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Wireless Sensor network has no central controller. Energy consumption is the major issue of wireless sensor network. In this paper, we have discussed routing protocol which utilized more energy. The energy must be quantized for computational purposes. Giving greater probability to nodes with higher energy, to be selected as CH, helps in better distribution of energy and more reliable message transmission. In this paper, diffusion based technique is used to synchronized cluster head clock. By doing so, energy consumption has been reduced in terms of energy, packet loss and delay.

Cluster head, RFID, Diffusion, Time lay

[1] T. Shah, N. Javaid, T. N. Qureshi, “Energy Efficient Sleep Awake Aware (EESAA) Intelligent Sensor Network Routing Protocol”, 15th IEEE International Multi Topic Conference, 2012.
[2] Charanpreet Kaur and Amit Chhabra, "An Energy Efficient Multihop Routing Protocol for Wireless Sensor Networks", International Journal of Computer Sciences and Engineering, Volume-03, Issue-07, Page No (86-91), Jul -2015
[3] M. Aslam, T. Shah, N. Javaid, A. Rahim, Z. Rahman, Z. A. Khan, “CEEC: Centralized Energy Efficient Clustering A New Routing Protocol for WSNs”, 15th International Conference on Computer Modelling and Simulation, IEEE, July 2012.
[4] S. Boulfekhar , M. Benmohammed, “A Novel Energy Efficient and Lifetime Maximization Routing Protocol in Wireless Sensor Networks”, Wireless Personal Communications, 10 March 2013.
[5] Jagmeet Singh and Harpreet Kaur, "Performance of Particle Swarm Optimization for Sensor Networks: A Survey", International Journal of Computer Sciences and Engineering, Volume-03, Issue-03, Page No (83-87), Mar -2015
[6] D. Waltenegus and P. Chritian; “Motivation for a Network of Wireless Sensor Nodes”, Fundamentals of Wireless Sensor Networks, 1st edition, John Wiley and Sons, Ltd, England, pp. 3-9, 2010.
[7] K. Hoglar and W. Andreas, “Applications and Challenges of Wireless Sensor Networks”, Protocols and Architecture for Wireless Sensor Networks, 1st edition, John Wiley and Sons, Ltd, England, pp. 6-9, 2005.
[8] F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirici, A survey on sensor networks, IEEE communications magazine 40 (8) (2002) 102–114.
[9] S.Ranjitha and D. Prabakar and S. Karthik, "A Study on Security issues in Wireless Sensor Networks", International Journal of Computer Sciences and Engineering, Volume-03, Issue-09, Page No (50-53), Sep -2015
[10] Jasmeet Kaur and Sukhwinder Sharma, "Genetic Algorithm based Stable Election Protocol for WSN", International Journal of Computer Sciences and Engineering, Volume-03, Issue-10, Page No (6-10), Oct -2015
[11] P. Saini, A. K. Sharma, “E-DEEC- Enhanced Distributed Energy Efficient Clustering Scheme for heterogeneous WSN”, International Conference on Parallel, Distributed and Grid Computing, 2010.
[12] T. N. Qureshi, N. Javaid, A. H. Khan, A. Iqbal, E. Akhtar, M. Ishfaq, “BEENISH: Balanced Energy Efficient Network Integrated Super Heterogeneous Protocol for Wireless Sensor Networks”, ELSEVIER, Procedia Computer Science 19 (2013), 920 – 925
[13] J. N. Al-Karaki, A. E. Kamal, “Routing Techniques in Wireless Sensor Networks: A Survey”, IEEE Wireless Communication, Vol. 11, pp. 6-28, December 2004.
[14] J. Yick, B. Mukherjee, D. Ghosal, “Wireless sensor network survey”, Department of Computer Science,2010.
[15] Neha Gupta and Balraj S. Sidhu “Cost Based EnergEfficient Routing Algorithm for Wireless Body Area Networks”, IJCSE, Vol.3, Issue-8, Aug-2015, pp. 1-5.

Iqbaljeet and Sonal Rana, "A Novel Technique for Clock Synchronization to Increase Network Lifetime in WSN," International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.106-110, 2016.

The digital signature system is more powerful than the authentication systems, because the digital signatures support both authentication and non-repudiation. Non-repudiation is necessary for the applications like electronic commerce, digital cash, banking applications, electronic voting etc. Here an efficient scheme based on RSA and arbitrary – precision numbers is discussed which is very secure. Also a secret sharing scheme is discussed which is based on visual cryptography. If the private key of users are compromised then intruder can use it illegitimately for signing purpose. So here the ways has been proposed to increase security of digital signatures by using combination of public key cryptography and secret sharing technique.

Digital Signatures; Arbitrary precision numbers; Secret sharing scheme using visual cryptography

[1] ITU-T Recommendation X.509, ISO/IEC 9594-8, Information Technology - Open Systems Interconnection - The Directory: Authentication Framework, 1993.
[2] S.R Subramanyam and Byung K.Yi, ”Digital Signatures”, IEEE, April 2006.
[3] Alexander W.Dent , “Choosing key sizes for cryptography”, Elsevier Ltd. , University Of London, Royal Holloway, UK, 2010, pp.21-27.
[4] William Stallings “Network Security Essentials (Applications and Standards)”, Pearson Education,2004.
[5] R.L. Rivest ,A. Shamir, L.M. Adleman, A method for obtaining digital signatures and public-key cryptosystems, Communications of the ACM ,1978, pp. 120–126.
[6] Digital security standard, federal information processing standard publication, available at http://dx.doi.org/10.6028/nist.fips.186-4,issue July 2013.
[7] Kai Wang et al.,”A multiple secret sharing scheme based on matrix projection”,Proceedings of 33rd annual IEEE international computer software and applications conference,U.S.A,2009,pp.400-405.
[8] A.Shamir,”How to share a secret”,Communication of ACM,vol.22,1979,pp-612-613.
Li Bai and Xukai Zou,”A proactive secret sharing scheme in matrix projection method”,International Journal of Security and Networks

Surbhi Sharma, "Embedding more security in digital signature system by using combination of public key cryptography and secret sharing scheme," International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.111-115, 2016.

In recent years, load balancing is the challenging task which affects the performance in allotting the resources on homogeneous and heterogeneous cluster computing environment. This research proposes an enhancement in ACCS (Adaptively Circulates job among all servers by taking account of both Client activity and System load) policies by incorporating Map Reduce to overcome the problem in balancing the workload for resources. This technique provides simplicity and flexibility for data partitioning, localization and processing jobs as indicated by their present sizes and ranks the servers based on their loads by giving high priority to the smaller jobs. Map Reduce emphasizes more on high throughput of data on low-latency of job execution in a cluster to accomplish huge execution advantages. Trace driven simulations demonstrate the viability and robustness of Map Reduce under numerous different situations.

Load Balancing, Map Reduce, Web Server Clusters, AdaptLoad, ACCS

[1] Gupta, V., Balter, M. H., Sigman, K., & Whitt, W. (2007). Analysis of join-the-shortest-queue routing for web server farms. Performance Evaluation,64(9), 1062-1081.
[2] Pai. V. S., Aron, M., Banga, G., Svendsen, M., Druschel, P., Zwaenepoel, W., & Nahum, E. (1998, October). Locality-aware request distribution in cluster-based network servers.InACM Sigplan Notices (Vol. 33, No. 11, pp. 205-216).ACM.
[3]Teo, Y. M., &Ayani, R. (2001). Comparison of load balancing strategies on cluster-based web servers. Simulation, 77(5-6), 185-195.
[4]Alonso-Calvo, R., Crespo, J., Garc’ia-Remesal, M., Anguita, A., &Maojo, V. (2010). On distributing load in cloud computing: A real application for very-large image datasets. Procedia Computer Science, 1(1), 2669-2677.
[5]Feng, H., Misra, V., & Rubenstein, D. (2005). Optimal state-free, size-aware dispatching for heterogeneous M/G/-type systems. Performance evaluation,62(1), 475-492.
[6]Harchol-Balter, M., & Downey, A. B. (1997). Exploiting process lifetime distributions for dynamic load balancing. ACM Transactions on Computer Systems (TOCS), 15(3), 253-285.
[7]Winston, W. (1977). Optimality of the shortest line discipline. Journal of Applied Probability, 181-189.
[8]Bonomi, F. (1990). On job assignment for a parallel system of processor sharing queues. Computers, IEEE Transactions on, 39(7), 858-869.
[9]Bachmat, E., &Sarfati, H. (2010). Analysis of SITA policies. Performance Evaluation, 67(2), 102-120.
[10]Riska, A., Sun, W., Smirni, E., &Ciardo, G. (2002). ADAPTLOAD: effective balancing in clustered web servers under transient load conditions. InDistributed Computing Systems, 2002.Proceedings. 22nd International Conference on (pp. 104-111). IEEE.
[11]Luis, A., &Azer, B. (2000). Load balancing a cluster of web servers. InProceedings of IEEE International Performance, Computing, and Communications Conference (IPCCC‟ 00), ISBN: 0-7803-5979-
[12]Crescenzi, P., Gambosi, G., Nicosia, G., Penna, P., & Unger, W. (2007). On-line load balancing made simple: Greedy strikes back. Journal of Discrete Algorithms, 5(1), 162-175.
[13]Niu, Y., Chen, H., Hsu, F., Wang, Y. M., & Ma, M. (2007, February). A Quantitative Study of Forum Spamming Using Context-based Analysis.InNDSS.
[14]Garg, A. (2015). A Framework to Optimize Load Balancing to Improve the Performance of Distributed Systems. International Journal of Computer Applications, 122(15).
[15]Psaras, I., &Mamatas, L. (2011). On demand connectivity sharing: Queuing management and load balancing for user-provided networks. Computer Networks, 55(2), 399-414.
[16] Gupta, R. K., & Ahmad, J. (2014). Dynamic Load Balancing By Scheduling In Computational Grid System. Computer Engineering and Intelligent Systems, 5(6), 39-45.
[17] Ungureanu, V., Melamed, B., &Katehakis, M. (2008). Effective load balancing for cluster-based servers employing job preemption. Performance Evaluation, 65(8), 606-622.

Deepti Sharma and Vijay B. Aggarwal, "Mapreduce- A Fabric Clustered Approach to Equilibrate the Load," International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.116-123, 2016.

Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. Cloud computing is a general term for the delivery of hosted services over the internet. Cloud computing enables companies to consume the resources and compute their utility rather than building and maintaining computing infrastructure. A cloud database is a database that has been optimized or built for a virtualized computing environment. Since these data-centers may be located in any part of the world beyond the reach and control of users, there are multifarious security and privacy challenges that need to be understood and addressed. Cloud has been prone to various security issues like storage, computation and attacks like Denial of service, Distributed Denial of Service, Eavesdropping, insecure authentication or logging etc. Privacy preservation is main security issue in public cloud. This paper focuses on various security mechanisms that are provided in the enterprises and also discusses few of the common security mechanisms like auditing, authentication, authorization, encryption and access control.

Cloud database, Security, Privacy Preservation, Auditing, Authentication, DaaS

[1] Peter Mell,TimothyGrance,”The NIST definition of cloud computing”,http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf
[2] InduArora ,Dr.AnuGupta, “Cloud database: A paradigm shift in Databases”, IJCI ,july 2012.
[3] R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic,“Cloud computing and emerging it platforms: Vision, hype, andreality for delivering computing as the 5th utility,” Future GenerationComput. Syst., vol. 25, no. 6, pp. 599–616, 2009.
[4] Suraj S. Gaikwad, Amar R. Buchade, "Survey on Securing data using Homomorphic Encryption in Cloud Computing.", International Journal of Computer Sciences and Engineering, Volume-04, Issue-01, Page No (17-21), Jan -2016
[5]Shivlal Mewada, Umesh Kumar Singh and Pradeep Sharma, "Security Enhancement in Cloud Computing (CC)", ISROSET-International Journal of Scientific Research in Computer Science and Engineering, Vol.-01, Issue-01, pp (31-37), Jan -Feb 2013.
[6] Boyang Wang, Baochun Li and Hui Li, Oruta: Privacy-Preserving Public Auditing for Shared Data in the Cloud, IEEE Transactions on Cloud Computing, Vol. 2, No. 1, January-March 2014, Pp. 43-57
[7] G. Ateniese, R. Burns, R. Curtmola, J. Herring, L. Kissner, Z. Peterson, and D. Song, “Provable Data Possession at Untrusted Stores,” Proc. 14th ACM Conf. Computer and Comm. Security (CCS ’07), pp. 598-610, 2007.
[8] H. Shacham and B. Waters, “Compact Proofs of Retrievability,” Proc. 14th Int’l Conf. Theory and Application of Cryptology and Information Security: Advances in Cryptology (ASIACRYPT ’08), pp. 90- 107, 2008.
[9] C. Erway, A. Kupcu, C. Papamanthou, and R. Tamassia, “Dynamic Provable Data Possession,” Proc. 16th ACM Conf. Computer and Comm. Security (CCS’09), pp. 213-222, 2009.
[10] Q. Wang, C. Wang, J. Li, K. Ren, and W. Lou, “Enabling Public Verifiability and Data Dynamic for Storage Security in Cloud Computing,” Proc. 14th European Conf. Research in Computer Security (ESORICS’09), pp. 355-370, 2009.
[11] C. Wang, Q. Wang, K. Ren, and W. Lou, “Ensuring Data Storage Security in Cloud Computing,” Proc. 17th Int’l Workshop Quality ofService (IWQoS’09), pp. 1-9, 2009.
[12] B. Chen, R. Curtmola, G. Ateniese, and R. Burns, “Remote Data Checking for Network Coding-Based Distributed Storage Systems,” Proc. ACM Workshop Cloud Computing Security Workshop (CCSW’10), pp. 31-42, 2010.
[13] Y. Zhu, H. Wang, Z. Hu, G.-J. Ahn, H. Hu, and S.S Yau, “Dynamic Audit Services for Integrity Verification of Outsourced Storages in Clouds,” Proc. ACM Symp. Applied Computing (SAC’11), pp. 1550-1557, 2011.
[14] N. Cao, S. Yu, Z. Yang, W. Lou, and Y.T. Hou, “LT Codes-Based Secure and Reliable Cloud Storage Service,” Proc. IEEE INFOCOM, 2012.
[15] C. Wang, S.S. Chow, Q. Wang, K. Ren, and W. Lou, “Privacy-PreservingPublic Auditing for Secure Cloud Storage,” IEEE Trans. Computers, vol. 62, no. 2, pp. 362-375, Feb. 2013.

Nitesh Kumar Namdeo and Sachin Choudhari, "Privacy Preservation and Auditing in Public Cloud: A Review," International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.124-127, 2016.

Denial of service attack (DoS) is serious threat to the internet. The DoS attack affects on the computing systems such as database server, web server etc. Denial of Service attack prevents authorized user from accessing online services. Therefore effective detection of DoS attack is necessary for increasing the efficiency of server. The Multivariate correlation analysis(MCA) for network traffic characterization overcomes the problem of DoS attack. MCA uses triangle area technique for extracting correlative information between network traffic. Triangle area based method is used to speed up the MCA process. Then Support Vector Machine based classification technique used for attack classification using the triangle area based multivariate correlation information. The min-max normalization method presented to increase the detection rate of DoS attack.

Multivariate correlation analysis, DoS attack, support vector machine, Triangle area technique, normalization, detection rate

[1] Shuyuan Jin and Daniel S. Yeung ,“A Covariance Analysis Model for DDoS Attack Detection” IEEE 2004.
[2] Gloria C.Y. Tsang, Patrick P.K. Chan, Daneil S. Yeung and Eric C.C. Tsang “Denial of service detection by support vector machines and radial-basis function neural network” IEEE 2004.
[3] Venkata Suneetha Takkellapati and G.V.S.N.R.V Prasad,” Network Intrusion Detection system based on Feature Selection and Triangle area Support Vector Machine” International Journal of Engineering Trends and Technology- Volume3, Issue4- 2012
[4] Chih-Fong Tsai and Chia-Ying Lin, “A triangle area based nearest neighbors approach to intrusion detection” Pattern Recognition, vol.43, pp. 222 – 229, (2010).
[5] Wei Wang , Xiangliang Zhang , Sylvain Gombault , and Svein J. Knapskog , “Attribute Normalization in Network Intrusion Detection” 10th International Symposium on Pervasive Systems, Algorithms, and Networks, 2009.
[6] Zhiyuan Tan1, Aruna Jamdagni, Xiangjian He, Priyadarsi Nanda, and Ren Ping Liu,” Multivariate Correlation Analysis Technique Based on Euclidean Distance Map for Network Traffic Characterization” Research Centre for Innovation in IT Services and Applications (iNEXT)
[7] B. Kiranmai and A. Damodaram, “A Comprehensive Survey on Methods Implemented For Intruder Detection System” International Journal of Computer Sciences and Engineering volume-2,Issue-8,E-ISSN:2347-2693 V
[8] Zhiyuan Tan,Aruna Jamdagni,Xiangjian He,Priyadarshi Nanda and Ren Ping Liu, “A System for Denial of Service Attack detection based on Multivariate Correlation Analysis” IEEE Transaction on parallel and distributed systems,vol.25,No.2,February 2014
[9] Peter Harrington,“Machine Learning in Action”, Manning Publications ©2012.
[10] Wun-Hwa Chen, Sheng-Hsun Hsu, Hwang-Pin Shen “Application of SVM and ANN for intrusion detection” Computers & Operations Research 32 (2005) 2617–2634

Subhash Pingale, Ranjeetsingh Parihar and Prajakta Solankar3, "Denial of Service Attack Detection Using Multivariate Correlation Information and Support Vector Machine Classification," International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.128-132, 2016.

Text passwords have been widely used for user authentication, e.g., by almost all websites on the Internet. However, it is well-known that text passwords are insecure for a variety of reasons. For example, users tend to choose simple passwords which can be remembered easily. In favor of memorability, making them subject to dictionary attacks; and text passwords can be stolen by malicious software (e.g., keystroke loggers) when being entered from keyboards. Phishing is another serious threat to text passwords, by which, a user could be persuaded to visit a forged website and enter their passwords. The system aim is to grant access to a legal user, and to prevent the system from illegal or non-authorized person.

Text Password, Passfaces, Authentication, Authorized user, Phishing, Hybrid Password, BODMAS, Fisher Yates Randomizer Algorithm

[1] S.Anna Suganthi K.Karnavel “Virtual Password with Secret Function and Codebook(VFC) scheme for Protecting User’s Password as a Test for Security”Transactions on Engineering and Sciences Vol.2, Issue 11, November 2014.
[2] Bin B. Zhu, Jeff Yan, Guanbo Bao,maowei Yang and Ning Xu “Captcha as Graphical Password- A New Security Primitive Based on Hard AI Probelms”IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,VOL.9, NO.6, JUNE 2014.
[3] Shubham Bhardwaj, Varun Gandhi, Varsha Yadav, Lalit Poddar “New Era of authentication: 3-D Password”International Journal of Science, Engineering and Technology Research (IJSETR) Volume 1, Issue 5, November 2012.
[4] Syed Shabih ul Hasan Naqvi ,Samiullah Afzal IEEE “Operation Code Authentication Preventing Shoulder Surfing Attacks”. 2010.
[5] Wei Hu,Xiaoping Wu, Guoheng Wei ,” The Security Analysis of Graphical Passwords” International Conference on Communications and Intelligence Information Security,2010 .
[6] http://www.mathsisfun.com/operation-order- bodmas.html
[7] www.ask.com
[8] www.wikipidia.com
[9] www.answers.yahoo.com
[10] www.ehow.com

Prasad N. Urankar and Prasanna J. Shete , "Authentication using Mixed-mode approach.," International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.133-135, 2016.

Content delivery networks (CDNs) play an important role in today’s Internet. They serve a large multimedia data over the Internet and resolve the issues of scalability and network congestion. The content providers are no longer the sole generators of the content in CDN system but user are also involved. User generated content (UGC) is emerging as one of the dominant forms in the multimedia industry and is reshaping the way users watch video, with millions of content producers and consumers. In particular, multimedia sites are creating new viewing patterns and social interactions that influence the way users consume data. There also exists various techniques such as collaborative caching, context-aware recommendations, etc. that can help in efficient content delivery. In this paper, we present a survey on various techniques and methods employed in the delivery of multimedia content. We present a way to optimize the system performance, and characterizing the tradeoff between cost and quality of service in content delivery.

Content delivery networks, data mining, quality of service, UGC, user behaviour

[1] Zhi Wang, WenwuZhu,Minghua Chen, Lifeng Sun and Shiqiang Yang,"CPCDN: Content Delivery Powered by Context and User Intelligence", IEEE Transactions on Multimedia, Vol. 17, No. 1, January 2015.
[2] M. Cha, H. Kwak, P. Rodriguez, Y. Ahn, and S. Moon, “I tube, you-tube, everybody tubes: Analyzing the World’s Largest User Generated Content Video System”, in Proc. ACM SIGCOMM Conference on Internet Measurement, pp. 1–14, 2007.
[3] F. Chen, K. Guo, J. Lin, and T. La Porta, “Intra-cloud lightning: Building CDNs in the Cloud”, in Proc. IEEE International Conference on Computer Communication, pp. 433–441, Mar. 2012.
[4] A.Datta, K. Dutta, H. Thomas,D.VanderMeer andK.Ramamritham, “Proxy-based Acceleration of Dynamically Generated Content on the World Wide Web:An approach and Implementation”, ACM Transaction onDatabase Systems, vol. 29, no. 2, pp. 403–443, 2004.
[5] H. Liu, Y. Wang, Y. Yang, A. Tian and H. Wang, “Optimizing Cost and Performance for Content Multihoming”, in Proc. ACM SIGCOMM Conference on Applications, technologies, architectures and protocols for computer communication, pp. 371–382, 2012.
[6] I. Poese, B. Frank, B. Ager, G. Smaragdakis and A. Feldmann, “Improving Content Delivery Using Provider-aided Distance Information”, in Proc. ACM SIGCOMM Conference on Internet Measurement, pp. 22–34, 2010.
[7] S. Scellato, C. Mascolo, M. Musolesi and J. Crowcroft, “Track Globally, Deliver Locally: Improving Content Delivery Networks by Tracking Geographic Social Cascades”, in Proc. ACM International Conference on World Wide Web, pp. 457–466, 2011.
[8] G. Szabo and B. A. Huberman, “Predicting the Popularity of Online Content”, Communications of ACM, vol. 53, no. 8, pp. 80–88, 2010.
[9] Z. Wang, L. Sun, X. Chen, W. Zhu, J. Liu, M. Chen and S. Yang, “Propagation-Based Social-Aware Replication for Social Video Contents”, in Proc. ACM International Conference on Multimedia, pp. 29–38, 2012.
[10] H. Yin, X. Liu, T. Zhan, V. Sekar, F. Qiu, C. Lin, H. Zhang and B. Li, “Design and Deployment of a Hybrid CDN-P2P System for Live Video Streaming: Experiences with Livesky”, in Proc. ACM International Conference on Multimedia, pp. 25–34, 2009.

Aaqib Bashir and Prof. T. H. Gurav, "A Survey on Content Delivery of Web-Pages," International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.136-138, 2016.

Ranking fraud in the mobile App market refers to fraudulent or deceptive activities which have a purpose of bumping up the Apps in the popularity list. Indeed, it becomes more and more frequent for App developers to use shady means, such as inflating their Apps’ sales or posting phony App ratings, to commit ranking fraud. While the importance of preventing ranking fraud has been widely recognized, there is limited understanding and research in this area. To this end, in this paper, we provide a holistic view of ranking fraud and propose a ranking fraud detection system for mobile Apps. Specifically, we first propose to accurately locate the ranking fraud by mining the active periods, namely leading sessions, of mobile Apps. Such leading sessions can be leveraged for detecting the local anomaly instead of global anomaly of App rankings. Furthermore, we investigate three types of evidences, i.e., ranking based evidences, rating based evidences and review based evidences, by modelling Apps’ ranking, rating and review behaviours through statistical hypotheses tests. In addition, we propose an optimization based aggregation method to integrate all the evidences for fraud detection. Finally, we evaluate the proposed system with real-world App data collected from the iOS App Store for a long time period. In the experiments, we validate the effectiveness of the proposed system, and show the scalability of the detection algorithm as well as some regularity of ranking fraud activities.

Mobile ranking, fraudulent mobile apps

[1] L. Azzopardi, M. Girolami, and K. V. Risjbergen, “Investigating the relationship between language model perplexity and ir precision-recall measures,” in Proc. 26th Int. Conf. Res. Develop. Inform. Retrieval, 2003, pp. 369–370.
[2] D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet allocation,” J. Mach. Learn. Res., pp. 993–1022, 2003.
[3] Java The complete Reference.
[4] My sql Reference Books.

Priyanka.jali and Nagavani Biradar , "A Novel Approach of Investigating Deceptive Activities of Developer for Ranking apps," International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.139-141, 2016.

Intelligent Shopping Agent is an Agent which helps user to browse online shops, compare prices and order merchandise sitting at home on their PC and get suggestions on what products are best suitable for them to buy and buy it at one click with an opportunity where the intelligent agents adds the products directly to the user cart. The interesting feature this website provides is price comparison and allowing the user to go buy goods from other website as well, without redirection this intern profits the user as they do not have to surf multiple website to find the best deal and the owner is profited as the users buy the products using their referral code.

Intelligent; Shopping Agent; Smart; E-commerce; Intelligent System, recommendation ,online shopping

[1]: Xiaojun Shen, Shirmohammadi, Desmarais, C., Georganas, Nicolas D., Kerr I., "Enhancing e-Commerce with Intelligent Agents in Collaborative e-Communities," in E-Commerce Technology, 2006. The 8th IEEE International Conference on and Enterprise Computing, E-Commerce, and E-Services, The 3rd IEEE International Conference on , On page(s): 35 - 35, 26-29 June 2006
[2]: Xin Zhang, Weilong Liu, Fang Jin, "Intelligent Agent for Knowledge Management in E-Commerce," in Pervasive Computing and Applications, 2006 1st International Symposium on , Volume: 3, On page(s): 455 - 460, 3-5 Aug. 2006
[3]: Rehman,, S.U. Coughlan, J., "Smart agent for automated e-commerce," in Sustainable Technologies (WCST), 2011 World Congress on , On page(s): 124 - 128, 7-10 Nov. 2011
[4]. Ma, Yanping, and Esma Aïmeur. "Intelligent agent in electronic commerce-XMLFinder." Enabling Technologies: Infrastructure for Collaborative Enterprises, 2001. WET ICE 2001. Proceedings. Tenth IEEE International Workshops on. IEEE, 2001.

Sameer Shanbhag, Sujith Nair, Nikhil Nair, Bushra Shaikh, "Intelligent Shopping Agent," International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.142-145, 2016.