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Software reliability models is the best choice to monitor reliability of software process. These methods aid the software development team to identify the necessary actions that are carried out during software failure process. The present work attempts to develop a new model to check the software reliability by incorporating the failure rate of both hardware as well as software. The proposed new model based on time between failures observation, which is based on Non-Homogeneous Poisson Process (NHPP). Maximum Likelihood Estimation (MLE) method applied to determine the unknown parameters of the model.

Non-Homogeneous Poisson Process,Failure Rate, Parameter Estimation

[1] Alan Wood, “Software Reliability Growth Models”, In 1996.
[2] D.Swamydoss, Dr.Kadhar Nawaz “Enhanced Version of Growth Model in Web Based Software Reliability Engineering” in JGRCS Vol.2,No.12,Dec.2011.
[3] Kimura, M., Yamada, S., Osaki, S., 1995.” Statistical Software reliability prediction and its applicability based on mean time between failures”. Mathematical and Computer Modelling, Volume 22, Issues 10-12, Pages 149-155.
[4] R. Satya Prasad, K. R. H. Rao and R. R. L Kantha, “Software Reliability Measuring using Modified Maximum Likelihood Estimation and SPC”, International Journal of Computer Applications (0975–8887), vol. 21, no. 7, (2011) May, pp. 1-5.
[5] G.Gayathry, R.Thirumalai Selvi, “A New Reliability Growth Model to Estimate the Quality of Software” in IJET ,Volume 4,Issue 3,Pages 312-314, May 2018

R. ThirumalaiSelvi, G. Gayathry, "A Model for Reliability Estimation Using Inter Failures Time Data," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.875-877, 2018.

Secured and reliable services in cloud computing environment is an important issue. A sensitive data in a cloud computing environment is major issues with regard to security in a cloud based system. Many cloud service providers obtain server from other service providers due to it is cost affective and flexible for operation which makes way for data stolen from the external server. To counter a variety of attacks, especially large-scale coordinated attacks, this paper provides a framework for identifying intrusions in cloud environment using Decision Tree Techniques. The proposed system could reduce the impact of these kinds of attacks through providing timely notifications about new intrusions to Cloud users’ systems.

Cloud computing, IDS, Threats, Decision Tree, cloud service providers

[1] Xinfeng Ye Access Control for Cloud Applications IEEE 2015.
[2] Sonia Bassi et al. Cloud Computing Data Security-Background & Benefits IJCSC 2015.
[3] Navadeep Agganwal et al. Cloud Computing: Data Storage Security Analysis and its Challenges International Journal of Computer Applications Volume 70– No.24, May 2013.
[4] Keiko Hashizume et al. An Analysis of security issues for cloud computing, Journal of Internet services and applications, Springer 2013.
[5] Manoj K et al. Unsupervised Outlier Detection Technique for Intrusion Detection in Cloud Computing, ICCT IEEE 2014.
[6] Roshanak Roshandel et al. User-Centric Monitoring of Sensitive Information Access in Android Applications, 2nd ACM International Conference on Mobile Software Engineering and Systems IEEE 2015.
[7] Richa et. al, To Improve Security in Cloud Computing with Intrusion detection system using Neural Network. IJSCE 2013.
[8] J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
[9] J. R. Quinlan. Induction of Decision Trees. Machine Learning, 1:81-106, 1986.
[10] Manish, Dr. Hanumanthappa M, Intrusion Detection System Using Decision Tree Algorithm, IEEE 2012.

Gopala B, M. Hanumanthappa, "An Intrusion Detection System for Malicious Attacks in Cloud Environment Using Decision Tree Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.878-881, 2018.

Cloud computing is the latest paradigm for providing many types of facilities that are suitable to transfer the data or any other information from the resource constraint devices. It is the delivery of computing services. Various services are servers, storage, database, software, networking and analytics over the network.. A lot of frameworks have stated the features of mobile cloud computing and challenges faced during its operational activities along with the concept of load balancing and offloading. Computation offloading can reduce the load during mobile computing. Load balancing is a concept that is used in the well allocation of resources to provide complete satisfaction of user during the remote processing of the mobile application. They are saving a lot of energy and enhance the performance of mobile devices. A lot of research work has been carried out on a single site offloading, but there is a need to carry out work on cost minimization in multisite offloading.. This proposed work provides better cost in case of various information centres using Ant Colony Optimization (ACO).We used ACO algorithm to minimize the cost of virtual machines of different sites. Matlab Simulation Tool has been used to perform cost optimization using ACO and greedy algorithms considering the deadline. Both ACO and Greedy algorithm have been compared by simulation in MATLAB in order to optimize the costs. The proposed methodology has been evaluated on two cloud services namely Amazon and Microsoft Azure for cost minimization and the results shows that the ACO is better as compared to compare to greedy approach for minimization of cost.

Mobile offloading, ACO, greedy algorithm, cost optimization

[1] P. Bahl, R. Y. Han, Li Erran, and M. Satyanarayanan, “Advancing the State of Mobile Cloud Computing,” in Proc. of MCS’12, June 25, 2012.
[2] M. Satyanarayanan, P. Bahl, R. Caceres, and N. Davies, “The case for VM-based cloudlets in mobile computing,” Pervasive Computing, IEEE, Volume 8, No. 4, pp. 14 –23, 2009.
[3] S. Kosta, A. Aucinas, P. Hui, R. Mortier, and X. Zhang, “Thinkair: Dynamic Resource Allocation and Parallel Execution in the Cloud for Mobile Code Offloading,” in Proc. of IEEE INFOCOM, 2012.
[4] M. V. Barbera, S. Kosta, A. Mei, and J. Stefa, “To Offload or Not to Offload? The Bandwidth & Energy Costs of Mobile Cloud Computing,” in Proc. of IEEE INFOCOM, 2013.

[5] N. Kaushik, and J. Kumar, “A Computation Offloading Framework to Optimize Energy Utilization in Mobile Cloud Computing Environment,” International Journal of Computer Applications & Information Technology, Volume 5, Issue II, April-May 2014.
[6] M. Jia, J. Cao, L. Yang “Heuristic Offloading of Concurrent Tasks for Computation-Intensive Applications in Mobile Cloud Computing” IEEE INFOCOM Workshop on Mobile Cloud Computing, 2014.
[7] P. Yang, Q. Li, “Friend is Treasure”: Exploring and Exploiting Mobile Social Contacts for Efficient Task Offloading”, 2015
[8] G. Orsinia, D. Bade “Context-Aware Computation Offloading for Mobile Cloud Computing: Requirements Analysis, Survey and Design Guideline”, The 12th International Conference on Mobile Systems and Pervasive Computing, 2015.
[9] A. Mukherjee, and D. De, “Low power offloading strategy for Femto-cloud mobile network,” Engineering Science & Technology, an International Journal, Vol. 19, Issue 1, pp. 260-270, March 2016.
[10] M. Shiraz, M. Sookhak, A. Gani, and S.A. Shah, “A Study on the Critical Analysis of Computational Offloading Frameworks for Mobile Cloud Computing,” Journal of Network and Computer Applications Vol. 47, pp. 47-60, 2017.
[11] D. Kovachev and R. Klamma” Framework for Computation Offloading in Mobile Cloud Computing” International Journal of Artificial Intelligence and Interactive Multimedia, Vol. 1, N7, 2017.
[12] R. Beraldi, A. Mtibaa “Cooperative Load Balancing Scheme for Edge Computing Resources”, 2017 Second International Conference on Fog and Mobile Edge Computing, 2017.
[13] C-A. Chen, R. Stoleruy, G.G. Xie “Energy-efficient Load-balanced Heterogeneous Mobile Cloud”,2017.
[14] P. Nawrocki, W. Reszelewski “Resource usage optimization in Mobile Cloud Computing”,2017.
[15] P.Yang, “Friend is Treasure”: Exploring and Exploiting Mobile Social Contacts for Efficient Task Offloading”, 0018-9545, 2015.
[16] M. V. Barbera, A .C. Viana, M. D Amorim, “Data offloading in social mobile networks through VIP delegation,” ad-hoc network, volume 19, pages 92-110, 2014.
[17] K. Kumar and Y-H Lu, “Cloud Computing For Mobile Users: Can Offloading Computation Save Energy?” Published by the IEEE Computer Society, April 2010.
[18] P. Nawrocki, and W. Reszelewski, “Resource usage optimization in Mobile Cloud Computing,” in Proc. of Computer Communications 99, pp. 1-12, 2017.
[19] G. Calice, A. Mtibaa, R. Beraldi, and H. Alnuweiri, “Mobile-to-Mobile Opportunistic Task Splitting and Offloading,” in Proc. of IEEE 11th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), 2015.
[20] G. Orsinia, D. Badea, and W. Lamersdorf, “Context-Aware Computation Offloading for Mobile Cloud Computing: Requirements Analysis, Survey & Design Guideline,” in Proc. of 12th International Conference on Mobile Systems & Pervasive Computing, Volume 56, pp. 10 – 17, 2015.

Kirti, Jitender Kumar, "Energy Efficient Load Balancing Strategy for Better Cost Of Multisite Offloading," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.882-889, 2018.

Mobile Ad hoc networks (MANETs) provide autonomous communication between the mobile nodes in the absence of predefined infrastructure. This property of MANETs makes it more vulnerable from conventional networks. Due to this reason, prevention mechanism such as authentication and cryptography techniques alone are not capable to protect it so that intrusion detection system (IDS) employed to facilitate the identification of intrusions in MANETs. This paper examined the detailed analysis on each class of intrusion detection systems that have been proposed in MANETs for preventing the network layer attacks and also focuses the further research areas in MANETs.

Mobile Ad hoc Networks (MANETs), Security Issues, Intrusion Detection System (IDS), Detection Architecture and Detection Techniques

[1] IETF Mobile Ad-Hoc Networks Working Group (MANET), IETF web-site www.ietf.org/dyn/wg/charter/manet-charter.html.
[2] IETF Ad-Hoc Networks Autoconfigurations (autoconf) Working Group, IETF website http://datatracker.ietf.org/wg/autoconf/charter/
[3] IEEE Std 802.11-2007, “IEEE standard for information technology-Telecommunication and information exchange between systems- Local and metropolitan area network-Specific requirement, Part 11 Wireless LAN medium access control and physical layer specifications”, June 2007.
[4] Chaudhary, Alka, V. N. Tiwari, and Anil Kumar. "A new intrusion detection system based on soft computing techniques using neuro-fuzzy classifier for packet dropping attack in manets." International Journal of Network Security 18, no. 3, 514-522, 2016.
[5] Chaudhary, A., V. N. Tiwari, and A. Kumar. "Analysis of fuzzy logic based intrusion detection systems in mobile ad hoc networks." BVICA M`s International Journal of Information Technology 6.1, 2014.
[6] Chaudhary, Alka. "Neuro-fuzzy based intrusion detection systems for network security." Journal of Global Research in Computer Science 5.1, pp. 1-2, 2014.
[7] Lundin E., Jonsson E., “Survey of Intrusion Detection Research”, Technical report 02-04, Dept. of Computer Engineering, Chalmers University of Technology, (2002).
[8] Kim J., P. Bentley, "The Artificial Immune Model for Network Intrusion Detection", In 7th European Congress on Intelligent Techniques and Soft Computing (EUFIT’99). Aachen, Germany. September 1999.
[9] Uppuluri P., Sekar R., “Experiences with Specification-based Intrusion Detection. In Proc of the 4th Int Symp on Recent Adv in Intrusion Detect LNCS 2212: 172-189, 2001.
[10] Tseng C-Y, Balasubramayan P. et al., “A Specification-Based Intrusion Detection System for AODV” , In Proc of the ACM Workshop on Secur in Ad Hoc and Sens Netw (SASN), 2003.
[11] Huang Y., Lee W., “Attack Analysis and Detection for Ad Hoc Routing Protocols”, In Proc of Recent Adv in Intrusion Detect LNCS 3224:125-145, 2004.
[12] Y. F. Jou, F. Gong, C. Sargor, X. Wu, S. Wu, H. Chang, and F., “ Wang, Design and Implementation of a Scalable Intrusion Detection System for the Protection of Networks Infrastructure," Proceedings of DARPA Information Survivability Conference and Exposition, Vol. 2, pp. 69-83, January 2000.
[13] Sevil Şen, John A. Clark “Intrusion detection in mobile ad hoc networks”, In Chapter 17, Guide to Wireless Ad Hoc Networks, Springer, 2008.
[14] P. Brutch and C. Ko, “Challenges in Intrusion Detection for Wireless Ad-hoc Networks," Proceedings of 2003 Symposium on Applications and the Internet Workshop, pp. 368-373, January 2003.
[15] Y. Zhang and W. Lee., “ Intrusion detection in wireless ad hoc networks” , In Proceedings of the 6th Annual International Conference on Mobile Computing and Networking (MobiCom`00), pages 275-283, 2000.
[16] Y. Zhang, W. Lee, and Y. Huang, “Intrusion detection techniques for mobile wireless networks”, In wireless Networks Journal (ACM WINET), September 2003.
[17] A.B. Smith, “An examination of an intrusion detection architecture for wireless ad hoc networks” , In Proceedings of the 5th National Colloquium for Information System Security Education, 2001.
[18] Y. Huang, Wei Fan, Wenke Lee, and Philip S. Yu, “Cross-feature analysis for detection ad-hoc routing anomalies”, In Proceedings of the 23rd International Conference on Distributed Computing Systems (ICDCS).
[19] Y. Huang and W. Lee., ”A cooperative intrusion detection system for ad hoc net-works”, In Proceedings of the 1st ACM Workshop on Security of Ad Hoc and Sensor Networks, 2003.
[20] B. Sun, K. Wu, and U.W. Pooch, “Zone-based intrusion detection for mobile ad hoc networks”, International Journal of Ad Hoc and Sensor Wireless Networks, 2003.
[21] B. Sun, “Intrusion Detection in Mobile Ad Hoc Networks”, PhD thesis, Computer Science, Texas A&M University, 2004.
[22] O. Kachirski and R. Guha, “Effective intrusion detection using multiple sensors in wireless ad hoc networks”, In Proceedings of the 36th IEEE International Conference on System Sciences, 2003.
[23] C.-Y. Tseng, P. Balasubramayan, C. Ko, R. Limprasittiporn, J. Rowe, and K. Levitt, “A specification-based intrusion detection system for AODV”, In Proceedings of the ACM Workshop on Security in Ad Hoc and Sensor Networks (SASN), 2003.
[24] E. Hansson, J. Gronkvist, K. Persson, and D. Nardquist, “Specification-based intrusion detection combined with cryptography methods for mobile ad hoc networks”, Technical report, FOI Swedish Defence Research Agency/Command and Control Systems, 2005.
[25] H.M. Hassan, M. Mahmoud, and S. El-Kassas, “ Securing the AODV protocol using specification-based intrusion detection”, In Proceedings of the 2nd ACM International Workshop on Quality of Service and Security for Wireless and Mobile Networks, pages 33-35, 2006.
[26] M. Wang, L. Lamont, P. Mason, and M. Gorlatova, “An effective intrusion detection approach for OLSR MANET protocol” ,In Proceedings of the 1st IEEE ICNP Workshop on Secure Network Protocols, pages 55{60, 2005.
[27] S. Sarafijanovic and J. Le Boudec, “An artificial immune system for misbehavior detection in mobile ad-hoc networks with virtual thymus, clustering, danger signal and memory detectors”, 2004, pp. 342- 356
[28] Y. Huang and Wenke Lee, “ Attack analysis and detection for ad hoc routing protocols” ,In Proceedings of the 7th International Symposium on Recent Advances in Intrusion Detection (RAID`04), pages 125-145, Springer, 2004.
[29] Megat Farez Azril Bin Zuhairi, M., Mohammad Haseeb Zafar, and David Harle. "The impact of mobility models on the performance of mobile Ad Hoc network routing protocol", IETE Technical Review, Vol. 29, No. 5, pp. 414-420, 2012.
[30] Ping Yi, Yiping Zhong, Shiyong Zhang, “A novel intrusion detection method for mobile ad hoc networks in Proceeding EGC`05 Proceedings of the 2005 European conference on Advances in Grid Computing Pages 1183-1192 2005.
[31] J.-M. Orset, B. Alcalde, and A. R. Cavalli, “An EFSM-Based Intrusion Detection System for Ad Hoc Networks”, Proc. International Conference on Automated Technology for Verification and Analysis, pp 400–413, 2005.
[32] Dina Sadat Jalali, Alireza Shahrbanoonezhad, “ a new intrusion detection method based on fsm and cache memory in ad hoc networks”,In Proceedings of IEEE CCIS2011, 2011
[33] L. Yu, L. Yang, and M. Hong, “Short Paper: A Distributed Cross-Layer Intrusion Detection System for Ad Hoc Networks,” in Proceedings of the 1st International Conference on Security and Privacy for Emerging Areas in Communication Networks, Athens, Greece, pp. 418-420, September 2005.
[34] S.Bose and A. Kannan, “Detecting Denial of Service Attacks using Cross Layer based Intrusion Detection System in Wireless Ad Hoc Networks” ,Iin International Conference on Signal Processing, Communications and Networking, ICSCN `08, 2008.
[35] Rakesh Shrestha, Kyong-Heon Han, Dong-You Choi, Seung-Jo Han, “A Novel Cross Layer Intrusion Detection System in MANET “, In 24th IEEE International Conference on Advanced Information Networking and Applications, 2010
[36] C. J. John Felix, A. Das, B.C. Seet, and B.-S. Lee, “CRADS: Integrated Cross Layer Approach for Detecting Routing Attacks in MANETs,” in IEEE Wireless Communications and Networking Conference (WCNC), Las Vegas, CA, USA, pp. 1525-1530, March 2008.
[37] .H. Tseng, S.-H. Wang, Wenke Lee, C. Ko, and K. Lewitt, “Demem: Distributed evidence driven message exchange intrusion detection model for MANET”, In Proceedings of the 9th International Symposium on Recent Advances in Intrusion Detection (RAID`06), pages 249-271. Springer, 2006.
[38] Sanandaji, A., Jabbehdari, S., Balador, A., & Kanellopoulos, D, “MAC Layer Misbehavior in MANETs”, IETE Technical Review, Vo. 30, No. 4, 2013.
[39] C.H. Tseng, T. Song, P. Balasubramanyam, C. Ko, and K. Levitt, “A specification-based intrusion detection model for OLSR”,In Proceedings of the 8th International Symposium on Recent Advances in Intrusion Detection (RAID`05), LNCS 3858, pages 330{350. Springer, 2005.
[40] A. Mitrokosta, N. Komninos and C. Douligeris, “Intrusion Detection with Neural Networks and Watermarking Techniques for MANETs”, In Proc. IEEE International Conference on Pervasive Services, pp 118-127, July 2007.
[41] Min-Hua Shao, Ji-Bin Lin, Yi-Ping Lee, “Cluster-based Cooperative Back Propagation Network Approach for Intrusion Detection in MANET in IEEE 10th International Conference on Computer an Information Technology (CIT), 2010.
[42] Zahra moradi Mohammad Teshnehlab Amir Masoud Rahmani, “ Implementation of Neural Networks for Intrusion Detection in MANET”, IN International Conference on Emerging Trends in Electrical and Computer Technology (ICETECT), 2011.
[43] Pasquale Donadio, Antonio Cimmino and Giorgio Ventre “Enhanced Intrusion Detection Systems in Ad Hoc Networks using a Grid Based Agnostic Middleware” , In Proceeding AUPC `08 Proceedings of the 2nd international workshop on Agent-oriented software engineering.challenges for ubiquitous and pervasive computing Pages 15-20, 2008.
[44] S.Sen and John Andrew Clark “A Grammatical Evolution Approach to Intrusion Detection on Mobile Ad hoc Networks”, In WiSec ’09: Proceedings of the Second ACM Conference on Wireless Network Security March 2009..
[45] S. Sen, J. A. Clark, and J. E. Tapiador, “Power-aware intrusion detection in mobile ad hoc networks”, in Ad Hoc Networks, ser. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Springer Berlin Heidelberg, vol. 28, pp. 224–239, 2010.
[46] Sujatha, K.S., “Design of genetic algorithm based IDS for MANET”, In IEEE International Conference on Recent Trends in Information Technology (ICRTIT), 28-33, 19-21 April 2012.
[47] Monita Wahengbam and Ningrinla Marchang, “Intrusion Detection in MANET using Fuzzy Logic”, In IEEE 3rd National Conference, pages 189-192 March 2012.
[48] Patidar, V., & Kumbhkar, M., “Analysis of Cloud Computing Security Issues in Software as a Service”, International Journal of Scientific Research in Computer Science and Engineering, 2(3), pp. 1-5, 2014.

A. Chaudhary, G. Shrimal, "A Critical Review of Intrusion Detection Systems and Its Applicability in Mobile Ad Hoc Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.890-898, 2018.

A mathematical investigation has been made to examine in what way the power function of the usual control chart for mean based on the assumption of normality is affected when the characteristics of an item possesses a non-normal distribution specified by the first four terms of an Edgeworth series. Power functions for known standard deviation have been considered and expressions giving corrections due to parental skewness and kurtosis have been obtained in addition to the normal theory expression.

Average Control Chart, Power Function, Non-Normal distribution

[1]. Chou, C.Y., Chen, C.H. and Liu, H.R. (2005). Acceptance Control Chart for Non-normal Data, Journal of Applied Statistics, 32(1), 25-36.
[2]. Chou, C.Y., Chen, C.H., Liu, H.R. and Wang, P.H. (2000). Statistically Minimum-loss Design of Averages Control Charts for Non-normal Data, Proc. Natl. Sci. Counc. ROC(A), 24(6), 472-479.
[3]. Doclos, E., Pillet, M. and Avrillon, L. (2005). The L-Chart for Non-Normal Process, Quality Technology & Quantitative Management, 2(1), 77-90.
[4]. Haynes, M., Mengersen, K. and Rippon, P. (2008). Generalized Control Charts for Non-Normal Data Using g-and-k Distribution, Communication in Statistics- Simulation and Computation, 37, 1881-1903.
[5]. Singh, J.R. and Mishra, U. (2017). Power of Control Chart for Singly Truncated Binomial Distribution under Inspection Error, Global and Stochastic Analysis, Special Issue: 25th International Conference of Forum for Interdisciplinary Mathematics.
[6]. Yourstone, S.A. and Zimmer, W.J. (1992). Non-Normality and the design of control charts for averages, Decision Sciences, 23, 1099-1113.

J.R.Singh, U. Mishra, "Statistical Power Function of Average Control Charts for Non-Normal Data," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.899-901, 2018.

Modern digital technology and the availability of increasingly powerful image processing tools can easily manipulate the digital images without leaving obvious visual traces of having been tampered, so there is an urgent need to identify the authenticity of images. In the fields such as forensics, medical imaging, e-commerce, and industrial photography, authenticity and integrity of digital images is essential. In this paper going to see about different types of detection techniques are namely called as active methods, passive, cloning and splicing.

Fragile Watermark, Semi fragile watermark, Passive Detection Method, Splicing Detection

[1] Dimpy Bansal, Sukhminder Kaushal,” A Novel Analysis Of Image Forgery Detection Using SVM”, International Journal Of Engineering And Applied Sciences (Ijeas) Issn: 2394-3661, Volume-3, Issue-12, December 2016.
[2] Mr. Soumen K. Patra, Mr. Abhijit D. Bijwe,” Copy-Move Image Forgery Detection Using Svd”, International Research Journal Of Engineering And Technology (Irjet).
[3] Manish Jain , Vinod Rampure,” Algorithm For The Digital Forgery Catching Technique For Image Processing Application”, International Journal Of Advancement In Engineering Technology, Management And Applied Science (Ijaetmas).
[4] [13] Abhishek Kashyap, Rajesh Singh Parmar, Megha Agarwal, Hariom,” An Evaluation Of Digital Image Forgery Detection Approaches”, International Journal Of Applied Engineering Research, Issn 0973-4562 Volume 12, Number 15 (2017) Pp. 4747–4758.
[5] Sapna Sameria, Vaibhav Saran, A.K.Gupta,” A Review Of Trends In Digital Image Processing For Forensic Consideration”, Ijournals: International Journal Of Software & Hardware Research In Engineering Issn-2347-4890.
[6] Harpreet Kaur1 , Jyoti Saxena2 And Sukhjinder Singh,” Simulative Comparison Of Copy- Move Forgery Detection Methods For Digital Images”, International Journal Of Electronics, Electrical And Computational System Ijeecs Issn 2348-117x.
[7] Sini P Somanathan, D Jude Hemanth, Jisi C And Jyothi M,” Forgery Detection In Digital Images Using Clustering Techniques”, International Conference On Security And Authentication - Sapience14.
[8] Amandeep Kaur, Vaibhav Saran, A. K. Gupta,” Digital Image Processing For Forensic Analysis Of Fabricated Documents”, International Journal Of Advanced Research In Science, Engineering And Technology.
[9] Jobin Abraham,” A Blind Watermarking Scheme For Tamper Detection In Digital Images”, Ictact Journal On Image And Video Processing, November 2015, Volume: 06, Issue: 02.
[10] Mrugesha Lad , Naresh Patel,” Passive Digital Image Forgery Detection Techniques And Implementation”, International Journal Of Innovative Research In Electrical, Electronics, Instrumentation And Control Engineering Vol. 4, Issue 5, May 2016.
[11] Thuong Le-Tien, 1marieluong, 2tu Huynh-Kha, Long Pham-Cong-Hoan, An Tran-Hong,” Block Based Technique For Detecting Copy-Move Digital Image Forgeries: Wavelet Transform And Zernike Moments”, Proceedings Of The Second International Conference On Electrical And Electronic Engineering, Telecommunication Engineering, And Mechatronics, Philippines 2016.
[12] Mrs.Nisha , Mr. Mohit Kumar,” Review Of Copy Move Forgery With Key Point Features”,,International Journal Of Advance Research , Ideas And Innovations In Technology.
[13] Yadwinder Kaur, Dr. Sukhjeet Kaur Ranade,” Image Authentication And Tamper Detection Using Fragile Watermarking In Spatial Domain”, International Journal Of Advanced Research In Computer Engineering & Technology (Ijarcet) Volume 6, Issue 7, July 2017, Issn: 2278 – 1323.

K. Manikantan, "Analyse And Overview on Digital Image Tampering Detection Using Matlab," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.902-906, 2018.

Nowadays, Mobile ad hoc networks are very attractive in terms of its flexibility because there is no need of predefined infrastructure for communication such as wired network. This flexibility makes mobile ad hoc network more vulnerable to threats. Threats compromise with the attributes of security so reason being threats can be decreased the survivability or performance of the mobile ad hoc networks. Several types of attacks in MANETs have been reviewed in the literature, the most popular attack is Distributed Denial of Services Attack (DDoS) because the aim of DDoS is to restrict the MANETs to provide the normal services to their intended users by consuming bandwidth or overload the resources. This paper focuses on the several detection techniques of DDoS attack and proposed a new technique based on genetic algorithm for the detection of DDoS attack on MANETs.

Mobile Ad hoc Networks (MANETs), DDoS, Intrusion Detection System (IDS), Detection Techniques

[1] IETF Mobile Ad-Hoc Networks Working Group (MANET), IETF web-site www.ietf.org/dyn/wg/charter/manet-charter.html
[2] IETF Ad-Hoc Networks Autoconfigurations (autoconf) Working Group, IETF website http://datatracker.ietf.org/wg/autoconf/charter/
[3] IEEE Std 802.11-2007, “IEEE standard for information technology-Telecommunication and information exchange between systems- Local and metropolitan area network-Specific requirement, Part 11 Wireless LAN medium access control and physical layer specifications”, June 2007.
[4] Chaudhary, Alka, V. N. Tiwari, and Anil Kumar. "A new intrusion detection system based on soft computing techniques using neuro-fuzzy classifier for packet dropping attack in manets", International Journal of Network Security 18, no. 3, 514-522, 2016.
[5] Chaudhary, A., V. N. Tiwari, and A. Kumar. "Analysis of fuzzy logic based intrusion detection systems in mobile ad hoc networks", BVICA M`s International Journal of Information Technology 6.1, 2014.
[6] Chaudhary, Alka. "Neuro-fuzzy based intrusion detection systems for network security", Journal of Global Research in Computer Science 5.1, pp. 1-2, 2014.
[7] Farooqui, Yassir, Vanita Mane, and Puja Padiya. "DDOS using Intrusion Detection System in Wireless Mobile Ad hoc Network”.
[8] Yi, Ping, et al. "A new routing attack in mobile ad hoc networks", International Journal of Information Technology 11.2, pp. 83-94,2005.
[9] Arunmozhi, S. A., and Y. Venkataramani. "DDoS Attack and Defense Scheme in Wireless Ad hoc Networks", arXiv preprint arXiv: pp.1106.1287,2011.
[11] Ahamad, Tariq, and Abdullah Aljumah. "Detection and defense mechanism against DDoS in MANET", Indian Journal of Science and Technology 8.33, 2015.
[10] Timcenko, V. V. "An approach for DDoS attack prevention in mobile ad hoc networks", Elektronika Ir Elektrotechnika 20, no. 6 pp.150-153, 2014.
[12] Huang, Qiang, Hisashi Kobayashi, and Bede Liu. "Modeling of distributed denial of service attacks in wireless networks", Communications, Computers and signal Processing, 2003. PACRIM. 2003 IEEE Pacific Rim Conference on. Vol. 1. IEEE, 2003.
[13] Aljumah, Abdullah. "detecting distributed denial of service (ddos) attack using ttl constraint in mobile adhoc networks (manet)", Science International 27.6,2015.
[14] Sharma, Prajeet, Niresh Sharma, and Rajdeep Singh. "A Secure Intrusion detection system against DDOS attack in Wireless Mobile Ad-hoc Network", International Journal of Computer Applications 41.21 2012.
[15] Thyagarajan, Karthikeyan, and Arunkumar Thangavelu. "An Integrated Defense Approach for Distributed Denial of Service Attacks In Mobile Ad-Hoc Network", International Journal of Applied Engineering Research 11.7, pp. 4898-4910,2016.
[16] Sharma, Neeraj, B. L. Raina, Prabha Rani, Yogesh Chaba, and Yudhvir Singh. "Attack Prevention Method Methods for DDOS Attack in MANET", Asian Journal Of Computer Science And Information Technology1 1, pp.18-21, 2010.
[17] Denko, Mieso K. "Detection and prevention of Denial of Service (DoS) attacks in mobile ad hoc networks using reputation-based incentive scheme", Journal of Systemics, Cybernetics and Informatics 3.4, pp. 1-9, 2005.
[18] Kumar, Mukesh, and Naresh Kumar. "DETECTION AND PREVENTION OF DDOS ATTACK IN MANET`S USING DISABLE IP BROADCAST TECHNIQUE", International Journal of Application or Innovation in Engineering & Management 2.7, pp. 29-36, 2013.
[19] Vishwakarma, Deepak, and D. S. Rao. "Detection Mechanism for Distributed Denial of Service (DDoS) Attack in Mobile Ad-hoc Networks", International Journal of Computer Applications 102.9, pp. 23-26, 2014.
[20] Kumari, Ranjana, and Achint Chugh. "Distributed Denial of Service Attack Detection, Prevention and Secure Communication in MANET",
[21] Timcenko, V. A. L. E. N. T. I. N. A., and Mirjana Stojanovic. "Application of forensic analysis for intrusion detection against DDoS attacks in mobile ad hoc networks", Proceedings of the 1st WSEAS Int. Conf. on Information Technology and Computer Networks (ITCN`12), Vienna. 2012.
[22] Nadeem, Adnan, and Michael Howarth. "Adaptive intrusion detection & prevention of denial of service attacks in MANETs", Proceedings of the 2009 international conference on wireless communications and mobile computing: Connecting the world wirelessly. ACM, 2009.
[23] Mitrokotsa, Aikaterini, Rosa Mavropodi, and Christos Douligeris. "Intrusion detection of packet dropping attacks in mobile ad hoc networks," Proceedings of the International Conference on Intelligent Systems And Computing: Theory And Applications. 2006.
[24] Chhabra, Meghna, Brij Gupta, and Ammar Almomani. "A novel solution to handle DDOS attack in MANET", Journal of Information Security 4.03, 2013.
[25] Sharma, Prajeet, Niresh Sharma, and Rajdeep Singh. "A Secure Intrusion detection system against DDOS attack in Wireless Mobile Ad-hoc Network", International Journal of Computer Applications 41.21 2012.
[26] Patidar, V., & Kumbhkar, M., “Analysis of Cloud Computing Security Issues in Software as a Service”, International Journal of Scientific Research in Computer Science and Engineering, 2(3), pp. 1-5, 2014.

A. Chaudhary, G. Shrimal, R. Rautela, "Distributed Denial of Service Attack Detection Techniques for Mobile Ad Hoc Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.907-914, 2018.

Now a Day, Many Companies and organizations are to make a large volume of information. In the enterprise, choice producers access from all sources and types of collection methods. The information warehouse is used for the enterprise for enhancing the selection-making. In aggressive commercial enterprise international, the values of strategic statistics techniques along with these are actually identified. The enterprise surroundings, the pace isn`t the simplest key to competitiveness. To investigate the records, it needs the unique equipment are called facts mining things. This paper survey of the facts mining set of rules which include Clustering, Time series, Logistic Regression, Naïve Bayes and its programs within the exclusive areas.

Data mining, Clustering, Time series, Logistic Regression, Naïve Bayes

[1] Sam Fletcher et al. “An anonymization technique using intersected decision trees” Journal of King Saud University – Computer and Information Sciences (2015) 27, 297–304.
[2] Ashish Kumar et al. “Implementation and Comparison of Decision Tree Based Algorithms”, International Journal of Innovations & Advancement in Computer Science IJIACS ISSN 2347 – 8616 Volume 4, Special Issue May 2015.
[3] Yash Jain et al.“A Survey On Data Mining Techniques, Their Application And Future Scope", International Journal Of Engineering Sciences & Research Technology [Jain*, 4.(8): April 2015] ISSN: 2277-9655 (I2OR), Publication Impact Factor: 3.785
[4] Neha Khan et al. “Big Data Classification using Evolutionary Techniques: A Survey”,2015 IEEE International Conference on Engineering and Technology (ICETECH), 20th March 2015, Coimbatore, TN, India.
[5] Sonia Singh et al. “COMPARATIVE STUDY ID3, CART AND C4.5 DECISION TREE ALGORITHM: A SURVEY”, International Journal of Advanced Information Science and Technology (IJAIST), ISSN: 2319:2682 Vol.27, No.27, July 2014.
[6] Manpreet Singh et al. “Performance Analysis of Decision Trees", International Journal of Computer Applications (0975 – 8887) Volume 71– No.19, June 2013
[7] T.Miranda Lakshmi et al. “An Analysis On Performance Of Decision Tree Algorithms Using Student’s Qualitative Data”, I.J.Modern Education And Computer Science, 2013, 5, 18-27 Published Online June 2013 In MECS (Http://Www.Mecs-Press.Org/) DOI: 10.5815/Ijmecs.2013.05.03
[8] Nikita Jain, Vishal Srivastava, M. Tech. Scholar, Associate Professor, Arya College Of Engineering And IT, “Data Mining Techniques: A Survey Paper”, International Journal Of Research In Engineering And Technology Eisen: 2319-1163 | ISSN: 2321-7308.
[9] Shikha Chourasia, “Survey paper on improved methods of ID3 decision tree classification”,
[10] International Journal of Scientific and Research Publications, Volume 3, Issue 12, December 2013 ISSN 2250-3153
[11] Mohd Afizi Mohd Shukran, Faculty of Science & Defense Technology, National Defense University of Malaysia (NDUM) "Artificial Bee Colony based Data Mining Algorithms for Classification Tasks", Canadian Center of Science and Education www.ccsenet.org/mas Modern Applied Science Vol. 5, No. 4; August 2011.
[12] Mrs. Bharati M. Ramageri, Lecturer Of Modern Institute Of Information Technology And Research, Department Of Computer Application, “Data Mining Techniques And Applications”, Indian Journal Of Computer Science And Engineering Vol. 1 No. 4 301-305.
[13] Fahad S. Abu-Mouti and Mohamed E. El-Hawary, “Overview of Artificial Bee Colony (ABC) Algorithm and Its Applications” IEEE, 2012.
[14] Er. Ankit Choubey And Dr. G. L. Prajapati, ” An Understanding Of Abc Algorithm And Its Applications” International Journal Of Current Engineering And Scientific Research (IJCESR), ISSN (Print): 2393-8374, (Online): 2394-0697, Volume-2, Issue-10, 2015.
[15] Alkin Yurtkuran and Erdal Emel, “An Enhanced Artificial Bee Colony Algorithm with Solution Acceptance Rule and Probabilistic Multisearch” Hindawi Publishing Corporation Computational Intelligence and Neuroscience Volume 2016, Article ID 8085953,2016.
[16] Quande Qin, Shi Cheng, Qingyu Zhang, Li Li and Yuhui Shi, "Artificial Bee Colony Algorithm with Time-Varying Strategy" Hindawi Publishing Corporation Discrete Dynamics in Nature and Society Volume 2015, Article ID 674595, 2015.
[17] Dhanya P Varghese & Tintu P B, "A SURVEY ON HEALTH DATA USING DATA MINING TECHNIQUES", International Research Journal of Engineering and Technology (JET), Volume: 02 Issue: 07, Oct-2015.
[18] Doron Shalvi & Nicholas DeClaris, “AN UNSUPERVISED NEURAL NETWORK APPROACH TO MEDICAL DATA MINING TECHNIQUES”, IEEE, 1998.
[19] Gustavo Santos-Garcia & Gonzalo Varela & Nuria Novoa & Marcelo F. Jimenez, “PREDICTION OF POSTOPERATIVE MORBIDITY AFTER LUNG RESECTION USING AN ARTIFICIAL NEURAL NETWORK ENSEMBLE”, Artificial Intelligence in Medicine 30:61–69, 2004.
[20] Hojin Moon & Hongshik Ahn & Ralph Kodell & Songjoon Baek & Chien- Ju Lin & James Chen, “ENSEMBLE METHODS FOR CLASSIFICATION OF PATIENTS FOR PERSONALIZED MEDICINE WITH HIGH-DIMENSIONAL DATA”. Artificial Intelligence in Medicine 41:197–207, 2007.
[21] I. Curiac & G. Vasile & O. Banias & C. Volosencu & A. Albu, “BAYESIAN NETWORK MODEL FOR DIAGNOSIS OF PSYCHIATRIC DISEASES”, Proceedings of the ITI 2009 31st Int. Conf. on Information Technology Interfaces, Cavtat, Croatia, 22-25 June-2009.
[22] Jeong-Yon Shim & Lei Xu, “MEDICAL DATA MINING MODEL FOR ORIENTAL MEDICINE VIA BYY BINARY INDEPENDENT FACTOR ANALYSIS”, IEEE.P1-4, 2003.
[23] K.Sharmila & Dr.S.A.Vethamanickam, “SURVEY ON DATA MINING ALGORITHM AND ITS APPLICATION IN HEALTHCARE SECTOR USING HADOOP PLATFORM”, International Journal of Emerging Technology and Advanced Engineering ISSN 2250-2459, Volume: 05, Issue: 01, January-2015.
[24] Andrii Shalaginov, Lars Strande Grini, Katrin Franke (2016) "Understanding Neuro-Fuzzy on a class of multinomial malware detection problems, In Neural Networks (IJCNN)," 2016 International Joint Conference
[25] Huda, Shamsul, et al. "A hybrid feature selection with ensemble classification for imbalanced healthcare data: A case study for brain tumor diagnosis." IEEE Access 4 (2016): 9145-9154.
[26] Azad, S., Fattah, S. A., & Shahnaz, C. (2017, November). “An automatic scheme for brain tumor region detection from 3D MRI data based on enhanced intensity variation.” In Region 10 Conference, TENCON 2017-2017 IEEE (pp. 1-6). IEEE.
[27] Ramani RG, Sivaselvi K. Classification of “Pathological Magnetic Resonance Images of Brain Using Data Mining Techniques. In Recent Trends and Challenges in Computational Models (ICRTCCM),” 2017 Second International Conference on 2017 Feb 3 (pp. 77-82). IEEE.

Harmeet Kaur, Jasleen Kaur, "Survey on Data Mining Technique," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.915-920, 2018.

Road safety is one of the main objectives on designing the assistance system. Nowadays everyone needs to have a safer transport. Even if any accident happens, no one take cares about it. A large number of precious lives are lost due to road traffics accidents every day. There is need to have effective road accident detection and information communication system in place to injured persons. The proposed system helps to identify the accidents in remote areas with the help of smart phone. It uses a vibration sensor which detects the variation of the vibration system, and makes a message to transfer the details about the location of accident to contacts .So we can save the life of people.

GPS Technology, GSM, Vibration sensor, Microcontroller

[1] Accident detection and reporting system using GPS, GPRS and GSM technology-September 2013.
[2] “A Vehicle-to-Vehicle Communication Protocol for Cooperative Collision Warning” Xue Yang University of Illinois at Urbana-Champaign xueyang@uiuc.edu,Jie Liu Microsoft Research Liuj@microsoft.com,Feng Zhao Microsoft Research zhao@microsoft.com
[3] “Automatic Accident Alert and Safety System using Embedded GSM Interface” Kajal Nandaniya I&C Department DDIT, NadiadViraj Choksi Project Scientist BISAG, Ghandhinagar
[4] “Vehicle Accident Detecting and Alerting System” -Hemangi S. Ahire, Madhri B. Kamble, Prajakta K. Khade, Rohini A Ghare.

Rasmi M, Harishma C M, "An Intelligent Vehicular Accident Notification System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.921-924, 2018.

With the increasing dependency of most of the organizations on technology in today’s world, the aspect of information security is a major concern. A plethora of data, be it insignificant (like text messages) or vital (data of some government agency) is stored and transferred over the internet and is exposed to a variety of attacks by people with immoral intentions. Information security is therefore a vital aspect for any organization or government body which has to ensure that their data is not mutated by an attacker and reaches the intended recipient unchanged. This paper discusses various types of security models which are used in practice by the organizations and various cryptographic methods that are commonly used for the protection of data against undesirable breaches. A variety of algorithms have been developed in order to protect the data. Various types of keys which form an essential component of the encrypting algorithms are also discussed. There are several cyber-attacks which pose an austere threat to the organizations. These threats can be mitigated using various cryptographic algorithms and network protocols. These protocols also play an important role in mitigating the security breaches of sensitive data and are discussed towards the end of this paper.

Cyber Security, Cyber-attacks, Cypher Text, Cryptography, Phishing, Security Protocol

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[2] Steve G. Walkins, “An Introduction to Information Security and ISO27001:2013: A Pocket Guide”, IT Governance Publishing, pp. 21-42, 2013.
[3] Suganya V,"A Review on Phishing Attacks and Various Anti Phishing Techniques." International Journal of Computer Applications 139.1 (2016): 20-23.
[4] Amalraj, A. Joseph, and J. John Raybin Jose. "A survey paper on cryptography techniques." International Journal of Computer Science and Mobile Computing 5.8 (2016): 55-59.
[5] Cherdantseva, Yulia, and Jeremy Hilton. "A reference model of information assurance & security." 2013 International Conference on Availability, Reliability and Security. IEEE, 2013.
[6] Vipin Poddar, Hitesh Choudhary. "A Comparitive Analysis of Wireless Security Protocols (WEP and WPA2)”, International Journal of AdHoc Networking Systems, Vol. 4, No. 3, July 2014.
[7] Mahjabin, Tasnuva, Yang Xiao, Guang Sun, and Wangdong Jiang. "A survey of distributed denial-of-service attack, prevention, and mitigation techniques." International Journal of Distributed Sensor Networks 13, no. 12, 2017.
[8] Mishra, Neha, and Sunita Gond. "Defenses to protect against SQL injection attacks." International Journal of Advanced Research in Computer and Communication Engineering 2.10 (2013).
[9] Ayushi. “A Symmetric Key Cryptographic Algorithm”, International Journal of Computer Applications, Volume 1 – No. 15, 2010.
[10] Sanjay E. Pate, Bhojaraj H. Barhate, "A survey of Possible Attacks on Text & Graphical Password Authentication Techniques", International Journal of Scientific Research in Computer Science and Engineering, Vol.06, Issue.01, pp.77-80, 2018
[11] B. Bhasker, T. Jagadish kumar, M.V.Kamal, "A Security Determination-Reaction Architecture for Heterogeneous Distributed Network", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.5, pp.26-34, 2017
[12] Poonam Devi , "Attacks on Cloud Data: A Big Security Issue", International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.2, pp.15-18, 2018
[13] Shailja Sharma, "A Review of Vulnerabilities and Attacks in Mobile Ad-Hoc Network", International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.2, pp.66-69, 2018

Pranav Chaudhary, "Mitigating Cyber Attacks Through Security Models and Networking Protocols," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.925-929, 2018.