Volume-4 , Issue-4 , Aug 2016, ISSN 2320-7639 (Online) Go Back

• Open Access   Article

  Internet Surfing Prediction System using Association Rule Mining based on FP-Growth

  A. Singh, N. Jain

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.4 , Issue.4 , pp.1-6, Aug-2016

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  Web Usage mining is nascent field of research which deals with extraction of interesting knowledge from the web log files. This paper describes the recent works on the field of Web Usage Mining(WUM) it is for the benefit of research on the future request and personalization of web based information services. Web Usage Mining puts an effort to determine valuable information from the secondary data obtained from the communications of users with the web. WUM involves three steps, which includes Preprocessing, Pattern discovery and Pattern analysis.

  Key-Words / Index Term
  Web Usage Mining Frequent Pattern Tree Association Rule Web Logs.

  References
  [1]. R. Chourasia, P. Choudhary, "An Approach For Web Log Pre-Processing And Evidence Preservation For Web Mining", International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.210-216, 2014.
  [2]. M. Spilopoulou, L.C. Faultstich, Wum, “AWeb utilization Miner”, The World Wide Web and Databases, Vol. 1950, pp,184-20, 1998.
  [3]. J. Srivastav, R. Colley, M. Despanade, PN. Tan, “Web Usage Mining: Discovery and Application of Usage Pattern from Web Data”, ACM SIGKDD Explorations Newsletter, Volume.1, Issue.2, pp.12-23, 2000.
  [4]. R. Cooley, B. Mobasher, J. Srivasta, “Web Mining: Information and pattern Discovery on theWorld WideWeb”, IEEE International Conference on tools with Artificial Intelligence,USA, pp.558-567, 1997.
  [5]. O. Etzioni, “The world wide web Quagmire or gold mine”, Magazine Communications of the ACM, Vol.39 Issue.11, pp.65-68, 1996.
  [6]. R. Das, L. Turkoglu, “ Creatingmeaningful data from web logs for improving the impressiveness of a website by using path analysis method”, Expert Systems with Applications, Vol.36, Issue.3, pp.6635–6644, 2009.
  [7]. P. Weichbrth, M. Owoc, M. Pleszkum, “Web User Navigation Pattern Discovery from WWW Server Log Files”, Proceeding of the Federal Conference on Computer Science and Information Systems, Wroclaw, pp.1171-1176, 2012.
  [8]. V. Sujatha, Punithavalli, “Improved User Navigation Pattern Preditions From Web Log Data ”, Procedia Engineering, Vol.30, pp.92-99, 2012.
  [9]. G. Mansingh, L. Rao, KMO. Bryson, “Profilling Internet Banking users:A Knowledge discovery in data mining process model based approach” , Information Systems Frontiers, Vol.17, Issue.1, pp.193–215, 2015.
  [10]. U. Patil, S. Pardeshi, “A Servey on User Future Request Prediction: Web Usage Mining”, International journal of Emerging Technology and advanced Engineering, Vol.2, Issue. 3, pp. 121-124, 2012.
  

  Citation
  A. Singh, N. Jain, "Internet Surfing Prediction System using Association Rule Mining based on FP-Growth," International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.4, pp.1-6, 2016

• Open Access   Article

  Discovery of Ranking Fraud Detection System for Mobile Apps

  R. Satraboyina, GK. Chakravarthi

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.4 , Issue.4 , pp.7-10, Aug-2016

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  In Mobile App market, ranking fraud refers to fraudulent or deceptive activities for the purpose of bounce up the Apps in the popularity list. It became more common for App developers to raise there App’s by tricky means, sales or posting phony App ratings, to commit ranking fraud. The significance of preventing ranking fraud has been widely identified. There is limited understanding and research in this area. In this paper, we provide a complete view of ranking fraud and suggest a ranking fraud detection system for mobile Apps. Firstly, we 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 rather of global anomaly of App ratings. Moreover, we scrutinize three types of evidences, i.e., ranking based evidences, rating based evidences and review based evidences, by modeling Apps’ ranking, rating and review nature through statistical hypotheses tests. Additionally, we propose an optimization based aggregation method to consolidate all the evidences for fraud detection and finally we evaluate the proposed system with real-world App data collected from the iOS App Store for a lengthy time period. In the experiments, we validate the effectiveness of the suggested system, and show the scalability of the detection algorithm as well as some regularity of ranking fraud activities.

  Key-Words / Index Term
  Mobile Apps; ranking fraud detection; evidence aggregation; historical ranking records; rating and review; Recommendation app; KNN

  References
  [1]. S. Gupta, "Addressing the Issues in Mobile Application Development", International Journal of Computer Sciences and Engineering, Vol.2, Issue.7, pp.1-5, 2014.
  [2]. P. Deshmukh, P. Agarkar, “Mobile Application For Malware Detection”, International Research Journal of Engineering and Technology (IRJET), Vol.2, Issue.2, pp.883-886, 2015.
  [3]. B. A. Wichmann, A. A. Canning, D. L. Clutterbuck, L. A. Winsborrow, N. J. Ward, D. W. R. Marsh, "Industrial perspective on static analysis", Journal of Software Engineering, Vol.10, No.2, pp. 69-75, 1995.
  [4]. MA. Dar, J. Parvez, “Evaluating Smartphone Application Security: A Case Study on Android”, Global Journal of Computer Science and Technology Network, Web & Security Vol.13, Issue.12, pp. 1-8, 2013.
  [5]. N. Radha and S. Ramya , "Performance Analysis of Machine Learning Algorithms for Predicting Chronic Kidney Disease", International Journal of Computer Sciences and Engineering, Vol.3, Issue.8, pp.72-76, 2015.
  [6]. A. Agarwal, N. K. Sharma, P. Gupta, P. Saxena, R.K. Pal, S. Mehrotra, P. Nair, M. Wadhwa, "Mobile Application Development with Augmented Reality", International Journal of Computer Sciences and Engineering, Vol.2, Issue.5, pp.20-25, 2014.
  [7]. SS. Bhadoria, H. Gupta , "A Wearable Personal Healthcare and Emergency Information Based On Mobile Application", International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.4, pp.24-30, 2013.
  [8]. S.Venkatesan and K.Renuka Devi, "Managing Connection Based Access Control Systems on the other hand Mobile Devices", International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.298-301, 2015.
  [9]. H. Zhu ; E. Chen ; H. Xiong ; H. Cao ; J. Tian, “Mobile App Classification with Enriched Contextual Information”, IEEE Transactions on Mobile Computing,Vol.13, Issue.7, 2014.
  

  Citation
  R. Satraboyina, GK. Chakravarthi, "Discovery of Ranking Fraud Detection System for Mobile Apps," International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.4, pp.7-10, 2016

• Open Access   Article

  Reputation Calculation and Trust Management System in Cloud Integrated Wireless Sensor Networks

  VS. Kumar, GK. Chakravarthi

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.4 , Issue.4 , pp.11-15, Aug-2016

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  The integration in Cloud computing with Wireless sensor network has been attracting several researchers in the industry as it provides many opportunities for organizations by contributing a range of computing services. So, data gathering capability of wireless sensor networks (WSNs) become easy. For cloud computing to become widely adopted by both the enterprises and individuals, several issues have to be solved. However, authentication as well as trust and reputation calculation and management of cloud service providers (CSPs) and sensor network providers (SNPs) are two very critical and barely explored issues for this new paradigm. Trust management is one of the most challenging issues in the emerging cloud computing area. During the past few years, many studies have suggested different techniques to address trust management issues. Yet, despite these past efforts, several trust management issues such as identification, privacy, personalization, integration, security, and scalability have been usually neglected and need to be addressed. In this article, we present an overview of the cloud service models and we survey the main techniques and research prototypes that adequately support trust management of services in cloud environments. We present a generic analytical framework that assesses existing trust management research prototypes in cloud computing and relevant areas using a set of assessment criteria. Open research issues for trust management in cloud environments are also discussed.

  Key-Words / Index Term
  Cloud; sensor networks; integration; authentication; trust; reputation

  References
  [1]. C. Zhu, V.C.M. Leung, “An Authenticated Trust and Reputation Calculatio n and Management System for Cloud and Sensor Networks Integration” IEEE Transactions on Information Forensics And Security, Vol. 10, No. 1, pp.118.131, 2015.
  [2]. R. Piplode, P. Sharma, 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.
  [3]. R. Buyya, C.S. Yeo, S. Venugopal, J. Broberg, I. Brandic, “Cloud computing and emerging IT platforms : Vis ion, hype, and reality for delivering computing as the 5th utility”, Future Generation Computer System, Vol.25, No.6, pp.599–616, 2009.
  [4]. C. Santhiya, KA. Vanishree, M.K. Chandrasekaran, "Secured Group Data Bestow with Key-Agglomerative Searchable Encryption via Cloud Storage", International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.242-247, 2015.
  [5]. K.M. Sim, “Agent-based cloud computing”, IEEE Transaction Services Computer, Vol.5, No.4, pp. 564–577, 2012.
  [6]. Aditya Singh Mandloi and Vineeta Choudhary, "Study of Various Techniques for Data Gathering in WSN", International Journal of Scientific Research in Network Security and Communication, Vol..1, Issue.3, pp.12-15, 2013.
  [7]. C. Zhu, L. Shu, T. Hara, L. Wang, S. Nishio, L.T. Yang, “A survey on communication and data management issues in mobile sensor networks”, Wireless Communication Mobile Computer, Vol.14, No. 1, pp. 19–36, 2014.
  [8]. M. Li and Y. Liu, “Underground coal mine monitoring with wireless sensor networks,” ACM Transaction Sensor Network., Vol.5, No.2, pp.1-29, 2009.
  [9]. Q. Zhang, L. Cheng, R. Boutaba, “Cloud computing: State-of-theart and research challenges”, Journal of Internet Services Applications, Vol. 1, No. 1, pp.7–18, 2010.
  [10]. R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, I. Brandic, “Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility”, Future Generation Computer System, Vol.25, No.6, pp.599–616, 2009.
  [11]. J. Baliga, R. W. A. Ayre, K. Hinton, R. S. Tucker, “Green cloud computing: Balancing energy in processing, storage, and transport”, Proceeding of IEEE, Vol. 99, No.1, pp.149–167, 2011.
  [12]. P. Kaur, S. Majithia, "Implementation and Analysis of Replicated Agent Based Load Balancing In Cloud Computing", International Journal of Computer Sciences and Engineering, Vol.2, Issue.8, pp.21-27, 2014.
  [13]. F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, “Wireless sensor networks: A survey,” Comput. Netw., Int. J. Comput. Telecommun. Netw., vol. 38, no. 4, pp. 393–422, Mar. 2002.
  [14]. Mohd.A. Salam, A.C. Pandey, "Mobile Cloud Computing: Taking Web-Based Mobile Applications to the Cloud", International Journal of Computer Sciences and Engineering, Vol.2, Issue.1, pp.35-42, 2014.
  

  Citation
  VS. Kumar, GK. Chakravarthi, "Reputation Calculation and Trust Management System in Cloud Integrated Wireless Sensor Networks," International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.4, pp.11-15, 2016

• Open Access   Article

  Key Exposure Resilience for Cloud Storage Auditing

  JS. Ande, AS. Kumar, AV. Kumar

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.4 , Issue.4 , pp.16-19, Aug-2016

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  Abstract
  Never days data is dynamically updated, the existing remote integrity examine the methods which served as a reason for static data can no longer be impose to authenticate the integrity of dynamic data in the cloud. In this scenario, cloud storage auditing carries an efficient and secure dynamic auditing protocol which pulls a confidence to the data owners that their data is correctly stored in the cloud. The existing auditing protocols assume that the secret key of the client is very secure while in reality, it is not. Therefore, to overcome these fault, this paper launches an idea of lessening the client’s secret key revelation. In this paper, we suggest a system where de-duplication strategy of data is taking on and it will verify the duplicity of data and eliminate the redundant one using MD5 hashing. Also, it uses tile bitmap method wherein it will identify the previous and the current versions of the data to ease the auditor’s workload and to make the system more efficient.

  Key-Words / Index Term
  Data storage; cloud storage auditing; de-duplication,; cloud computation; key-exposure resistance; tile bitma

  References
  [1]. S.L.Mewada, U.K. Singh, P. Sharma, "Security Enhancement in Cloud Computing (CC)", International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.1, pp.31-37, 2013.
  [2]. M. Armbrust, A. Fox, R. Griffith, A.D. Joseph, R.H. Katz, A. Konwinski, G. Lee, D.A. Patterson, A. Rabkin, I. Stoica, M. Zaharia, “Above the clouds: A berkeley view of cloud computing,” University of California, Berkeley, pp.1-28, 2009.
  [3]. S. Ayyub, D. Roy, "Cloud Computing Characteristics and Security Issues", International Journal of Computer Sciences and Engineering, Vol.1, Issue.4, pp.18-22, 2013.
  [4]. J. Kincaid, “MediaMax/TheLinkup Closes Its Doors,” from www.techcrunch.com, US, pp.1-2,2008.
  [5]. Amazon.com, “Amazon s3 availability event: July 20, 2008,” Online at http://status.aws.amazon.com/s3-20080720.html, 2008.
  [6]. S. Wilson, “Appengine outage”, Online at http://www. cio-weblog.com/50226711/appengine outage.php, June 2008.
  [7]. B. Krebs, “Payment Processor Breach May Be Largest Ever”, Online at http://voices.washingtonpost.com/securityfix/ 2009/01/payment processor breach may b.html, Jan. 2009.
  [8]. G. Ateniese, R. Burns, R. Curtmola, J. Herring, L. Kissner, Z. Peterson, D. Song, “Provable data possession at untrusted stores” Provable data possession at untrusted stores, Proceedings of the 14th ACM conference on Computer and communications security, Virginia, pp.598-609, 2007.
  [9]. M. A. Shah, R. Swaminathan, M. Baker, “Privacypreserving audit and extraction of digital contents”, Cryptology ePrint Archive Report, Vol.2008, pp.1-21, 2008.
  [10]. Q. Wang, C. Wang, J. Li, K. Ren, W. Lou, “Enabling public verifiability and data dynamics for storage security in cloud computing”, LNCS Springer, vol.5789, pp.355–370, 2009.
  A. Juels, J. Burton S. Kaliski, “Pors: Proofs of retrievability for large files”, Proceedings of the 14th ACM conference on Computer and communications security, NewYork, pp.584–597, 2007..
  [11]. E. Thomas, R. Puttini, M. Zaigham, "Book of Cloud Computing: Concepts, Technology & Architecture", Prentice Hall, NewJersey, pp.1-528, 2013.
  [12]. H. Shacham and B. Waters, “Compact proofs of retrievability”, in Proc. of Asiacrypt 2008, vol. 5350, Dec 2008, pp. 90–107.
  [13]. M. A. Shah, M. Baker, J. C. Mogul, R. Swaminathan, “Auditing to keep online storage services honest”, Proceeding HOTOS`07 Proceedings of the 11th USENIX workshop on Hot topics in operating systems, SanDiego, pp.1-6, 2007.
  

  Citation
  JS. Ande, AS. Kumar, AV. Kumar , "Key Exposure Resilience for Cloud Storage Auditing," International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.4, pp.16-19, 2016

• Open Access   Article

  Multi path dynamic routing for data integrity and delay Minimization differentiated services in wireless sensor network

  V. Prasad, VS. Sunsan

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.4 , Issue.4 , pp.20-23, Aug-2016

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  Abstract
  Wireless Sensor Network (WSN) is usually indicated as a network of devices. Devices are represented as nodes, which may sense the neighbouring and communicate the data gathered from the neighbouring in wireless way. In WSN, the QoS requirements can be determined as the performance measurements, such as, delay, reliability, throughput or jitter. The different applications which run on WSN can have distinct QoS requirements. The dynamic routing algorithm called Integrity and Delay Differentiated Routing (IDDR) is used to differentiate the packets with different QoS requirements. Low Energy Adaptive Clustering Hierarchy (LEACH) protocol has been used to make the clustering and cluster heads are chosen considering the highest energy. The cluster heads with minimum distance are used to transmit packets to the sink node. The routing algorithm differentiate the packet based on the weight present at header and sends the delay sensitive packet through shortest path and data integrity packets along sub optimal path. Data integrity packets are encrypted using homomorphic encryption technique to provide security. The performance metrics like energy consumption, average packet drop, packet delivery ratio, and queue length are examined. The simulation results show that suggested method is much efficient when compared with MinRoute protocol.

  Key-Words / Index Term
  Wireless sensor networks, Quality of service, Routing, Performance

  References
  [1]. A. Ganz, Z. Ganz, and K. Wongthavarawat, “Multimedia Wireless networks: technologies Standards, and QoS” , Prentice Hall, Upper Saddle River, NJ, 2004.
  [2]. J. Zhang, F. Ren, S. Gao, H. Yang, C. Lin, “Dynamic Routing for Data Integrity and Delay Differentiated Services in Wireless Sensor Networks”, IEEE Transactions on Mobile Computing, Vol.14, Issue.2, Feb.1, pp.328-343, 2015
  [3]. T. He, et al., “SPEED: A Stateless Protocol for Real-Time Communication in Sensor Networks”, Proceedings of International Conference on Distributed Computing Systems, Providence, pp. 46-55, 2003.
  [4]. R. Nathiya, S.G. Santhi, "Energy Efficient Routing with Mobile Collector in Wireless Sensor Networks (WSNs)", International Journal of Computer Sciences and Engineering, Vol.2, Issue.2, pp.36-43, 2014.
  [5]. Emad Felemban, Chang-Gun Lee, Eylem Ekici, “MMSPEED: Multipath Multi-SPEED Protocol for QoS Guarantee of Reliability and Timeliness in Wireless Sensor Networks”, IEEE Transaction on mobile computing, Vol. 5, no. 6, pp.710-715, 2006.
  [6]. Smita, R. Bhakar, "Improving the Stability and Quality of Service in Cluster Head Rule Base System Using MANETs", International Journal of Computer Sciences and Engineering, Vol.2, Issue.5, pp.4-7, 2014.
  [7]. H.W. Tsai, “Mobile Object Tracking in Wireless Sensor Networks”, Journal of Computer Communications,Vol. 30, No. 8, March 2007.
  [8]. P. Ji, “DAST: A QoS-Aware Routing Protocol for Wireless Sensor Networks”, Proceeding of International Conferences on Embedded Software and Systems Symposia, Sichuan, pp. 259-264, 2008.
  [9]. Ben-Othman, Jalel, Bashir 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.
  [10]. S-J Park and R. Sivalumar, "Poster: Sink-to-Sensors Reliability in Sensor networks", ACM SIGMOBILE Mobile Computing and Communications Review, Vol. 7, No. 3, pp. 27-28, 2003.
  [11]. P.T.A. Quang, D.S. Kim, “Enhancing Real-Time Delivery of Gradient Routing for Industrial Wireless Sensor networks”, IEEE Transactions on Industrial Informatics, Vol.8, No. 1, pp. 61–68, 2012.
  [12]. M. Radi, B. Dezfouli, K. A. Bakar, S. A. Razak, M. A. Nematbakhsh, “Interference-Aware Multipath Routing Protocol for QoS Improvement in Event-driven Wireless Sensor Networks,” Tsinghua Science & Technology, vol. 16, no. 5, pp. 475–490, 2011.
  [13]. M. Razzaque, M.M. Alam, M. MAMUN-OR-RASHID, C.S. Hong, “Multi-constrained QoS Geographic Routing for Heterogeneous Traffic in Sensor Networks”, IEICE Transactions on Communications,” IEICE Transactions on Communications, Vol.91B, No.8, pp.2589– 2601, 2008.
  

  Citation
  V. Prasad, VS. Sunsan, "Multi path dynamic routing for data integrity and delay Minimization differentiated services in wireless sensor network," International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.4, pp.20-23, 2016