta and A.K. Tripathi, “A new approach for detecting design patterns by graph decomposition and graph isomorphism”, In International Conference on Contemporary Computing, pp. 108-119, Springer, Berlin, Heidelberg, 2010.
[40] M.Gupta, A.Pande and A.K. Tripathi, “Design patterns detection using SOP expressions for graphs”, In ACM SIGSOFT Software Engineering Notes, 36.1, pp.1-5, 2011.
[41] M. Gupta, R. S. Rao and A. K. Tripathi, “Design pattern detection using inexact graph matching”, In International Conference on Communication and Computational Intelligence (INCOCCI), IEEE, 2010.
[42] R.S. Rao, M, Gupta, “Design Pattern Detection By Multilayer Neural Genetic Algorithm”, In International Journal of Computer Science and Network (IJCSN), Vol. 3, Issue 1, pp. 9-14, 2014.
[43] M. Gupta, R.S. Rao, A. Pande and A.K. Tripathi, “Design pattern mining using state space representation of graph matching”, In Advances in Computer Science and Information Technology, pp.318-328, 2011.
[44] M. Gupta, “Design pattern mining using greedy algorithm for multi-labelled graphs”, International Journal of Information and Communication Technology, Vol. 3.4, pp. 314-323, 2011.
[45] T. Diamantopoulos, A. Noutsos and A. Symeonidis, “DP-CORE: A Design Pattern Detection Tool for Code Reuse”.
[46] M.L.Bernardi, M. Cimitile, and G.A. Di Lucca, “A model-driven graph-matching approach for design pattern detection”, In 20th Working Conference on Reverse Engineering (WCRE), IEEE, pp.172-181, 2013.
[47] L. wen-Jin, P.Ju-long and W.Kang-Jian, “Research on detecting design pattern variants from source code based on constraints”, International Journal of Hybrid Information Technology, Vol. 8.5, pp.63-72,
[48] E. Gamma, R. Helm, R.Johnson, and J. Vlissides, Design Patterns Elements of Reusable Object-Oriented Software, Addison- Wesley, 1995.
[49] M. Smolarova and P. Navrat, “Software reuse: Principles, patterns, prospects”, In Journal of Computing and Information Technology, 5.1, pp. 33-49, 1997.
[50] K.M. Hasan and M. S. Hasan, “A Parsing Scheme for Finding the Design Pattern and Reducing the Development Cost of Reusable Object Oriented Software”, In International Journal of Computer Science and Information Technology, Vol. 2.3, June 2010.
[51] G. Rasool and D. Streitfdert, “A survey on design pattern recovery techniques”, In International Journal of Computer Science Issues (IJCSI), Vol. 8.2, pp.251-260, 2011.
[52] R.K. Priya, “A survey: Design pattern detection approaches with metrics”, In IEEE National Conference on Emerging Trends In New & Renewable Energy Sources And Energy Management (NCET NRES EM), pp.22-26, December 2014.
[53] H. Alshira and H. Mohammad, “Integrating user knowledge into design pattern detection”, (Doctoral dissertation, Department of Computer Science), 2015.
[54] U. Tekin, U. Erdemir and F. Buzluca, “Mining object-oriented design models for detecting identical design structures”, In Proceedings of the 6th International Workshop on Software Clones, IEEE Press, pp. 43-49, 2012.
[55] A. Nagy and B. Kovari, “Programming language neutral design pattern detection”, In 16th IEEE International Symposium on Computational Intelligence and Informatics (CINTI), pp.215-219, 2015.
[56] D. Beyer and C. Lewerentz, “CrocoPat: Efficient pattern analysis in object-oriented programs”, In 11th IEEE International Workshop on Program Comprehension, pp. 294-295, 2003.
[57] M. Vokac, “An efficient tool for recovering Design Patterns from C++ Code”, In Journal of Object Technology, Vol.5.1, pp. 139-157, 2006.
[58] A.K. Gautam and S.Diwakar, “Automatic Detection of Software Design Patterns from Reverse Engineering”, In Issues and Challenges in Networking, Intelligence and Computing Technologies-ICNICT 2012, Special Issue of International Journal of Computer Application, November 2012.
[59] G. Costagliola, A. De Lucia, V. Deufemia, C. Gravino and M. Risi, “Design pattern recovery by visual language parsing”, In Ninth European Conference on Software Maintenance and Reengineering, CSMR, IEEE, pp.102-111, 2005.
[60] A. Blewitt, A. Bundy and I. Stark, “Automatic verification of design patterns in Java”, In Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering, ACM, pp. 224-232, 2005.
Citation
A. Chaturvedi, "Present Approaches for Detection of Design Pattern: A Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.948-958, 2018.
A Comparative And Statistical Approach To Leverage Cloud Computing And Big Data Analytics In E-Governance
Research Paper | Journal Paper
Vol.6 , Issue.8 , pp.959-967, Aug-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i8.959967
Abstract
Nowadays, the governments across the world uses internet for providing their maximum number of services to the different stakeholders with the help of ICT. But due to the legacy computing architecture and data processing technologies, it become difficult for the governments to fulfill the current requirement of E-governance which makes impact on its usage. Therefore the emerging technologies like cloud computing and big data analytics can be used together in E-governance to overcome the existing challenges related to computing and data processing. The Cloud computing and big data analytics can be used together to introduce the many features which are not there in existing system. But before opting them their relevance needs to be studied. Therefore the main objective of this paper is to perform the in-depth study of Cloud and Bigdata enabled E-governance along with the statistical and comparative analysis. This research paper also proposes a model for integrating cloud computing and big data analytics in to E-governance along with advantages and disadvantages.
Key-Words / Index Term
E-governance, Cloud Computing, Big data analytics
References
[1] Satyabrata Dash and Subhendu Kumar Pani, “E-Governance Paradigm Using Cloud Infrastructure: Benefits and Challenges”, International Conference on Computational Modeling and Security, Elsevier, pp:843-855,2016.
[2] Smitha K. K., Dr. Tony Thomas, Chitaranjan K,”Cloud based E-Governance System: A Survey”, International Conference on Modeling, Optimization and Computing”, Elsevier, pp: 3816-3823, 2012.
[3] Rajkumar Sharma and Priyesh Kanungo,”An Intelligent Cloud Computing Architecture Supporting e-Governance”, International Conference on Automation & Computing,IEEE, 2011,pp:1-5,2015.
[4] Yannis Charalabidis, Sotirios Koussouris, Antonis Ramfos,” A Cloud Infrastructure for Collaborative Digital Public Services”, International Conference on Cloud Computing Technology and Science, IEEE, pp:342-347, 2011.
[5] Rajiv Ranjan, Saurabh Garg, Ali Reza Khoskbar, et al.: “Orchestrating Big Data Analysis Workflows”.IEEE transaction on Cloud Computing.IEEE Computer Society, pp:20-28,2017
[6] Deka Ganesh Chandra and Robin Singh Bhadoria, “Cloud Computing Model for National e-Governance Plan(NeGP)”, International Conference on Computational Intelligence and Communication Networks,IEEE, pp:520-524,2012.
[7] Rajagopalan M.R and Solaimurugan vellaipandiyan,”Big Data Framework for National e-Governance Plan”, International Conference on ICT and Knowledge Engineering,IEEE,2013.
[8] Saeid Abolfazli, Zohreh Sanaei et al.,”Cloud Adoption in Malaysia: Trends, Opportunities, and Challenges”, IEEE CLOUD COMPUTING SOCIETY,2015.
[9] Prajakta N. Warale, Hemalatha Diwakar,” A Study of Citizen Satisfaction for e-Governance Initiative SETU in Maharashtra (INDIA)”, International Journal of Computer Applications (0975 – 8887),Volume 124 – No.17, August 2015.
[10] Bhushan Jadhav, Archana Patankar, ”Opportunities and Challenges in integrating Cloud computing and Big data analytics to E-governance”, International Journal of Computer Applications, (0975 – 8887), Volume No.182, January 2018.
[11] Bhushan Jadhav, Archana Patankar, ”A Novel Solution for Cloud enabled E-governance using Openstack: Opportunities and Challenges”, International conference on computers network and communications, proceedings in Communications in computer and information science, Springer CCIS, 2018.
[12] Ibrahim Abaker, Targio Hashem, Ibrar Yaqoob et al.:” The rise of “big data” on cloud computing : Review and open research issues”,International Journal of Information Sciences, Elsevier,(2014)
[13] Ethiranjan D.,S. Purushothaman et al.,”Adoption of E-governance applications towards Big data approach”, International Journal of Applied Engineering Research, ISSN 0973-4562,pp: 11336-11340, Volume 12, (2017)
[14] https://blog.microfocus.com/how-much-data-is-created-on-the-internet-each-day/
[15] Fang Liu, Jin Tong, Jian Mao, Robert Bohn, John Messina, Lee Badger and Dawn Leaf,”NIST Cloud Computing Reference Architecture”, Recommendations of the National Institute of Standards and Technology, Special Publication 500-292, 2011.
[16] https://www.gartner.com/it-glossary/big-data
Citation
Bhushan Jadhav, Archana B. Patankar, "A Comparative And Statistical Approach To Leverage Cloud Computing And Big Data Analytics In E-Governance," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.959-967, 2018.
Modeling Role of ICT in Business Startups and Incubation
Research Paper | Journal Paper
Vol.6 , Issue.8 , pp.968-973, Aug-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i8.968973
Abstract
Ubiquitous Information Communication Technologies are contributing to further the cause of the startups and incubation process in numerous ways. All starts ups are expected to leverage ICT for understanding the ‘market and cultures’ of various geographies. ICT facilitates the production, transmission, and processing of information. Initial investigation shows that even to work the system (all kind of licenses and permissions from various statutory bodies), ICT driven practices are going to be effective. The paper investigate the areas in which ICT practices can be employed for building efficiencies in the processes of concept to servicing the market. This paper seeks to contribute to the ongoing research in the field of Information & Communication Technology, its impact on the Economic growth & how ICT tools can be used to foster the fast growth of entrepreneurship. Detailed analysis of ICT tools to evaluate the risk capital involved and mitigation of risk in project implementation is done. How modeling and simulation tools be used for projecting the future of a business process is also analyzed. The impact of business analytics/business intelligence techniques for forecasting market behavior is also done thoroughly. Evidence has been collected through a literature search, questionnaires, entrepreneur’s reports and interviews. The detailed study of the challenges faced by startups and the best solutions for them is also done. This paper also analyzes how ICT driven practices & procedures act as a ready reference for Entrepreneurs. This paper also focuses on how ICT plays an important role in fostering innovation leading to enhance firm productivity and economic growth.
Key-Words / Index Term
Information Communication Technology (ICT), Enterprise Resource Planning (ERP), Big data analytics (BDA)
References
[1]. Richman, Tom, “The Evolution of Professional Entrepreneur”, the State of Small Business, 1997, pp 50-53.
[2]. Tang, Linghui; Koveos, Petre E, “Venture Entrepreneurship, Innovation Entrepreneurship and Economic Growth”, Journal of Development Entrepreneurship, August, 2004.
[3]. Jeremy Grace, Charles Kenny and Christine Zhen-Wei Qiang, “Information Communication Technologies and Broad-Based Development”, World Bank working Paper no.12, 2003.
[4]. Venkataramana Gajjala, “The role of ICT in enhancing process of Entrepreneurship and Globalization in Indian Software Companies”, EJISDC, 2006.
[5]. Mohsel A. Khalil and Ellen Olafsen, “Enabling Innovative Entrepreneurship through Business Incubation”, the innovation for Development report 2009-2010 pp 69-84.
[6]. Elias G. Carayannis, Denisa Popescu, Caroline Sipp, McDonald Stewart, “Technological learning for Entrepreneurial development (TL4ED) in the Knowledge economy (KE):Case studies and lessons learned ”, Technovation Vol. 26, Issue 4, pp. 419-443, April 2006.
[7]. William J. Kramer, Beth Jenkins and Robert S. Katz, “The role of ICT sector in expanding economic opportunity”.
[8]. Rathore B.S and Saini J.S, “A Handbook of Entrepreneurship”, Apga Publications , India.
[9]. Saini J.S, Gurjar, B.R, and Rathore, B.S, “Enterprise Support System in India”, Wheeler Publisher, India, 2001.
[10]. Nathalie Hyde-Clarke, “ The impact of Mobile Technology on Economic Growth amongst ‘Survivalists’ in the Informal Sector in the Johannesburg CBD, South Africa, International Journal of Business and Social Science, Vol. 4, Issue 16, 2013.
[11]. Esselaar S., Stork C.,Ndiwalana A. & Deen-Swarray M. “ICT Usage and Its Impact on Profitability of SMEs in 13 African Countries”, Information Technologies and International Development, The MIT Press, Vol. 4, 2008.
[12]. Eric Ries, “The lean startup: How today’s entrepreneur use continuous innovations to create radically successful business”, 2011.
[13]. Prahalad, C. K, “The fortune at the bottom of the pyramid”, Wharton School Publishing: New Jersey.
[14]. Amue, Gonewa John, Sunny R, Horsfall, “ICT Entrepreneurship and small business innovation: A mechanism for sustainability”, European journal of business and social sciences, Vol. 3, Issue 6, pp. 103-112, 2014.
[15]. Geoff D. Skinner, “A study of Fostering Entrepreneurship in Information Communication Technology (ICT)”, International Journal of Computers and Communications, Vol. 2, Issue 4, 2008.
[16]. T.SWETHA and DR.K.VENUGOPAL RAO, “Entrepreneurship in India” International Journal of Social Science & Interdisciplinary Research IJSSIR, Vol. 2, Issue 7, 2013.
[17]. MEENU GOYAL and JAI PARKASH, “WOMEN ENTREPRENEUR-SHIP IN INDIA-PROBLEMS AND PROSPECTS”, International Journal of Multidisciplinary Research Vol.1, Issue 5, 2011.
[18]. Danso Ansong, Ed, Affum Emmanuel A.K and Hayfron-Acquah J. B, “The Challenges of Young I.C.T Entrepreneur in Developing Countries: Case Study – Ghana”, International Journal of Computer Applications, Vol. 45, Issue 21, 2012.
Citation
Gopal Pardesi, "Modeling Role of ICT in Business Startups and Incubation," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.968-973, 2018.
Wi-Li-Fi an Innovative Wireless Network Architecture for Future IOT Applications
Research Paper | Journal Paper
Vol.6 , Issue.8 , pp.974-977, Aug-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i8.974977
Abstract
Wi-Fi plays an important role in the current scenario. The whole world runs at the back of Wi-Fi technology for their seamless data connectivity. As the usage increases more and more the technology steps back in his performance slightly. The performance is based on the uninterrupted connectivity, the speed of data transfer and the network traffic. Researchers say Light Fidelity (Li-Fi) uses light emitting diodes (LEDs) for high-speed wireless communications. VLC (visual light Communication) is the channel for Li-Fi to transmit data nearly 200 Gigabits/sec. Since employing a different range of the electromagnetic spectrum from radio frequency (RF) communications, Li-Fi (Light Fidelity) enters in this phase of Wi-Fi to take part in the performance based upon the speed of data transfer. Hence, a combination of Li-Fi and RF networks becomes a challenging work in future indoor wireless communications. Thus the backdrops of both Li-Fi and Wi-Fi are complementing each other by their performance. Wi-Fi is used for uplink and Li-Fi can be used for downlink. With this strategy, all researchers have focused in this area by integrating the Wi-Fi and Li-Fi for seamless data transmission especially in the indoor environment. This hybrid can be called easily abbreviated as Wi-Li-Fi. There are many challenges combining the both different types of networks. A newly Hybrid Wi-Li-Fi network is proposed and its performances are compared with standalone Li-Fi and Wi-Fi networks, metrics such as Throughput, Energy consumption and data loss, when the end users are at different distance from the Access point.
Key-Words / Index Term
Li-Fi, Wi-Fi, VLC, RF, IOT
References
[1] Xu Bao, Guanding Yu, Jisheng Dai, Xiaorong Zhu “Li-Fi: Light fidelity-a survey” Published online: 18 January 2015 Springer Science+Business Media New York 2015
[2] Kumar, A., Mihovska, A., Kyriazakos, S. et al. “Visible Light Communications (VLC) for Ambient Assisted Living” Wireless Pers Commun (2014) 78: 1699. doi:10.1007/s11277-014-1901-1
[3] M. Ayyash et al., "Coexistence of WiFi and LiFi toward 5G: concepts, opportunities, and challenges" in IEEE Communications Magazine, vol. 54, no. 2, pp. 64-71, February 2016. doi: 10.1109/MCOM.2016.7402263
[4] S. Shao et al., "An Indoor Hybrid WiFi-VLC Internet Access System" 2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems, Philadelphia, PA, 2014, pp. 569-574. doi: 10.1109/MASS.2014.76
[5] T. Komine and M. Nakagawa, “Fundamental analysis for visible-lightcommunication system using led lights,” Consumer Electronics, IEEE Transactions on, vol. 50, no. 1, pp. 100–107, 2004.
[6] S. Rajagopal, R. D. Roberts, and S.-K. Lim, “Ieee 802.15. 7 visible light communication: modulation schemes and dimming support,” Communications,Magazine, IEEE, vol. 50, no. 3, pp. 72–82, 2012.
[7] Li-Fi Consortium. http://www.lificonsortium.org/
[8] Stefan, I., Burchardt, H., & Haas, H. (2013). “Area spectral efficiency performance comparison between VLC and RF femtocell networks.” In 2013 IEEE international conference on communications (ICC), pp. 3825–3829.
[9] Cisco Visual Networking Index. (Feb. 2013). Global mobile data traffic forecast update, 2012–2017. CISCO: White paper..
[10] National Telecommunications and Information Admission(NTIA).
(2003). FCC frequency allocation chart Available http://www.Ntia. doc.gov/osmhome/allochrt
[11] M. Kavehrad, "Sustainable energy-efficient wireless applications using light," in IEEE Communications Magazine, vol. 48, no. 12, pp. 66-73, December 2010. doi: 10.1109/MCOM.2010.5673074
Citation
S. Arunmozhi Selvi, P. Sivananaintha Perumal, R.S. Rajesh, "Wi-Li-Fi an Innovative Wireless Network Architecture for Future IOT Applications," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.974-977, 2018.
Multihoming in Multihomed AD HOC Networks with SCTP
Research Paper | Journal Paper
Vol.6 , Issue.8 , pp.978-982, Aug-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i8.978982
Abstract
SCTP multi-homing is the independence to choose its transmission route from the given alternative routes by a node, ad-hoc networks are independent of the infrastructure and are created on demand driven processes giving multihoming functionalities to the system, Controlled at the network layer of the system. IETF rules at transport layer have mentioned all the multihoming characteristics of the system. This research prioritize multihoming feature at both network and transport level and aims to prove the SCTP functionalities over DSR ad-hoc system. Afterwards the dysfunction of the multi- homing feature, introduces CLI(cross layer interface) for added multihoming functionality. The simulation in NS2 is utilized for design and simulates the study. In case of network failure, the multihoming has enhanced the good put rates. Multihoming is better for simultaneous fast retransmission policies, the results provided gives an idea of multihoming advantages.
Key-Words / Index Term
Multi-homing, SCTP, DSR, IETF, NS2, ad-hoc, CLI
References
[1] Marc Greis, 1995. NS2 network simulation,
[Online] Available at: http://www.isi.edu/nsnam/ns[accessed 20 March 2011].
[2] R. Stewart, 2000. SCTP, IETF RFC 2960.
[3] Iyengar, J R, et. al., “Concurrent multipath transfer using SCTP Multihoming”, IEEE , IJCN,2006, Issue 4, Vol. 14, pages 951–964.
[4] Jon Postel, 1981, Information Sciences Institute, University of Southern California,
[5] Changqing, G, et. al., “Improvement of TCP Congestion Control Algorithm”, International Symposium on “Technologies for Wireless Communications”, IEEE,2007,pages 197-199.
[6] J. Iyengar. “Performance Implications of a Bounded Receive
Buffer In Concurrent Multipath Transfer”. IJCC, Randall Stewart
Computer Communications, 2007, Vol. 30, Issue 4,pages 818–829.
[7] Perotto, F , et. al., “SCTP-based Transport Protocols for CMT”, WCNC IEEE, 2007, pages 2969-2974.
[8] Conti, M , et. al., “Cross - layering in Mobile Ad hoc”, IJCN,2004, IEEE, Vol. 37, Issue 2, pages 48-51.
[9] Srivastava, V , et. al., “Cross-layer design”, IJCN,2005,IEEE, Vol. 43, Issue 12,pages 1112–1119.
[10] Borgia, E , “Mobile MAN: Experimentation of Cross-Layer Mobile Multi-hop Ad Hoc Networks”, IJCN, IEEE,2006, Vol. 44, Issue 7, pages 80-85.
[11] Wallace, T D, et. al., “An Analytic Model for the Stream Control Transmission Protocol”, Conference (GLOBECOM 2010), IEEE on Communication, Networking & Broadcasting,2010, pages 1-5.
[12] Taehun Kim, et. al., “ Concurrent Multipath Transfer using SCTP multihoming”, International Conference on communication & Networking, IEEE,2010, pages 1598-1602.
[13] Wang, B , et. al., “ Concurrent multipath transfer protocol used in ad hoc networks”. IJCN, IET Communications, Vol. 4, Issue7, pages 884 – 893.
[14] Natarajan, P , et. al., “Concurrent Multipath Transfer Using SCTP Multi-homing”, Proceedings of the 7th international IFIP-TC6 networking conference on Ad Hoc and sensor Networks, wireless networks, next generation internet,2008, pages 727-734.
[15] Sakuna Charoenpanyasak, 2009. “Real Multi-route System (RMS) for Mobile Ad hoc Networks”, ACM 7th International Conference on Advances in Mobile Computing and Multimedia, pages 431-435.
[16] Sakuna Charoenpanyasak, et. al., “ Improving end-to-end good put of ad hoc networks with SCTP multi-homing”, The 9-th ACM/IEEE International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWIM 06), 2006,pages 67-72.
[17] Andrews, J , “Rethinking Information Theory for Mobile Ad Hoc Networks”. IJCN, IEEE,2008, Vol. 46, Issue 12, pages 94-101.
[18] Gu, X , et. al., “To layer or Not To layer: ArchitecturalConsiderations on Autonomic Communications”, IJIPT, 2007, Vol. 2, Issue 1, pages.67-76.
[19] K. Chen,Y. Xue, et. al., “Understanding Bandwidth- Delay Product in Mobile Ad Hoc Networks”, IJCC, Elsevier Computer Communications Journal,2004, Vol. 27, Issue 10,pages 923–934.
[20] Salmi, S , et. al., “Ad Hoc MANET mobile networks and the integration of the Multihoming concept”, SIECPC, 2011 Saudi International, IEEE, pages 1-6.
[21] Nesargi, S , et. al., “MANET conf : Configuration of Hosts in a Mobile Ad Hoc Networks”,Proceedings IEEE INFOCOM 2002, The 21st Annual Joint Conference of the IEEE Computer and Communications Societies, New York, USA, pages 1059 – 1068.
[22] Yuan-Ying Hsu, et. al., “Prime DHCP: A Prime Numbering Address Allocation Mechanism” , IJCN, IEEE,2005, Vol. 9, Issue 8, pages 712 – 714.
[23] Insu Jeong, et. al., “Study on Address Allocation in Ad Hoc Networks”, Proceedings of the Fourth Annual ACIS International Conference on Computer and Information Science, IEEE Computer Society, Washington, DC, USA,2005, pages 604-609.
[24] Stephen Mueller, et. al., “Multipath Routing in Mobile Ad Hoc Networks: Issues”, International Journal of Performance Tools and Applications to Networked Systems, Springer,2004, Volume 2965, pages 209-234.
Citation
Sarthika Dutt, Astha Sharma, Anuj Saxena, "Multihoming in Multihomed AD HOC Networks with SCTP," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.978-982, 2018.
A Survey of Genome Compression Methodology
Survey Paper | Journal Paper
Vol.6 , Issue.8 , pp.983-991, Aug-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i8.983991
Abstract
Storing the information about human nucleotide is become essential now a day for various medical research purposes. A human genome consists of almost 3.2 billion nucleotides. It is unmanageable to store, access and retrieve the desired information from the massive bulk of unprocessed data. So the possible solution is genome compression. By compression we mean that we are restricting on the data storage. Existing data compression methodology is not suitable to deal with this massive data. In this paper we provide a survey analysis on various types of genome compression and read compression algorithm which are specially designed to handle this voluminous raw DNA information. To extract the unique non repeated information from the whole sequence is actually a tough challenge .These compression algorithms not only save time but also provide high compression rate. We have discussed all the types of compression algorithm with their distinctive approach. Each of them having some benefit over other. We also briefly discuss on various file formats used while compression.
Key-Words / Index Term
Genome compression, Read compression, Data formats
References
[1] P.Raja Rajeswari (1) Allam Apparo (2), V.K. Kumar, Genbit Compress Tool(GBC): A Java-Based Tool to Compress DNA Sequences and Compute Compression Ratio(bits/base) of Genomes , Acharya Nagarjuna University, India, (2) Jawaharlal Nehru Technological University, India and (3) S.V.H. College Of Engineering, India7 Jun 2010
[2] Jacob Ziv and Abraham Lempel. A universal algorithm for sequential data compression. IEEE Transactions on Information Theory, 23(3):337-343, 1977
[3] Ateet Mehta and Bankim Patel. Dna compression using hash based data structure. International Journal of Information Technology & Knowledge Management, 3:383-386, 2010.
[4] Piyuan Lin, Shaopeng Liu, Lixia Zhang, et al. Compressed pattern matching in dna sequences using multithreaded technology. In 3rd International Conference on Bioinformatics and Biomedical Engineering, ICBBE`09, 2009.
[5] Pothuraju Rajeswari and Allam Apparao. Dnabit compress -genome compression algorithm. Bioinformation, 5(8):350-60, 2011.
[6] Grumbach, S. and Tahi, F. (1993). Compression of DNA sequences. In DCC`93:Proceedings of the Conference on Data Compression, pages 340-350.
[7] Grumbach, S. and Tahi, F. (1994). A new challenge for compression algorithms:Genetic sequences. Information Processing and Management, 30(6), 875-886
[8] Rivals, E., Delahaye, J., Dauchet, M., and Delgrange, O. (1996). A guaranteed compression scheme for repetitive DNA sequences. In DCC `96: Proceedings of the Conference on Data Compression, page 453.
[9] Chen, X., Kwong, S., and Li, M. (2000). A compression algorithm for DNA sequences and its applications in genome comparison. In RECOMB`00: Proceedings of the 4th Annual International Conference on Computational Molecular Biology, pages 107-117.
[10] Chen, X., Li, M., Ma, B., and Tromp, J. (2002). DNACompress: fast and effective DNA sequence compression. Bioinformatics, 18(12), 1696-1698.
[11] T. Matsumoto, K. Sadakane, and H. Imai. Biological sequence compression algorithms. Genome l.Informatics, 11:43–52, 2000.
[12] Kuruppu S, Beresford-Smith B, Conway T, et al. Iterative dictionary construction for compression of large DNA datasets. IEEE-ACM Trans Computat Biol Bioinformatics 2012;9:137-49
[13] Manzini, G., and Rastero, M., 2004, A Simple and Fast DNA Compressor, Software: Practice and Experience, 34(14), 1397–1411.
[14] A. J. T. Lee, C. Chang and C. Chen, "DNAC: An Efficient Compression Algorithm for DNA Sequences," National Taiwan University, Taipei, Taiwan 10617, R.O.C., 2004.
[15] Dimitris Antoniou, Evangelos Theodoridis, and Athanasios Tsakalidis. Compressing biological sequences using selfadjusting data structures. In Information Technology and Applications in Biomedicine, 2010
[16] Kalyan Kumar Kaipa, Ajit S Bopardikar, Srikantha Abhilash, et al. Algorithm for dnasequence compression based on prediction of mismatch bases and repeat location. In Bioinformatics and Biomedicine Workshops, BIBMW, 2010.
[17] Behzadi, B. and Le Fessant, F. (2005). DNA compression challenge revisited: A dynamic programming approach. In CPM`05: Proceedings of the 16th Annual Symposium on Combinatorial Pattern Matching, volume 3537 of LNCS, pages 85-96.
[18] Matsumoto, T., Sadakane, K., and Imai, H. (2000). Biological sequence compression algorithms. Genome Informatics, 11, 43-52.
[19] I. Tabus, G. Korodi, and J. Rissanen, "DNA sequence compression using the normalized maximum likelihood model for discrete regression," in Proc. of the Data Compression Conf. (DCC2003), 2003, 253–262.
[20] Korodi, G. and Tabus, I. (2005). An effcient normalized maximum likelihood algorithm for DNA sequence compression. ACM Transactions on Information Systems, 23(1), 3-34.
[21] Mishra, K. N., Aaggarwal, A., Abdelhadi, E., et al., 2010, An Efficient Horizontal and Vertical Method for Online DNA Sequence Compression, International Journal of Computer Applications, 3(1), 39-46.
[22] David A Huffman. A method for the construction of minimum-redundancy codes. Proceedings of the Institute of Radio Engineers, 40(9):1098-1101, 1952.
[23] D. Loewenstern, and P. N. Yianilos, "Significantly lower entropy estimates for natural DNA sequences," in Proc. of the Data Compression Conf., (DCC `97), 1997, 151–160.
[24] Allison, L., Edgoose, T., and Dix, T. I., 1998, Compression of strings with approximate repeats, Proc. ISMB, 8–16.
[25] M. D. Cao, T. I. Dix, L. Allison, et al., "A Simple Statistical Algorithm for Biological Sequence Compression," in Proc. of the Data Compression Conf., (DCC `07), 2007, 43–52.
[26] Diogo Pratas and Armando J. Pinho. Compressing the human genome using exclusively markov models. In Miguel P. Rocha, Juan M. Corchado Rodrguez, Florentino Fdez-Riverola, and Alfonso Valencia, editors, PACBB, volume 93 of Advances in Intelligent and Soft Computing, pages 213-220. Springer, 2011.
[27] K. R. Venugopal, K. G. Srinivasa, and Lalit Patnaik. Probabilistic Approach for DNA Compression, chapter 14, pages 279-289. Springer, 2009.
[28] I. Tabus and G. Korodi. Genome compression using normalized maximum likelihood models for constrained markov sources. In Information Theory Workshop, 2008.
[29] Kalyan Kumar Kaipa, Kyusang Lee, Taejin Ahn, et al. System for random access dna sequence compression. In International Conference on Bioinformatics and Biomedicine Workshops, 2010.
[30] Marty C. Brandon, Douglas C. Wallace, and Pierre Baldi. Data structures and compression algorithms for genomic sequence data. Bioinformatics, 25(14):1731-1738, 2009.
[31] Golomb S. Run-length encodings. IEEETrans InformTheory 1965;12:399–401.
[32] Elias P. Universal codeword sets and representations of the integers. IEEETrans InformTheory 1975;21:194–203.
[33] Huffman DA. A method for the construction of minimum redundancy codes. Proc IRE 1952;40:1098–101.
[34] Scott Christley, Yiming Lu, Chen Li, et al. Human genomes as email attachments. Bioinfor-matics, 25(2):274-275, 2009.
[35] Congmao Wang and Dabing Zhang. A novel compression tool for efficient storage of genome resequencing data. Nucleic Acids Research, 39(7):e45, 2011.
[36] Shanika Kuruppu, Simon J. Puglisi, and Justin Zobel. Relative lempel-ziv compression of genomes for large-scale storage and retrieval. In Proceedings of the 17th International Conference on String Processing and Information Retrieval, SPIRE`10, pages 201-206, 2010.
[37] Shanika Kuruppu, Simon Puglisi, and Justin Zobel. Optimized relative lempel-ziv compression of genomes. In Australasian Computer Science Conference, 2011.
[38] Szymon Grabowski and Sebastian Deorowicz. Engineering relative compression of genomes. CoRR, abs/1103.2351, 2011.
[39] Armando J. Pinho, Diogo Pratas, and Sara P. Garcia. Green: a tool for efficient compression of genome resequencing data. Nucleic Acids Research, 2011.
[40] Sebastian Kreft and Gonzalo Navarro. Lz77-like compression with fast random access. In Proceedings of the 2010 Conference on Data Compression, DCC`10, pages 239-248, 2010.
[41] Heba Afify, Muhammad Islam, and Manal Abdel Wahed. Dna lossless differential compression algorithm based on similarity of genomic sequence database. CoRR, abs/1109.0094, 2011.
[42] Heba Afify, Muhammad Islam, and Manal Abdel Wahed. Genomic sequences differential compressionmodel. International Journal of Computer Science and Information Technology,3:145-154, 2011.
[43] Deorowicz S, Grabowski S. Robust relative compression of genomes with random access. Bioinformatics 2011;27:2979–86.
[44] Mohammed MH, Dutta A, Bose T, et al. DELIMINATE-afast and efficient method for loss-less compression of genomic sequences. Bioinformatics 2012;28:2527–9.
[45] Pinho AJ, Ferreira PJSG, Neves AJR, et al. On the representability of complete genomes by multiple competing finite-context (Markov) models. PLoS One 2011;6:e21588.
[46] Hunt JJ, Vo K-P, Tichy WF. Delta algorithms: an empirical analysis. ACMTrans Software EngMethodol (TOSEM) 1998;7:192–214.
[47] Pinho AJ, Ferreira PJSG, Neves AJR, et al. On the representability of complete genomes by multiple competing finite-context (Markov) models. PLoS One 2011;6:e21588.
[48] Oscar Herrera and Angel Kuri-Morales. Lossless compression of biological sequences with evolutionary metadictionaries. In Workshop on Machine Learning and Data Mining, 2009.
[49] Giulia Menconi, Vieri Benci, and Marcello Buiatti. Data compression and genomes: a two dimensional life domain map. Journal of Theoretical Biology, 253(2):281-288, 2008.
[50] Zexuan Zhu, Jiarui Zhou, Zhen Ji, et al. Dna sequence compression using adaptive particle swarm optimization-based memetic algorithm. IEEE Transactions on Evolutionary Computation, 15(5):643-658, 2011.
[51] Vishal Bhola, Ajit Bopardikar, Rangavittal Narayanan, et al. No-reference compression of genomic data stored in fastq format. In Proceedings of the 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM`11, pages 147-150, 2011.
[52] Raymond Wan, Vo N. Anh, and Kiyoshi Asai. Transformations for the compression of fastq quality scores of next generation sequencing data. Bioinformatics, 2011
[53] Waibhav Tembe, James Lowey, and Edward Suh. G-sqz: compact encoding of genomic sequence and quality data. Bioinformatics, 26(17):2192-2194, 2010.
[54] Sebastian Deorowicz and Szymon Grabowski. Compression of dna sequence reads in fastq format. Bioinformatics, 27(6):860-862, 2011.
[55] Wei-Hsin Chen, Yu-Wen Lu, Feipei Lai, et al. Integrating human genome database into electronic health record with sequence alignment and compression mechanism. Journal of Medical Systems, 36(3):2587-2597, 2011.
[56] Kenny Daily, Paul Rigor, Scott Christley, et al. Data structures and compression algorithms for high-throughput sequencing technologies. BMC Bioinformatics, 11(1):514+, 2010.
[57] Christos Kozanitis, Chris Saunders, Semyon Kruglyak, et al. Compressing genomic sequence fragments using slimgene. In Proceedings of the 14th Annual International Conference on Research in Computational Molecular Biology, RECOMB`10, pages 310-324, 2010.
[58] Fritz MH-Y, Leinonen R, Cochrane G, et al. Efficient storage of high throughput DNA sequencing data using reference based compression. GenomeRes 2011; 21:734–40.
[59] Jones DC, Ruzzo WL, Peng X, et al. Compression of nextgeneration sequencing reads aided by highly efficient denovo assembly. Nucleic Acids Res 2012;40:e171.
[60] Popitsch N, von Haeseler A. NGC: lossless and lossy compression of aligned high-throughput sequencing data. Nucleic Acids Res 2013;41:e27.
[61] Bonfield JK, Mahoney MV. Compression of FASTQ and SAM format sequencing data. PLoS One 2013;8:e59190.
[62] Markus H. Fritz, Rasko Leinonen, Guy Cochrane, et al. Effcient storage of high throughput dna sequencing data using reference-based compression. Genome Research, 21(5):734-740,2011.
[63] Yanovsky V. ReCoil - an algorithm for compression of extremely large datasets of DNA data. Algorithms Mol Biol 2011;6:23.
[64] Cox AJ, Bauer MJ, Jakobi T, et al. Large-scale compression of genomic sequence databases with the Burrows-Wheeler transform. Bioinformatics 2012;28:1415–9.
[65] Hach F, Numanagic I, Alkan C, et al. SCALCE: boosting sequence compression algorithms using locally consistent encoding. Bioinformatics 2012;28:3051–7.
[66] Heba Afify, Muhammad Islam and Manal Abdel Wahed. DNA LOSSLESS DIFFERENTIAL COMPRESSION ALGORITHM BASED ON SIMILARITY OF GENOMIC SEQUENCE DATABASE. International Journal of Computer Science & Information Technology (IJCSIT) Vol 3, No 4, August 2011 .
[67] Bacem Saada, Member, IAENG, Jing Zhang. DNA Sequences Compression Techniques Based on Modified DNABIT Algorithm. Proceedings of the World Congress on Engineering 2016 Vol I WCE 2016, June 29 - July 1, 2016, London, U.K.
[68] Rajesh Mukherjee , Subhrajyoti Mandal , Bijoy Mandal. Reverse Sequencing based Genome Sequence using Lossless Compression Algorithm. International Research Journal of Engineering and Technology (IRJET) Volume: 03 Issue: 05 ,May-2016 .
[69] Rexline S J, Trujilla Lobo F. DNA Compression Algorithm Using Pattern Hunter. International Journal on Computer Science and Engineering (IJCSE).
[70] Peter J. A. Cock,Christopher J. Fields, Naohisa Goto, Michael L. Heuer and Peter M. Rice. The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Plant Pathology, SCRI, Invergowrie, Dundee DD2 5DA, UK.
Citation
Rituparna Mitra, Subhankar Roy, "A Survey of Genome Compression Methodology," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.983-991, 2018.
Host Based Intrusion Detection Using Data Mining Methodologies
Research Paper | Journal Paper
Vol.6 , Issue.8 , pp.992-998, Aug-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i8.992998
Abstract
In today’s computing world there is an inconceivable growth in the usage of computers over different networks and domains, which in turn increases the security threats in terms of intrusions. An intrusion can be either internal or external and the conventional methods used in the detection of intrusion are failed to meet the necessities of preventing and detecting threats or intrusions. In this paper, Data Mining methodologies are combined to handle some of the problems like data Preparation, pre-processing of the data, data classification and Intrusion Detection. The definitive role of IDS is to recognize threats or attacks in contrast to computing schemes. The intrusion detection system is one of the vital networks shielding device or software for safeguarding computing schemes and it is capable to discover and to examine network traffic data packets. This research paper is developed situated on advanced snort rules have been developed. The main goal of this research paper is to detect fraudulent network traffic.
Key-Words / Index Term
Intrusion Detection System, Intrusion Prevention System, Snort
References
[1]. Anderson, James P., "Computer Security Threat Monitoring and Surveillance," Washing, PA, James P. Anderson Co., 1980.
[2]. Bellovin, S.M. “Network Firewalls”, IEEE Communications Magazine, Vol. 32, pp. 50- 57, 1994.
[3]. Mohammadreza Ektefa, Sara Memar, Fatimah Sidi, Lilly Suriani Affendey. Intrusion detection using data mining techniques. In: International conference on information retrieval and knowledge management; 2010. p. 200–204.
[4]. Ching-Hao, Hahn-Ming L, Devi P, Tsuhan C, Si-Yu H. Semi-supervised co-training and active learning based approach for multiview intrusion detection. In: ACM symposium on applied computing, no. 9; 2009. p. 2042– 7.
[5]. Denning, D.E. “An Intrusion-Detection Model”, in IEEE Transactions on Software Engineering, Vol.13, No. 2, pp. 222-232, 1987.
[6]. Sethuramalingam S. Hybrid feature selection for network intrusion. Int. J Computer Science Eng 2011; 3(5):1773–9.
[7]. Mchugh, J. “Intrusion and Intrusion Detection”, International Journal of Information Security, Vol. 1, No. 1, pp. 14- 35, 2001.
[8]. Prof. Ujwala Ravale, Prof. Nilesh Marathe, Prof. Puja Padiya, Feature Selection Based Hybrid Anomaly Intrusion Detection System Using K Means and RBF Kernel Function, International Conference on Advanced Computing Technologies and Applications (ICACTA- 2015), Procedia Computer Science 45 ( 2015 ) 428 – 435
[9]. Lee, W. and S. J. Stolfo, ”Data mining approaches for intrusion detection”, In Proc. of the 7th USENIX Security Symp., San Antonio, TX.USENIX, 1998
[10]. Gao Xiang, Wang Min. Applying semisupervised cluster technique for anomaly detection. In: IEEE international symposium on information processing, no. 3; 2010. p. 43–5.
[11]. Mrutyunjaya Pandaa, Ajith Abrahamb, Manas Ranjan Patrac, a*,A Hybrid Intelligent Approach for Network Intrusion Detection, International Conference on Communication Technology and System Design 2011, Procedia Engineering 30 (2012) 1 – 9
[12]. Lane T. A decision-theoretic, semi-supervised model for intrusion detection. In: International conference on machine learning and data mining for computer security; 2006. p. 157–77.
[13]. Qiang Wang, Vasileios Megalooikonomou. A clustering technique for intrusion detection. In: International conference on data mining, intrusion detection, information assurance, and data networks, security, 5(12), 2005, p. 31–8.
[14]. Li Jimin, Zhang Wei, KunLun Li. A novel semi-supervised SVM based on tri-training for intrusion detection. J Comput 2010;5(4): 638–45.
[15]. G.V. Nadiammai, M. Hemalatha. The effective approach toward Intrusion Detection System using data mining techniques In: Egyptian Informatics Journal (2014) 15, 37–50, ISSN: 1110-8665.
[16]. Ghosh, A. and Schwartzbard, A. “A Study in using Neural Networks for Anomaly and Misuse detection”, in Proceedings of the Eighth USENIX Security Symposium, Vol. 8, pp. 443-482, 1999.
[17]. Zhang Fu, Marina Papatriantafilou, Philippas Tsigas. Off-the-wall: lightweight distributed filtering to mitigate distributed denial of service attacks. In: IEEE international symposium on reliable distributed systems, no. 31; 2012. p. 207–12.
[18]. SivathaSindhu, S.S., Geetha, S. and Kannan, A. “ Decision Tree-based Light Weight Intrusion Detection using a Wrapper Approach”, in Journal of Expert Systems with Applications, Vol. 39, pp. 129-141, 2012.
[19]. Zhang Fu. Marina Papatriantafilou, Philippas Tsigas. CluB: a cluster-based framework for mitigating distributed denial of service attacks. In: ACM symposium on applied computing, no. 26; 2011. p. 520–27.
[20]. Heady, R., Luger, G., Maccabe, A., and Servilla. M. “The Architecture of a Network Level Intrusion Detection System”, Technical report, Computer Science Department, University of New Mexico, 1990.
[21]. Hesham Altwaijry, Saeed Algarny, Bayesian-based intrusion detection system, Journal of King Saud University – Computer and Information Sciences, (2012) 24, 1–6
[22]. Jian Pei, Shambhu J. Upadhyaya, Faisal Farooq, Venugopal Govindaraju. Data Mining for Intrusion Detection – Techniques, Applications, and Systems. Data Mining Techniques for Intrusion Detection and Computer Security
[23]. Zhang Fu. Marina Papatriantafilou, Philippas Tsigas, Wei Wei. Mitigating denial of capability attacks using sink tree based quota allocation. In: ACM symposium on applied computing, no. 25; 2010. p. 713–18.
[24]. Li Hanguang, Ni Yu, Intrusion Detection Technology Research Based on Apriori Technique, 2012 International Conference on Applied Physics and Industrial Engineering, Physics Procedia 24 (2012) 1615 – 1620
[25]. Zhang Fu. Marina Papatriantafilou, Philippas Tsigas. CluB: a cluster-based framework for mitigating distributed denial of service attacks. In: ACM symposium on applied computing, no. 26; 2011. p. 520–27.
[26]. Chien-Yi Chiu, Yuh-Jye Lee, Chien-Chung Chang. Semi-supervised learning for false alarm reduction. In: Industrial conference on data mining, no. 10; 2010. p. 595–605.
[27]. Neminath Hubballi, Vinoth Suryanarayanan. False alarm minimization techniques in signature-based intrusion detection systems: A survey, Computer Communications 49 (2014) 1–17
[28]. PremaRajeswari, L., and Kannan, A. “An Intrusion Detection System based on Multiple-Level Hybrid Classifier using Enhanced C4.5”, IEEE International Conference on Signal Processing, Communications and Networking, pp. 75-79, 2008.
[29]. Vincenzo Gulisano, Zhang Fu, Mar Callau- Zori, Ricardo Jim Enez-Peris, Marina Papatriantafilou, Marta Patino-Martınez. STONE: a stream-based DDoS defense framework. In: Technical report no. 2012-07, ISSN 1652-926X, Chalmers University of Technology; 2012.
[30]. Zhang Fu, Marina Papatrianta Filou, Philippas Tsigas. Mitigating distributed denial of service attacks in multiparty applications in the presence of clock drifts. IEEE Trans Depend Secure Computing 2012;9(3):401–13.
[31]. Li Jimin, Zhang Wei, KunLun Li. A novel semi-supervised SVM based on tri-training for intrusion detection. J Comput 2010;5(4): 638–45.
[32]. Monowar H. Bhuyan, Bhattacharyya DK, Kalita JK. An effective unsupervised network anomaly detection method. In: International conference on advances in computing, communications and informatics, no. 1; 2012. p. 533–9.
[33]. Catania Carlos A, Garino Carlos. Automatic network intrusion detection: current techniques and open issues. Elsevier Comput Electr Eng 2012; 38(5):1062–72.
[34]. KDD Cup99 intrusion Detection Dataset.
Citation
M Naga Surya Lakshmi, K V N Sunitha, "Host Based Intrusion Detection Using Data Mining Methodologies," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.992-998, 2018.
Two-Level Image Encryption Algorithm Based On Key-Image
Research Paper | Journal Paper
Vol.6 , Issue.8 , pp.999-1008, Aug-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i8.9991008
Abstract
In the contemporary world images play key role in information interchange. Medical, defence, space and various areas of domain make use of high scale images in several applications. Security becomes the main concern wherein the images are to be protected so that they cannot be seen by any advisory. This can be achieved by image encryption. There are various image encryption methods that are based on textual key and text data which are not efficient for high definition images. In this paper we propose a three step image encryption algorithm which uses another image as a key. In the first step, key image is scaled and tiled, in step2 encryption is achieved using grey-value substitution and in step-3 the output generated from step-2 is scrambled using Fibonacci transformation to add additional security. This multistep encryption provides high security for images. The performance of this algorithm is analyzed using different attack models which results in high security without any loss of input image.
Key-Words / Index Term
Image security, Cryptography, Network security, Image processing
References
[1] Y. Zhou, K. Panetta and S. Agaian, "Image encryption using binary key-images," 2009 IEEE International Conference on Systems, Man and Cybernetics, San Antonio, TX, 2009, pp. 4569-4574. doi: 10.1109/ICSMC.2009.5346780
[2] Y. Zhou, K. Panetta and S. Agaian, "Comparison of recursive sequence based image scrambling algorithms," 2008 IEEE International Conference on Systems, Man and Cybernetics, Singapore, 2008, pp. 697-701. doi:10.1109/ICSMC.2008.4811359
[3] Yicong Zhou, Sos Agaian, Valencia M. Joyner, Karen Panetta, "Two Fibonacci P-code based image scrambling algorithms", Proc. SPIE 6812, Image Processing: Algorithms and Systems VI, 681215 (3 March 2008); doi:10.1117/12.766591
[4] Jiancheng Zou, R. K. Ward and Dongxu Qi, "A new digital image scrambling method based on Fibonacci numbers," 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512), 2004, pp.III-965-8Vol.3. doi:10.1109/ISCAS.2004.1328909
[5] K. C. Iyer and A. Subramanya, "Image Encryption by Pixel Property Separation," Cryptology ePrint Archive, 2009.
[6] M. Yang, N. Bourbakis, and S. Li, "Data-image-video encryption," Potentials, IEEE, vol. 23, no. 3, pp. 28-34,2004.
[7] J. Zou, R. K. Ward, and D. Qi, "A new digital image Scrambling method based on Fibonacci numbers," in Circuits and Systems, 2004. ISCAS `04. Proceedings of the 2004 International Symposium on, 2004 pp. III-965-8 Vol.3.
[8] A. Sharma, RS Thakur, S. Jaloree, "Investigation of Efficient Cryptic Algorithm for image files Encryption in Cloud", International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.5, pp.5-11, 2016.
[9] Rashmi P., Bharathi R.K., Shruthi Prabhakar, Reshma Banu, Rachana C.R., "Performance Analysis of Self Adaptive Image Encryption Technique", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.44-58, 2017.
[10] Kodge B. G., "Information Security: A Review on Steganography with Cryptography for Secured Data Transaction", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.6, pp.1-4, 2017.
Citation
V. Sridhar, M. Dyna, "Two-Level Image Encryption Algorithm Based On Key-Image," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.999-1008, 2018.