Volume-6 , Issue-3 , Mar - 2018 Go Back

In today’s world where everything relies on the networks, the data in transfer may be susceptible to outside attacks. And these attacks are vulnerable because the data is huge in size and critical or may be confidential in nature. Due to this it becomes the prime activity to protect the information and the system processing this huge amount of information from the unauthorized access and theft. And this makes the role of Intrusion detection system very important as this helps in the protection of Confidentiality and maintenance of the integrity and reliability of the information. A number of methods are present and being used to their limits for the protection. Data mining techniques are used for the purpose of pattern extraction and analysis of the attack patterns helps in developing better system for the network. After the review of a number of data mining algorithms for clustering, classifications and classification via clustering (CvC) the conclusion is that CvC algorithm shows the best performance in intrusion detection. In the review datasets like KDDcup 99, NSL_KDD, GureKDD and Kyoto 2006+ is discussed with their performance and results for analysis.

Intrusion, IDS, ID3, C4.5, Classification, Decision Tree, Clustering, Pruning, Classification via Clustering

[1] W. Pu, W. Jun-qing, “Intrusion Detection System with the Data Mining Technologies”, In the Proceedings of the 2011 IEEE International Conference on Communication Software and Networks (ICCSN), China, 2011, ISBN: 978-1-61284-486-2.
[2] S. K. Sahu, S. Sarangi, S. K. Jena, “A Detail Analysis on Intrusion Detection Datasets”, In the Proceedings of the 2014 IEEE International Advance Computing Conference (IACC), India, 2014, ISBN: 978-1-4799-2572-8.
[3] N. G. Relan D. R. Patil, “Implementation of Network Intrusion Detection System Using Variant of Decision Tree Algorithm”, In the Proceedings of the 2015 IEEE International Conference on Nascent Technologies in the Engineering Field (ICNTE),India, 2015, ISBN: 978-1-4799-7263-0.
[4] G. Kayacik, A. N. Zincir-Heywood, M. I. Heywood. “Selecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD 99 Intrusion Detection Datasets”, In the Proceedings of the 2005 IEEE Annual conference on privacy, security and trust, Canada, 2005.
[5] M. Kumar, M. Hanumanthappa, T. V. Suresh Kumar. “Intrusion Detection System Using Decision Tree Algorithm”, In the Proceedings of the 2012 IEEE International Conference on Communication Technology (ICCT), China, 2012, ISBN: 978-1-4673-2101-3.
[6] Wang, B. Chen, “Intrusion Detection System Based On Multi-Strategy Pruning Algorithm of the Decision Tree”, In the Proceedings of the 2013 IEEE International Conference on Grey Systems and Intelligent Services, China, 2013, ISBN: 978-1-4673-5248-2.
[7] N. M. Prajapati, A. Mishra, P. Bhanodia, “Literature Survey- IDS for Ddos Attacks”, In the Proceedings of the Conference on IT in Business, Industry and Government (CSIBIG), India, 2014, ISBN: 978-1-4799-3064-7.
[8] G. Zhai, C. Liu, “Research and Improvement on ID3 Algorithm in Intrusion Detection System”, In the Proceedings of the 2010 IEEE International Conference on Natural Computation (ICNC), China, 2010, ISBN: 978-1-4244-5961-2.
[9] Thakur, N. Markandaiah, D. S. Raj. “Re- Optimization of ID3 and C4. 5 Decision Tree”, In the Proceedings of the 2010 IEEE International Conference on Computer and Communication Technology (ICCCT), India, 2010, ISBN: 978-1-4244-9034-9.
[10] A.P. Muniyandi, R. Rajeswari, R. Rajaram, “Network Anomaly Detection by Cascading K-Means Clustering and C4. 5 Decision Tree Algorithm”, Procedia Engineering, Vol. 30, pp. 174-182, 2012.
[11] P. Aggarwal, S. K. Sharma, “An Empirical Comparison of Classifiers to Analyze Intrusion Detection”, In the Proceedings of the 2015 IEEE International Conference on Advanced Computing Communication Technologies (ACCT), India, 2015, ISBN: 978-1-4799-8488-6.
[12] Y. J. Zhao, M. J. Wei, J. Wang “Realization of Intrusion Detection System Based on the Improved Data Mining Technology”, In the Proceedings of the 2013 IEEE International Conference on Computer Science Education (ICCSE), Sri Lanka, 2013, ISBN: 978-1-4673-4463-0.
[13] K. S. Elekar, “Combination of data mining techniques for intrusion detection system”, In the Proceedings of the 2015 IEEE International Conference on Computer, Communication and Control. IEEE, India, 2015, ISBN: 978-1-4799-8164-9.
[14] S. Sahu, B. M. Mehtre, “Network Intrusion Detection System Using J48 Decision Tree”, In the Proceedings of the 2015 IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI), India, 2015, ISBN: 978-1-4799-8792-4.
[15] M. V. Kotpalliwar, R. Wajgi, “Classification of Attacks Using Support Vector Machine (SVM) on KDDCUP` 99 IDS Database”, In the Proceedings of the 2015 IEEE International Conference on Communication Systems and Network Technologies (CSNT), India, 2015, ISBN: 978-1-4799-1797-6.
[16] Marie Fernandes , "Data Mining: A Comparative Study of its Various Techniques and its Process", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.1, pp.19-23, 2017 .
[17] Kruegel, F. Valeur, G. Vigna, “Intrusion Detection and Correlation: Challenges and Solutions”, Springer Science and Business Media, Inc. Boston, 2005, ISBN: 978-0-387-23399-4.
[18] Z. Muda, W. Yassin, M. N. Sulaiman, N. I. Udzir, “Intrusion Detection Based on K Means Clustering and Naïve Bayes Classification”, In the Proceedings of the 2011 IEEE International Conference on Information Technology in Asia (CITA 11), Malaysia,pp.1-6, 2011, ISBN: 978-1-61284-130-4.
[19] U. Bashir, M. Chachoo, “Intrusion Detection and Prevention System: Challenges and Opportunities”, In the Proceedings of the 2014 IEEE International Conference on Computing for Sustainable Global Development (INDIACom), India, 2014, ISBN: 978-93-80544-12-0.
[20] M. Padmadas, N. Krishnan, J. Kanchana, M. Karthikeyan, “Layered Approach for Intrusion Detection Systems Based Genetic Algorithm”, In the Proceedings of the 2013 IEEE International Conference on Computational Intelligence and
Computing Research (ICCIC). India, 2013, ISBN: 978-1-4799-1597-2.
[21] M. Aslahi-Shahri, R. Rahmani, M. Chizari, A. Maralani, M. Eslami, M. J. Golkar, A. Ebrahimi, "A Hybrid Method Consisting of GA and SVM for Intrusion Detection System", Neural Computing and Applications, Vol. 27, 2016, p.p. 1669–1676.
[22] S. B. Kotsiantis, “Decision Trees: A Recent Overview”, Artificial Intelligence Review, Vol. 39, 2013, p.p. 261–283.
[23] S. Sathyamoorthy, "Data Mining and Information Security in Big Data", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.86-91, 2017.
[24] Zhang, M. Zulkernine, A. Haque, “Random-Forests-Based Network Intrusion Detection Systems”, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), Vol. 38, 2008.
[25] M. Tavallaee, E. Bagheri, W. Lu, A. A. Ghorbani , “A Detailed Analysis of the KDD CUP 99 Data Set”, IEEE Symposium on Computational Intelligence for Security and Defense Applications, Canada, 2009, ISSN: 2329-6267.
[26] R. Kumar, "A Review of Network Intrusion Detection System using Machine Learning Algorithms", International Journal of Computer Sciences and Engineering (IJCSE), Vol. 5, Issue-12, p.p. 94-100, 2017

Ramakant Soni, Pradeep Singh Shekhawat, "A Comparative Review on the Performance of Intrusion Detection Algorithms and Datasets in Networks Using Data Mining Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.327-332, 2018.

Due to rapid evolution of micro blog content on social media websites, internet has become a vital medium for a huge source of data. Internet has change the general perspective of socializing and finding the information regarding various (entities). Use of data from social networks for different purposes, such as election prediction, sentimental analysis, marketing, communication, business, and education, is increasing day by day. Due to overwhelming amount of user opinion, reviews, and suggestions available through the web platform, and it helps in analysing and taking better decisions. Micro blogging websites becomes a major source for the gauging the perspective of the user. In this paper, we are using the concept of opinion mining and analysing tweets to classify the data and extract the sentiments from it. Extraction of valuable information precisely from social media website and thus Several decisions can be made more efficiently using sentiments of individuals. Verified reviews need to be used for better accuracy. Proposed system is tested on the collection of real time data extracted from Twitter. The resultant opinion is represented in the form of graph and sentimento.

Opinion mining, sentiment analysis, twitter, natural language processing, sentimento.

[1] Pankaj Kumar , Kashika Manoche and Harshita Gupta “Enterprise Analysis Through Opinion Mining”, ICEOT, 2016
[2] Deepanshi Sharma, Achal Kulshreshtha and Priyanka Paygude “Tourview:Sentiment BasedAnalysis On Tourist Domain”, IJCSIT,vol 6 (3),2015
[3] Rabia Batool, Asad Masood Khattak , Jahanzeb Maqbool and Sungyoung Lee “Precise Tweet Classification And Sentiment
Analysis”, IEEE ,2013.
[4] Dharmesh Ramani and Hazari Prasun “A Survey : Sentiment Analysis of Online Review ”, IJARCSSC , Vol 4 ,Issue 11, November 2014
[5] Deepali Virmani, Vikrant Malhotra, Ridhi “Sentiment Analysis Using Collaborated Opinion Mining”, IJSCE,Vol 4, Issue ICCIN-
2k14,March 2014.
[6] Yogesh Dubey, Pranil Chaudhari,Shaldon Chaphya “Efficient Detection of Legitimate and Malicious URL’s Using ID3 Algorithm”,IJAIS , Vol 11,Number 11, March 2017.
[7] Tina D’abero ,Anand Khandare and Prachi Janrao “ A Novel Approach To Cluster Web Pages Dynamically Based On Domain Knowledge” IJAIS,Vol 11,2016
[8] Tina D’abero ,Anand Khandare and Prachi Janrao ” Static Clustering Of Web Pages For Relevant Recommandation”,IJARCCE,Vol 5,Issue 9, september2016.
[9] Kazutak shimada, Shunsuke Inoue, Hiroshi Madea and Psutomu Endo,”Analysing Tourism Information on Twitter For a Local City”,IEEE, 2011.

Y.K. Pitale, D.C. Salot , R.S. Mhatre, T. Dabreo, "Sentiment Analysis on Microblog Content," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.333-336, 2018.

The potential of the right brain like Intuition, Creativity, Sensitivity can be achieved by the generation of Alpha & delta waves in the human brain. It helps human beings to be efficient in their physical, mental & spiritual welfare & helps the human beings in day to day life to be healthy, lovable, intuitive, emotional balance, creative & make them a better decision maker. It is the technique of fine tuning the frequency of our thoughts to the most subtle frequency. This can be achieved by the change of the Neurological setup in the brain by making the neuron to fire & wire each other making them to communicating with each other. Change in the Neurological setup can be achieved by the practicing the Heartfulness meditation, exercises to increase the ability of brain to regenerate neurons, left and right brain synchronization exercises, exposure to Brain waves oscillation, music to enhance brain connections, eyeball exercises to enhance the neuronal circuits & by activities for sensory augmentation.

sensory augmentation, Delta Waves, Brain Functions

[1] Raja Amarnath ,Natwar Sharma and Subbulakshmi Balasubramanian “Improving Immunity through Heartfulness Meditation” Department of Pulmonary Medicine, Director-Journal of Recent Scientific Research 8, Issue 7, pp. 18874-18879, July, 2017.
[2] P. Gerjets, C. Walter, W. Rosenstiel, M. Bogdan, T. O. Zander, "Cognitive state monitoring and the design of adaptive instruction in digital environments: Lessons learned from cognitive workload assessment using a passive brain-computer interface approach", Frontiers in Neuroscience, vol. 8, pp. 1-21, Dec 2014.
[3] Huan, R. Palaniappan, "Neural network classification of autoregressive features from electroencephalogram signals for braincomputer interface design",Journal of Neural Engineering, vol. 1, pp. 142-150, 2004.
[4] Karmen Resnik, David Bradbury,Gareth R. Barnes, Magnetoencephalography Study of the Tip-of-the-Tongue”- Journal of Cognitive Neuroscience Volume: 26, Issue 10 Year: 2014.
[5] Mohan Kotwal “A Study of Meditation and Other Self Development Techniques for Effective Management” AIMS International Conference on Value-based Management ,Pune.August 11-13, 2010.
[6] Stein Joel, "Just Say OM -The Science of Meditation", Article and cover story in "TIME" International Magazine, August 4, 2003 issue.
[7] Vyakti Vikas Kendra, "Healing Breath Breathing Health" - Amazing Facts", a compilation of case studies of practitioners on reports of Health Improvements. The Art of Living Foundation, Bangalore, 1997.
[8] Patwardhan Dr. Bhushan, "Patanjali, Penfield Va Samadhi", Article in Sakal Daily, 17 Dec. 2003, Pune.
[9] Lyubimov,N.N.” Changes in ectro-encephalogram & evoked potentials during application of the specific form of physiological training (meditation)” Human Physiology, 25 (1), pp171-180,1999.
[10] W. Klimesch, "Alpha-band oscillations attention and controlled access to stored information", Trends in Cognitive Sciences, vol. 16, no. 12, pp. 606-617, 2012
¬[11] Z.A. Keirn, “Fort Collins Man-machine communications through brain-wave processing- Department” IEEE Engineering in Medicine and Biology Magazine Volume: 9, Issue: 1, March 1990.
[12] W. Klimesch, "EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis", Brain Research Reviews, vol. 29, no. 2–3, pp. 169-195, 1999.

Shobanbabu R.J, Raju, Anil Kumar Chengali, "Inducing Neuro-linguistic changes in human brain to improve the efficiency of human being," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.337-342, 2018.

Growing popularity of multimedia data give birth to a new field in sensor network, called wireless multimedia sensor network (WMSN) that can handle multimedia data along with the normal scalar data. Advances in CMOS (Complementary Metal Oxide Semiconductor), help sensor nodes to gather, process and transport multimedia data along with the textual data. But resource constraint nature of sensor network makes the implementation of multimedia sensor network very difficult and traditional approaches fail to deal with the consequences generated from transmitting multimedia data. Yet its popularity is increasing as it has potential civilian and military applications. Multimedia data has stringent Quality of Service requirement (QoS) such as delay, jitter, packet loss rate, energy etc. and to satisfy those criteria, routing protocols needs to modify. The critical problem to handle by the routing protocols are managing the energy while maintaining QoS requirement and handling the unreliable error prone communication medium. Here we have studied existing routing protocols used for multimedia data transmission and figure out some problems need to be addressed for future research.

Quality of Service(QoS), Hole Bypassing, Bio-inspired, Cross Layer

[1] J. Campbell, P.B. Gibbons, S. Nath, P. Pillai, S. Seshan, R. Sukthankar, “IrisNet: an Internet-scale architecture for multimedia sensors”, in the proceedings of the ACM Multimedia Conference, 2005.
[2] F. Hu, S. Kumar,“QoS considerations for wireless sensor networks in telemedicine”, in the proceedings of the Intl. Conf. on Internet Multimedia Management Systems, Orlando, FL, September 2003.
[3] Virtual Reality: http://www.realitytechnologies.com/virtual-reality
[4] R. Holman, J. Stanley, T. Ozkan-Haller, “Applying video sensor networks to nearshore environment monitoring”, IEEE Perv. Comput. 2 (4) (2003) 14–21.
[5] I. F. Akyildiz, T. M. (n.d.). “Wireless multimedia sensor networks: applications and testbeds”. Proceedings of the IEEE, vol. 96, no. 10, pp. 1588–1605, 2008.
[6] A. Mohammed, F. Norshiela, "Routing Protocols for wireless multimedia sensor network: A survey," Journal of Sensors, vol. 2013, pp. 1-11, 2013.
[7] L. Shu, Y. Zhang, L. T. Yang, Y. Wang, M. Hauswirth, and N. Xiong, “TPGF: geographic routing in wireless multimedia sensor networks,” Telecommunication Systems, vol. 44, no. 1-2, pp. 79–95, 2010.
[8] B. Karp and H. T. Kung, “GPSR: greedy perimeter stateless routing for wireless networks,” in Proceedings of the 6th Annual International Conference on Mobile Computing and Networking (MOBICOM ’00), pp. 243–254, August 2000.
[9] S. Medjiah, T. Ahmed, and F. Krief, “GEAMS: a geographic energy-aware multipath stream-based routing protocol for WMSNs,” in the Proceedings of the Global Information Infrastructure Symposium (GIIS ’09), pp. 1–8, Hammamet, Tunisia, June 2009.
[10] M. Chen, V. C. M. Leung, S. Mao, and Y. Yuan, “Directional geographical routing for real-time video communications in wireless sensor networks,” Computer Communications, vol. 30, no. 17, pp. 3368– 3383, 2007.
[11] I. Bennis, O. Zytoune, D. Aboutajdine, and H. Fouchal, “Low energy geographical routing protocol for wireless multimedia sensor networks,” in Proceedings of the 9th InternationalWireless Communications and Mobile Computing Conference (IWCMC ’13), pp. 585–589, July 2013.
[12] J. Wang, Y. Zhang, J. Wang, Y. Ma, and M. Chen, “PWDGR: pair-wise directional geographical routing based on wireless sensor network,” IEEE Internet of Things Journal, vol. 2, no. 1, pp. 14–22, 2015.
[13] Manju Bhardwaj, "Faulty Link Detection in Cluster based Energy Efficient Wireless Sensor Networks", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.1-8, 2017.
[14] C. E. Perkins and E. M. Royer, “Ad-hoc on-demand distance vector routing,” in Proceedings of the 2nd IEEE Workshop on Mobile Computing Systems and Applications (WMCSA ’99), pp. 90–100, New Orleans, La, USA, February 1999.
[15] A. Nayyar, F. Bashir, and Ubaid-Ur-Rehman, “Load based energy aware multimedia routing protocol-(LEAR),” in Proceedings of the 3rd International Conference on Computer Research and Development (ICCRD ’11), vol. 2, pp. 427–430, Shanghai, China, March 2011.
[16] Y. Sun, H. Ma, L. Liu, and Y. Zheng, “ASAR: an ant-based service-aware routing algorithm for multimedia sensor networks,” Frontiers of Electrical and Electronic Engineering in China, vol. 3, no. 1, pp. 25–33, 2008.
[17] C. Intanagonwiwat, R. Govindan, D. Estrin, J. Heidemann, and F. Silva, “Directed diffusion for wireless sensor networking,” IEEE/ACM Transactions on Networking, vol. 11, no. 1, pp. 2–16, 2003.
[18] A. Rahman, R. Ghasemaghaeil, A. El Saddikl, and W. Gueaieb, “M-IAR: biologically inspired routing protocol for wirelessmultimedia sensor networks,” in Proceedings of the Instrumentation and Measurement Technology Conference (IMTC ’08), pp. 1823–1827, Victoria, Canada, May 2008.
[19] R.G.Aghaei,M. A. Rahman,W.Gueaieb, andA. El Saddik, “Ant colony-based reinforcement learning algorithm for routing in wireless sensor networks,” in Proceedings of the IEEE Instrumentation & Measurement Technology Conference (IMTC ’07), 6, 1pages, IEEE,Warsaw, Poland, May 2007.
[20] J. Bi, Z. Li, and R. Wang, “An ant colony optimizationbased load balancing routing algorithm for Wireless multimedia sensor networks,” in Proceedings of the IEEE 12th International Conference on Communication Technology (ICCT ’10), pp. 584– 587, Nanjing, China, November 2010.
[21] L.Cobo, A.Quintero, andS.Pierre, “Ant-based routing forwireless multimedia sensor networks using multiple QoS metrics,” Computer Networks, vol. 54, no. 17, pp. 2991–3010, 2010.
[22] A. Nayyar, F. Bashir, Ubaid-Ur-Rehman, and Z. Hamid, “Intelligent routing protocol for multimedia sensor networks,” in Proceedings of the 5th International Conference on Information Technology and Multimedia (ICIM ’11), pp. 1–6, Kuala Lumpur,Malaysia, November 2011.
[23] S. Kumar, M.Dave, and S.Dahiya, “ACObasedQoS aware routing for wireless sensor networks with heterogeneous nodes,” in Emerging Trends in Computing and Communication, vol. 298 of Lecture Notes in Electrical Engineering, pp. 157–168, Springer, Berlin, Germany, 2014.
[24] Z. Ke, L. Li, Q. Sun, and N. Chen, “Ant-like game routing algorithm for wireless multimedia sensor networks,” in Proceedings of the 4th International Conference onWireless Communications, Networking and Mobile Computing (WiCOM ’08), October 2008.
[25] Sooraj Surendran, Gnanaguru G, "Intelligent Reverse Sensor for Electric Wheelchair", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.51-53, 2017.
[26] K. Kotecha and S. Popat, “Multi objective genetic algorithm based adaptive QoS routing in MANET,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC ’07), pp.1423–1428, Singapore, September 2007.
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[29] D. B. Johnson and D. A. Maltz, “Dynamic source routing in ad hoc wireless networks,” in Mobile Computing, T. Imielinski and H. F. Korth, Eds., vol. 353 ofThe Kluwer International Series in Engineering and Computer Science, pp. 153–181, 1996.
[30] T. H. T. He, J. A. Stankovic, C. L. C. Lu, and T. Abdelzaher, “SPEED: a stateless protocol for real-time communication in sensor networks,” in Proceedings of the 23th IEEE International Conference on Distributed Computing Systems, pp. 46–55, IEEE, May 2003.
[31] K. Akkaya and M. Younis, “An energy-aware QoS routing protocol forwireless sensor networks,” In Proceedings of the 23rd International Conference on Distributed Computing Systems Workshops (ICDCSW ’03), pp. 710–715, Providence, RI, USA, May 2003.
[32] O. Chipara, Z. He, G. Xing et al., “Real-time power-aware routing in sensor networks,” in Proceedings of the 14th IEEE International Workshop on Quality of Service (IWQoS ’06), pp. 83–92, New Haven, Conn, USA, June 2006.
[33] S. Medjiah, T. Ahmed, and F. Krief, “AGEM: adaptive greedy-compass energy-aware multipath routing protocol for WMSNs,” in Proceedings of the 7th IEEE Consumer Communications and Networking Conference (CCNC ’10), pp. 1–6, IEEE, January 2010.
[34] J. Ben-Othman and B. Yahya, “Energy efficient and QoS based routing protocol for wireless sensor networks,” Journal of Parallel and Distributed Computing, vol. 70, no. 8, pp. 849–857, 2010.
[35] M. A. Razzaque, M. M. Alam, M. Mamun-Or-rashid, and C. S. Hong, “Multi-constrained QoS geographic routing for heterogeneous traffic in sensor networks,” IEICE Transactions on Communications, vol. 91, no. 8, pp. 2589–2601, 2008.
[36] A. A. Ahmed and N. Fisal, “A real-time routing protocol with load distribution in wireless sensor networks,” Computer Communications, vol. 31, no. 14, pp. 3190–3203, 2008.
[37] H. Shen, G. Bai, Z. Tang, and L. Zhao, “QMOR: QoS-aware multi-sink opportunistic routing for wireless multimedia sensor networks,”Wireless Personal Communications, vol. 75, no. 2, pp. 1307–1330, 2014.
[38] X. Yan, L. Li, and F. J. An, “Multi-constrained routing in wireless multimedia sensor networks,” in Proceedings of the International Conference on Wireless Communications & Signal Processing (WCSP ’09), pp. 1–5, IEEE, November 2009.
[39] E. Felemban, C.-G. Lee, and E. Ekici, “MMSPEED: multipath multi-SPEED protocol for QoS guarantee of reliability and timeliness in wireless sensor networks,” IEEE Transactions on Mobile Computing, vol. 5, no. 6, pp. 738–753, 2006.
[40] G. Han et al., “Cross-layer Optimized Routing in WSN with Duty-Cycle and Energy Harvesting,” Wireless Commun. and Mobile Computing, published online, 3 Feb. 2014, DOI: 10.1002/wcm.2468.
[41] I.T. Almalkawi, M.G. Zapata, and J. N. Al-Karaki, “A Secure cluster based Multipath Routing Protocol for WMSNs”; Sensors, 2011, pp.4401–4424.
[42] I.T. Almalkawi, M.G. Zapata, and J. N. Al-Karaki, “A Cross-Layer-Based Clustered Multipath Routing with QoS Aware Scheduling for Wireless Multimedia Sensor Networks.” Int’l. J. Distrib. Sensor Networks, 2012, pp. 1–12.
[43] A.Ajina, K. Nair, “A Study of QoS algorithm for Wireless
Multimedia Sensor Networks”, International Journal of Computer Science Engineering (IJCSE), Vol. 4 No.04 Jul 2015

Moumita Deb, Abantika Choudhury, "A Review of Wireless Multimedia Sensor Network and existing Routing Protocols," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.343-358, 2018.

As we know in every era, we need knowledge for performing the efficient job. That knowledge comes from past experiences & information, if we don’t have proper storage for information then that information will go in vain after few days. Today we have a large amount of data for storing, for which we used a different type of devices. Based on our need we have developed a different type of technologies to store the data such as Cds, DVDs, Floppy Disk, Hard Disks, Flash drive. As we know that technology is moving towards IoT and experts believe that IoT will consist 30 billion devices by 2020. So the physical world has more direct involvement in the computer world. Now our devices become intelligent systems that can share and analyze data. This analyzed data or information will change our business and daily life. Big possibilities come for analyzing the data across the system. For this, we need a better storage so that we can store a large amount of data easily & retrieve the data without any error. So in this paper we have discussed the technique for converting the information in 2.14*10^6 bytes in DNA oligos.

DNA,IOT

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[7] M. Young, The Technical Writer’s Handbook. Mill Valley, CA: University Science, 1989.

A. Saxena, S. Sharma, S. Dangi, A. Sharma, C. Patel, "New Trends in Digital Data Storage for the Internet of Things," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.359-363, 2018.

In today’s world security has become a major threat over the transmission channel. To overcome this DNA cryptography is used to encrypt and transfer the message from sender to receiver over a secured communication network. This paper focuses on the encryption and decryption of the message using DNA encoded sequences and discusses the cryptographic applications of single qubit rotations from the view of one-way trapdoor functions. For encryption quantum public key is used and for decryption the concept of classical private key is used. The mapping between integer numbers and quantum states is done using one way trapdoor function.

Quantum Cryptography, DNA Cryptography, One-way trapdoor function, Qubit

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[5] Abdulrahman Aldhaheri, Khaled Elleithy, Majid Alshammari, Hussam, “A Novel Secure Quantum Key Distribution Algorithm”, University of Bridgeport, 2014.
[6] Ammar Odeh, Khaled Elleithy, Muneer Alshowkan, Eman Abdelfattah, “Quantum Key Distribution by Using Public Key Algorithm (RSA)”, London, United Kingdom: third International Conference on Innovative Computing Technology (INTECH),IEEE, August 2013.
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[13] Pramanik Sabari and Kumar Sanjit Setua, “DNA cryptography”, Electrical & Computer Engineering (ICECE), 7th IEEE International Conference, pp.551-554, 2012.
[14] Yunpeng Zhang, Bochen Fu, and Xianwei Zhang, “DNA cryptography based on DNA Fragment assembly “, Information Science and Digital Content Technology (ICIDT), 8th IEEE International Conference, Vol.1, pp.179-182, 2012.
[15] Olga Tornea, and Borda E. Monica, “Security And Complexity Of A DNA-Based Cipher”, Roedunet International Conference (Ro Edu Net), 11th IEEE International Conference,pp.1-5, 2013 .
[16] Ashish Kumar Kaundal, A.K Verma,”Extending Feistel structure to DNA Cryptography”, Journal of Discrete Mathematical Sciences and Cryptography Volume 18, Issue 4, pp.349-362, 2015.
[17] Kazuo Tanaka, Akimitsu Okamoto, and Isao Saito, “Public-Key System Using DNA as a One-Way Function for Key Distribution”, Biosystems 81, pp.25-29, 2005.
[18] A. Menezes, P. Van Oorschot and S. Vanstone, “Handbook of Applied Cryptography”, CRC Press, 1996.
[19] M. A. Nielsen and I. L. Chuang, “Quantum Computation and Quantum Information”, Cambridge University Press, Cambridge, London, 2000.
[20] C.-H. F. Fung, K. Tamaki, and H.-K. Lo, "Performance of two quantum key- distribution Protocols," Phys. Rev. A vol. 73, 2006.
[21] Simmon, G. J., “Symmetric and Asymmetric Encryption”, ACM Computing Surveys, 11(4), pp. 305-330, 1979.
[22] W. K. Wooters and W. H. Zurek, “A single quantum cannot be cloned”, Nature 299, 802, 1982
[23] Young, A., “The future of cryptography: Practice and theory”, IEEE IT Professional Journal, pp. 62-64, 2003.
[24] Vishnu Teja, Payel Banerjee, N. N. Sharma and R. K. Mittal, “Quantum Cryptography: State-of-Art, Challenges and Future Perspectives”. 7th IEEE International Conference on Nanotechnology, pp. 1296-1301, 2007.
[25] C. Elliott, D. Pearson and G. Troxel, “Quantum Cryptography in Practice”, Preprint of SIGCOMM 2003.
[26] Mehrdad S. Sharbaf, “Quantum Cryptography: An Emerging Technplogy in Network Security”. IEEE, 2011.
[27] Partha Sarathi Goswami, Prasun Chakrabarti “Approach towards realizing resource mining and secured information transfer”, International Journal of Computer Science and Network Security, pp. 345-350 Vol. 8, No. 7, 2008.
[28] Partha Sarathi Goswami, Pabitra Kumar Dey, Dr. A.C. Mandal, “Quantum Cryptography: Security through Uncertainty”, National Conference on Computing & Systems 2010, pp 86-89, 2010.
[29] Partha Sarathi Goswami, Tamal Chakraborty, Sourav Saha, “Cryptographic Scheme using the Biological Properties of DNA-RNA - A Review”, American Journal of Advanced Computing, Vancouver, Canada, Vol III(2), pp.61-65, 2016. ISSN: 2368-1209129.

Partha Sarathi Goswami, Tamal Chakraborty, Harekrishna Chatterjee, "A Novel Encryption Technique Using DNA Encoding and Single Qubit Rotations," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.364-369, 2018.

Software Testing is the process of testing the software in order to ensure that it is free of errors and produces the desired outputs in any given situation. Properly generated test suites may not only locate the defects in software systems, but also help in reducing the high cost associated with software testing. Model based software testing is an approach in which software is viewed as a set of states. There are a number of models of software in use today, a few of which make good models for testing. This paper introduces model-based testing and discusses its tasks in general terms with finite state models. Ant colony optimization (ACO) is best suited to model based software testing like finite state machines, state charts, the unified modeling language (UML) and Markov chains.

Ant Colony, Optimization, Model Based Software Testing, Optimal Path, State Machine

[1] Huaizhong LI, C. Peng LAM, “An Ant Colony Optimization Approach To Test Sequence Generation For State Based Software Testing”, Fifth International Conference on Quality Software (QSIC’05), pp. 255-264, 2005.
[2] Dan Liu,Xuejun Wang, Jianmin Wang, “Automatic Test Case Generation Based On Genetic Algorithm”, Journal of Theoretical and Applied Information Technology, Vol. 48 No. 1, pp. 411-416, 2013.
[3] Praveen Ranjan Srivastava1, Nitin Jose, Saudagar Barade, Debopriyo Ghosh, “Optimized Test Sequence Generation From Usage Models Using Ant Colony Optimization”, IJSEA, Vol.1, No.2, pp. 14-28, 2010.
[4] Navneet Kaur, Jaspreet Singh Budwal, “Hybrid Approach to Retrieval of Reusable Component from a Repository Using Genetic Algorithms and Ant Colony”, International Conference on Genetic and Evolutionary Method, Las Vegas, Nevada, USA , pp.147-152, 2008.
[5] Rafael S. Parpinelli1, Heitor S. Lopes1, And Alex A. Freitas2, “Data Mining With An Ant Colony Optimization Algorithm”, IEEE Transactions on Evolutionary Computation”, Vol. 6, Issue: 4, pp. 321 – 332, 2002.
[6] Praveen Ranjan Srivastava1 and Tai-hoon Kim, “Application of Genetic Algorithm in Software Testing”, International Journal of Software Engineering and Its Applications, Vol. 3, No.4, pp. 87-96, October 2009.
[7] Navneet Kaur, Jaspreet Singh Budwal ,“Intelligent Web Search Optimization with reference to Mutation Operator of Genetic and Cultural Algorithms Framework”, 2014 IEEE International Conference on Advanced Communication, Control and Computing Technologies (ICACCCT), pp. 619-623, 2014.
[8] Vittorio Maniezzo, Luca Maria Gambardella, Fabio de Luigi, “Ant Colony Optimization”, Studies in Fuzziness and Soft Computing book series STUDFUZZ, Vol 141, pp 101-121.
[9] Dorigo, M., Di Caro, G. & Gambardella, L. M. “Ant algorithms for
discrete optimization”. Artificial Life Vol 5, No. 2, 137-172, 1999.
[10] Huaizhong Li and C. Peng Lam, “Software Test Data Generation using Ant Colony Optimization”, International Journal of Computer, Information Science and Engineering Vol:1 No:1, pp 126-129, 2007.
[11] M. Dorigo, A. Colorni and V. Maniezzo, “The Ant System: optimization
by a colony of cooperating agents,” IEEE Transactions on Systems,
Man, and Cybernetics-Part B, vol. 26, No. 1, pp. 29-41, 1996.
[12] L.M Gambardella and M. Dorigo M, “Solving Symmetric and Asymmetric TSPs by Ant Colonies”, Proceedings of the IEEE Conference on Evolutionary Computation, ICEC96, Nagoya, Japan, May 20-22, pp. 622-627, 1996.
[13] Ahmed S. Ghiduk, “A New Software Data-Flow Testing Approach via
Ant Colony Algorithms”, Universal Journal of Computer Science and
Engineering Technology, pp 64-72, 2010.
[14] Neha Pahwa, Kamna Solanki, “UML based Test Case Generation
Methods: A Review”, International Journal of Computer Applications,
Vol 95– No.20, pp 1-6, 2014.

Navneet Kaur, Jaskaranjit Kaur, J.S.Budwal, "Application of ACO in Model Based Software Testing: A Review," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.370-374, 2018.

In this paper, we consider dynamic scheduling algorithm with task execution time estimation method in cloud which aims to satisfy the workflow deadline by consuming the figure of job performance time prospect and typical nonconformity to approximation real task finishing times. Existing workflow scheduling algorithms in the grid and cloud background concentrated on a number of QoS parameters such as cost, CPU time, makespan and dependability etc. An Effectual Load Balancing based on Resource Utilization is proposed and related algorithm is executed on CloudSim and its toolkit. The results show the effectiveness and decrease the renting cost of the proposed algorithm. The proposed algorithm improves efficiency and response time compared to delay based dynamic scheduling.

Cloud computing, scheduling algorithms, workflow scheduling algorithm.

[1] P. Mell, and T. Grance, “The NIST Definition of Cloud computing,” National Institute of Standards and Technology, 2009.
[2] Y. Jadeja and K. Modi, “Cloud Computing - Concepts, Architecture and Challenges”, in IEEE proc. of International Conference on Computing, Electronics and Electrical Technologies (ICCEET), pp. 877-880, 2012.
[3] HE Xiaoshan, Xian-He Sun, Gregor von Laszewski2 “QoS Guided Min-Min Heuristic for Grid Task Scheduling”( 2003).
[4] Yu, J., Buyya, R. and Tham, C.K. ‘‘Cost-based scheduling of scientific workflow applications on utility grids’’, First Int’l Conference on e-Science and Grid Computing, Melbourne, Australia, pp. 140–147 (2005).
[5] Sakellariou, R., Zhao, H., Tsiakkouri, E. and Dikaiakos, M.D. ‘‘Scheduling workflows with budget constraints’’, In Integrated Research in GRID Computing, S. Gorlatch and M. Danelutto, Eds Springer- Verlag., pp. 189–202, (2007).
[6] Kobra Etminani, M.Naghibzadeh“A Min-Min Max-Min selective algorihtm for grid task scheduling” in IEEE 3rd International Conference on Computing, Electronics and Electrical Technologies (ICCEET) in central asia ( 2007).
[7] Y. Yang, K. Liu, J. Chen, X. Liu, D. Yuan and H. Jin, An Algorithm in SwinDeW-C for Scheduling Transaction-Intensive Cost-Constrained Cloud Workflows, Proc. of 4th IEEE International Conference on e-Science, 374-375, Indianapolis, USA, December 2008.
[8] M. Xu, L. Cui, H. Wang, Y. Bi, “A multiple QoS constrained scheduling strategy of multiple workflows for cloud computing,” IEEE international symposium on parallel and distributed processing with applications, pp. 629-634, 2009
[9] K. Liu, Y. Yang, J. Chen, X. Liu, D. Yuan and H. Jin, “A Compromised-Time- Cost Scheduling Algorithm in SwinDeW-C for Instance-intensive Cost-Constrained Workflows on Cloud Computing Platform”, International Journal of High Performance Computing Applications, vol.24 no.4 445-456,May,2010.
[10] Z. Wu, X. Liu, Z. Ni, D. Yuan and Y. Yang, “A Market Oriented Hierarchical Scheduling Strategy in Cloud Workflow Systems,” The Journal of Super Computing, vol. 63, no. 1, pp. 256-293, Springer US(2011)
[11] Ghanbari, Shamsollah, and Mohamed Othman. "A priority based job scheduling algorithm in cloud computing." Procedia Engineering 50 (2012): 778-785
[12] Behzad, Shahram, Reza Fotohi, and Mehdi Effatparvar. "Queue based Job Scheduling algorithm for Cloud computing." International Research Journal of Applied and Basic Sciences ISSN (2013): 3785-3790.
[13] Agarwal, Dr, and Saloni Jain. "Efficient optimal algorithm of task scheduling in cloud computing environment." arXiv preprint arXiv:1404.2076 (2014).
[14] Theng, D., "Efficient Heterogeneous Computational Strategy For Cross-Cloud Computing Environment" Emerging Research in Computing, Information, Communication and Applications (ERCICA), 2014 Second International Conference on, vol., no., pp.8,17, 1-2 August 2014
[15] Aujla, Sumandeep, and Amandeep Ummat. "Task scheduling in Cloud Using Hybrid Cuckoo Algorithm." International Journal of Computer Networks and Applications (IJCNA) 2.3: 144-15(2015).
[16] Zhicheng Cai, Xiaoping Li, Rubén Ruiz, Qianmu Li “A delay-based dynamic scheduling algorithm for bag-of-task workflows with stochastic task execution times in clouds” Future Generation Computer Systems vol 71, pp. 57–72 (2017)
[17] D. Amalarethinam and F. Selvi, “A minimum makespan grid workflow scheduling algorithm,” In International Conference on Computer Communication and Informatics (ICCCI), pp. 1-6, IEEE, 2012.
[18] R. N. Calheiros, R. Ranjan, R. Buyya, et al. “Cloudsim: a novel framework for modeling and simulation of cloud computing infrastructures and services”, pp. 1-9, 2009.

Babita Rani Radwal, Sanjay Kumar, "Dynamic Scheduling Algorithm With Task Execution Time Estimation Method In Cloud," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.375-379, 2018.

Key management in cryptosystem has more security concerns. In traditional cryptosystem key is generated randomly and very difficult to remember. The keys generated from biometric features provide better option than traditional cryptographic key management techniques such as password based key generations. Cancelable biometric is a customized technique in biometric based cryptography, where Cancelable Biometric refers to the intentional and systematically repeatable distortion of biometric features in order to protect sensitive user-specific data. This paper presents the survey conducted for same of the cancelable biometric key generation techniques.

Cryptographic Key Generation, Biometrics, Feature Extraction , Key Generation, Biometric cryptography

[1] 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.
[2]Colin Soutar, Danny Roberge, Alex Stoianov, Rene Gilroy, and B.V.K. Vijaya Kumar,” Biometric Encryption” McGraw-Hill, (1999)
[3] Indu Verma, Sanjay Jain ,”Biometric based Key-Generation System for Multimedia Data Security”IEEE,2016
[4] 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.
[5]https://researcher.watson.ibm.com/researcher/view_group_subpage.php?id=1914
[6]http://www.scholarpedia.org/article/Cancelable_biometrics
[7]Archana C. Lomte,”Biometric Fingerprint Authentication with Minutiae using Ridge Feature Extraction” International Conference on Pervasive Computing (ICPC) IEEE,2015
[8]Subhas Barman,Debasis Samanta, Samiran chattopadhyay “Revocable Key Generation From Irrevocable Biometric Data for Symmetric Cryptography”IEEE,2015
[9] Gaurangkumar Panchal, Debasis Samanta “Comparable Features and Same Cryptography Key Generation using Biometric Fingerprint Image ” International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB16) IEEE,2016
[10] Aditi Bhatega,Kapil Sharma,”Secure Cancelable Fingerprint key Generation”IEEE,2014
[11]Padma Polash Paul,Marina Gavrilova” Multimodal Cancelable Biometrics” Int. Conf. on Cognitive Informatics & Cognitive Computing (ICCI*CC’12),IEEE,2012

K.N. Joshi, P. Chaudhari, "A Survey of Cancelable Biometric Based Key Generation Scheme using various Cryptography Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.380-383, 2018.

Cloud computing is a term, which involves virtualization, distributed computing, networking, software and web services. Cloud computing changes the paradigm of computing by providing computing as service. To provide scalable services to a user, load balancing a key requirement for customer satisfaction and proper work management. This paper proposes an effective load balancing algorithm using various parameters to distribute the load efficiently among various processors enabling better resource utilization and system response time. This proposed method assigns priority to the server and balance the load to servers according to propriety and also considering the waiting time. This proposed method almost guarantees the maximum throughput in minimum response time, and thus the user has to wait for minimum amount of time to get the job done.

Cloud Computing, Virtualization, distributed computing, load balancing

[1] The NIST Definition of Cloud Computing , Peter Mell Timothy Grance, NIST Special Publication 800-145
[2] A Comparative Study of Load Balancing Algorithms in Cloud Computing Environment Mayanka Katyal, Atul Mishra http://www.publishingindia.com.
[3] R.Piplode, P. Sharma and U.K. Singh, "Study of Threats, Risk and Challenges in Cloud Computing", International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.1, pp.26-30, 2013.
[4] ENHANCED EQUALLY DISTRIBUTED LOAD BALANCING ALGORITHM FOR CLOUD COMPUTING, Shreyas Mulay, Sanjay Jain, IJRET: International Journal of Research in Engineering and Technology ISSN: 2319-1163.
[5] A. M. Nakai, E. Madeira, and L. E. Buzato, “Load Balancing for Internet Distributed Services Using Limited Redirection Rates”, 5th IEEE Latin-American Symposium on Dependable Computing (LADC), 2011, pages 156-165.
[6] J. Hu, J. Gu, G. Sun, and T. Zhao, “A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud ComputingEnvironment”, Third International Symposium on Parallel Architectures, Algorithms and Programming (PAAP), 2010, pages 89-96
[7] A. Bhadani, and S. Chaudhary, “Performance evaluation of web servers using central load balancing policy over virtual machines oncloud”, Proceedings of the Third Annual ACM Bangalore Conference (COMPUTE), January 2010.
[8]Y. Fang, F. Wang, and J. Ge, “A Task Scheduling Algorithm Based on Load Balancing in Cloud Computing”, Web InformationSystems and Mining, Lecture Notes in Computer Science, Vol. 6318, 2010, pages 271-277.
[9] Anitha H M, P. Jayarekha , "Security Challenges of Virtualization in Cloud Environment", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.1, pp.37-43, 201
[11] Shridhar G.Domanal and G.Ram Mohana Reddy, “Load Balancing in Cloud Computing Using Modified Throttled Algorithm”, IEEE Randles, M.Bendiab, A. T. & Lamb, D. (2008).
[12] Agarwal, M., Srivastava, D.: A Genetic Algorithm inspired task scheduling in Cloud Computing. In : International Conference on Computing, Communication and Automation (ICCCA2016) (2016)
[13] Applied Soft Computing (2013) Ramezani, F., Lu, j., Hussain, F.: Task-Based System Load Balancing in Cloud Computing Using Particle Swarm Optimization. International journal of Parallel Programming (2014)
[14] Dasgupta, K., Mandal, B., Dutta, P., Mondal, J., Dam, S.: A Genetic Algorithm (GA) based Load Balancing Strategy for Cloud Computing. First International Conference on Computational Intelligence: Modeling Techniques and Applications (CIMTA) 2013 (2013)
[15] Patel, R., Patel, S., Patel, D., Patel, T.: Improved GA Using Population Reduction Load Bancing in Cloud Computing. 2016 Intl. Conference on Advances in Computing, Communications and Informatics (ICACCI) (2016)
[16] Bei, W., Jun, L.: Load balancing Task Scheduling based on multi Population Genetic Algorithm in cloud computing. In : 35th Chinese control Conference, China (2016) .
[17] Sandeep kaur and Sujhwinder Sharma Load Balancing in Cloud Computing with Enhanced Optimal Cost Scheduling Algorithm. Imperial Journal of Interdisciplinary Research (IJIR) (2016)
[18]B. Mondal,., K. Dasgupta, P. Dutta, P.: Load Balancig in Cloud Computing using Stochastic Hill Climbing-A soft Computing Appproach. ELEVIER (2012)
[19] Ariharan, V., Manakattu, S.: Neighbour Aware Random Sampling (NARS) algorithm for load balancing in Cloud computing. 2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT) (2015)
[20] Vanithaa, M., Marikkannu, P.: Effective resource utilization in cloud environment through a dynamic well Organised load balancing algorithm for virtual machines. Computers and Electrical Engineering (2017)
[21] Ekta Rani , Harpreet Kaur Efficient Load Balancing Task Scheduling in Cloud Computing using Raven Roosting Optimization Algorithm International Journal of Advanced Research in Computer Science Volume 8, No. 5, May-June 2017.
[22] Annwesha Banerjee Majumder Dipak Kumar Shaw and Sourav Majumder “ A Load Balancing Algorithm for Selection of Competent Server in Cloud Environment Based on Capacity, Load and Energy” Indian Journal of Computer Science and Engineering (IJCSE) Vol. 8 No. 4 Aug-Sep 2017

A.B. Majumder, S. Sil, S. Das, A. Mondal, "Priority Based Least Waiting Time Load Balancing Algorithm Applied in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.384-388, 2018.