Volume-5 , Issue-11 , Nov - 2017 Go Back

Cloud computing is an effective and emerging technology for storing huge amounts of data. Most of the organizations and people are using cloud for storing various types of data. The critical challenge is to maintain the stored data without any redundancies due to billing nature of cloud. Deduplication is a popular technique used to remove duplicate copies from cloud. Existing deduplication techniques using convergent encryption does not support for authorized duplicate check. Authorized duplicate check is essential to protect the sensitivity and integrity of data that is stored. In this paper, the client-side authorized deduplication is implemented using hybrid cloud where the duplicate check is performed at client-side which improves data security and reduce network bandwidth. In this work, duplicate check for a file is performed by a token generated by private cloud based on privilege of user issued during system initialization and file content. Each file uploaded to the cloud is also bounded by a token to specify which kind of users is allowed to perform the duplicate check and access the files. The user is able to find a duplicate for this file if and only if there is a copy of this file and a matched privilege. To prevent unauthorized access, a secure proof of ownership (PoW) protocol is also implemented to provide the proof that the user indeed owns the same file instead of small information of file when a duplicate is found. It makes overhead to minimal compared to the normal convergent encryption and file upload operations.

Deduplication,AuthorizedDuplicateCheck,Confidentiality,ConvergentProtocol,HybridCloud,PoW

[1] Jin Li, Yan Kit Li, Xiaofeng Chen, Patrick P. C. Lee, Wenjing Lou” A Hybrid Cloud Approach for Secure Authorized De-duplication” in vol: pp no-99, IEEE, 2014.
[2] S. Quinlan and S. Dorward, “Venti: A new approach to archival storage,” in Proc. 1st USENIX Conf. File Storage Technol., Jan. 2002, p. 7.
[3] J. R. Douceur, A. Adya, W. J. Bolosky, D. Simon, and M. Theimer, “Reclaiming space from duplicate files in a serverless distributed file system,” in Proc. Int. Conf. Distrib. Comput. Syst., 2002, pp. 617–624.
[4] S. Halevi, D. Harnik, B. Pinkas, and A. Shulman-Peleg, “Proofs of ownership in remote storage systems,” in Proc. ACM Conf. Comput. Commun. Security, 2011, pp. 491–500
[5] D. Ferraiolo and R. Kuhn, “Role-based access controls,” in Proc. 15th NIST-NCSC Nat. Comput. Security Conf., 1992, pp. 554–563.
[6] R. S. Sandhu, E. J. Coyne, H. L. Feinstein, and C. E. Youman, “Role-based access control models,” IEEE Comput., vol. 29, no. 2, pp. 38–47, Feb. 1996.
[7] A. Shamir, How to Share a Secret, Communications of the ACM, vol. 22, no 11, pp. 612-613, 1979.
[8] M.W. Storer, K. Greenan, D.D.E. Long, and E.L. Miller, “Secure Data De-duplication”, Proceedings of the 4th ACM international workshop on Storage security and survivability, pp1-10, 2008
[9] M. Bellare, S. Keelveedhi, and T. Ristenpart, “Message-locked encryption and secure deduplication,” in Proc. 32nd Annu. Int. Conf. Theory Appl. Cryptographic Techn., 2013, pp. 296–312.
[10] W.K. Ng, Y. Wen, and H. Zhu, Private Data De-duplication Protocols in Cloud Storage, Proceedings of the 27th Annual ACM Symposium on Applied Computing,S. Ossowski and P. Lecca, Eds.,pp. 441-446, 2012.
[11] R.D. Pietro and A. Sorniotti, Boosting Efficiency and Security in Proof of Ownership for De-duplication,in Proceedings of ACM Symposium on Information, Computer and Communication Security, H.Y. Youm and Y. Won, Eds., pp. 81-82, 2012.
[12] S. Kamara and K. Lauter, Cryptographic Cloud Storage, in Proceedings of Financial Cryptography: Workshop on Real-Life Cryptograph. Protocols Standardization, pp.136-149, 2010
[13] D.T. Meyer and W.J. Bolosky, A Study of Practical De-duplication, in Proceedings of 9th USENIX Conference on File and Storage Technologies, pp. 1-13, 2011.
[14] M. Bellare, S. Keelveedhi, and T. Ristenpart, “Dupless: Serveraided encryption for deduplicated storage,” in Proc. 22nd USENIX Conf. Sec. Symp., 2013, pp. 179–194.
[15] J. Li, X. Chen, M. Li, J. Li, P. Lee, and W. Lou, “Secure deduplication with efficient and reliable convergent key management,” in Proc. IEEE Trans. Parallel Distrib. Syst., http://doi.ieeecomputersociety.org/10.1109/TPDS.2013.284, 2013.
[16] C. Ng and P. Lee, “Revdedup: A reverse deduplication storage system optimized for reads to latest backups,” in Proc. 4th AsiaPacific Workshop Syst., http://doi.acm.org/10.1145/2500727. 2500731, Apr. 2013.
[17] V.P.Muthukumar and R.Saranya, "A Survey on Security Threats and Attacks in Cloud Computing", International Journal of Computer Sciences and Engineering, Page No : 120-125, Volume-02 , Issue-11, E-ISSN: 2347-2693, Nov - 2014
[18] J. Stanek, A. Sorniotti, E. Androulaki, and L. Kencl, “A secure data deduplication scheme for cloud storage,” Tech. Rep. IBM Research, Zurich, ZUR 1308-022, 2013.
[19] M. Bellare, C. Namprempre, and G. Neven, “Security proofs for identity-based identification and signature schemes,” J. Cryptol., vol. 22, no. 1, pp. 1–61, 2009.

P. Mounika, S.Jyothsna, "Client-Side Authorized Deduplication In Cloud Using PoW," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.120-126, 2017.

In the last few years, the number of people around the world is increasing day by day in matching the use of the internet and social media. For this reason, a large volume of data is generated by the internet and social media from gigabytes (GB) to petabytes (PB) with high speed. In this work, it is proposed Intrusion Detection System (IDS) with large amounts of data to address challenges in various types of network attacks using machine learning techniques. On another hand, it is proposed Principal Components Analysis method to reduce high dimensionality and features of data. Therefore, in order to reduce amounts of calculations and improve an accuracy of classification of data. That is, why the use of DARBAI data set in this model and it is applied to K-nearest neighbour method for classification.

Big data; Intrusion Detection System (IDS), Principal Component Analysis (PCA), K-Nearest neighbour (KNN)

[1]. Md. Al Mehedi Hasan et al: Support Vector Machine and Random Forest Modeling for Intrusion Detection System (IDS) Journal of Intelligent Learning Systems and Applications, 45-52, 2014.
[2]. Yuteng Guo et al: Feature Selection Based on Rough Set and Modified Genetic Algorithm for Intrusion Detection. The 5th International Conference on Computer Science & Education Hefei, China. August 24–27, 2010, 97 8-1-4244-6005-2/10/$26.00 ©2010 IEEE.
[3]. Dong Seong et al: An Optimized Intrusion Detection System Using PCA and BNN.
[4]. Elkhadir et al: Intrusion Detection System Using PCA and Kernel PCA Method. IAENG International Journal of Computer Science, 43:1, IJCS_43_1_09(Advance online publication: 29 February 2016)
[5]. Gholam Reza et al: Category-Based Intrusion Detection Using PCA. Journal of Information Security, 2012, 3, 259-271 http://dx.doi.org/10.4236/jis.2012.34033 Published Online October 2012
[6]. R. Wankhede, V. Chole, "Intrusion Detection System Using Hybrid Classification Technique", International Journal of Computer Sciences and Engineering, Vol.4, Issue.11, pp.30-33, 2016.
[7]. Gan Xu-shing et al: Anomaly intrusion detection based on PLS feature extraction and core vector machine.0950-7051/$-see front matter 2012 Elsevier B.V. All rights reserved.
[8]. Zyad Elkhadir et al: Intrusion Detection System Using PCA and Kernel PCA Methods.16 April 2016
[9]. S.Venkata Lakshmi et al: Application of k-Nearest Neighbour Classification Method for Intrusion Detection in Network Data. International Journal of Computer Applications (0975 – 8887) Volume 97– No.7, July 2014.
[10]. Yihua Liao et al: Use of K-Nearest Neighbor classifier for intrusion detection. Computers & Security Vol 21, No 5, pp 439-448, 2002 Copyright ©2002 Elsevier Science Ltd.

Saqr Mohammed H. Almansob, Santosh Shivajirao. Lomte, "Addressing Challenges in Big Data Intrusion Detection System using Machine Learning Techniques," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.127-130, 2017.

Two technologies currently change the world and most important for organizations: Cloud Computing and Big Data. Data is everywhere; this data is generated by the organization, people and machine. Insight analysis of this data is most important. Cloud computing provide the services for storage, process and analysis of this large and complex data sets that can create competitive advantage, spark new innovations. The main objective of this paper to describe the concepts of big data, cloud computing, Big Data as a Service (BDaaS) and Hadoop as a Service (HDaaS) on cloud platform. . Apache Hadoop is an open source software framework to store and analysis of Big Data. Demo IBM Ambari Console is used to demonstrate the working architecture of Big Data Hadoop on Cloud platform. Focusing on Infrastructure as a service, platform as a Service and Software as a Service in terms of Big Data model.

BDaaS, HDaaS, Big Data, IBM Ambari Console, Cloud Computing, SaaS, IaaS, PaaS

[1] Harshawardhan S. Bhosale, Review Paper on Big Data and Hadoop, International Journal of Scientific and Research Publications, Volume 4, Issue 10, October 2014 1 ISSN 2250-3153
[2] Raghavendra Kune, The anatomy of big data computing, software: practice and experience Softw. Pract. Exper. 2016; 46:79–105 Published online 9 October 2015 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/spe.2374
[3] Santosh Kumar, Cloud Computing – Research Issues, Challenges,Architecture, Platforms and Applications: A Survey International Journal of Future Computer and Communication, Vol. 1, No. 4, December 2012.
[4] A. Kundu, C. D. Banerjee, P. Saha, “Introducing New Services in Cloud Computing Environment”, International Journal of Digital Content Technology and its Applications, AICIT, Vol. 4, No. 5, pp. 143-152, 2010.
[5] 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.
[6] Amol C. Adamuthe, Cloud Computing – A market Perspective and Research Directions, I.J. Information Technology and Computer Science, 2015, 10, 42-53 Published Online September 2015 in MECS (http://www.mecs-press.org/)
[7] VARIA, J.2009. Cloud Architectures. Amazon Web Services.
[8] CHAPPELL, D.2009. Introducing the Azure Services Platform. David Chappell & Associates.
[9] RAYPORT, J. F. and HEYWARD, A.2009. Envisioning the Cloud: The Next Computing Paradigm. Marketspace.
[10] Palaghat Yaswanth Sai, Pabolu Harika, "Illustration of IOT with Big Data Analytics", International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.221-223, 2017.

Ujjwal Agarwal, "Cloud Computing: BDaaS and HDaaS (Big Data as a Service and Hadoop as a Service)," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.131-134, 2017.

The battery life of robots is increasing by harvesting energy from the environment with photovoltaic solar panels. Solar harvesting has proven to be useful in marine and extra-terrestrial robotics applications which take place in open space. This is a far more cost effective solution than purchasing additional solar panels. It has been estimated that the yield from solar panels can be increased by 30 to 60 percent by utilizing a tracking system instead of a stationary array. The system implemented in paper develops an automatic tracking system which will keep the solar panels aligned with the sun in order to maximize efficiency, then after one can add a battery which can be charged through solar panels. The battery is finally connected to a robot which performs the desired activity.

Sensor, CPU, Stepper Motor, Solar Panel

[1] Vijay Raghunathan, Aman Kansal, Jason Hsu, Jonathan Friedman, Mani Srivastava, “Design Considerations for Solar Energy Harvesting Wireless Embedded Systems”, In the Proceedings of the 2005 Information processing in sensor network.
[2] Davide Brunelli, Clemens Moser, Lothar Thiele, Luca Benini, “Design of a Solar-Harvesting Circuit for Batteryless Embedded Systems”. IEEE Transaction, Vol.56, pp.2519-2528, 2009.
[3] Klar Jager, Olindo Isbella, “Solar Energy Fundamental, Technology and system”.
[4] Abi Sharma, “Wireless RF modulu”.

R. Nidhi, "Study and Implemenation of Solar Botics," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.135-140, 2017.

Nowadays our systems need strong security to protect data and resources access from unauthorized persons. For that purpose, biometric-based authentication system provides more security than other approaches. Uni-modal biometric systems use only one biometric trait such as fingerprint, finger vein, voice, face, ear, iris, retina etc., which has some limitations due to noise, spoof attacks etc., The multimodal biometric system is the combination of more than one biometric trait to authenticate an individual. In this paper, a multimodal approach has been proposed by integrating the finger vein and fingerprint to enhance the performance of personal recognition system. Preserving the privacy of stored biometric templates in a centralized database is of more important at present. Visual Steganography provides a very powerful technique by which one secret can be distributed in two or more shares. When the shares on transparencies are superimposed exactly together, the original secret can be discovered without computer participation. In the enrollment procedure, the secret key is encrypted by using AES algorithm and by using the visual steganographic technique, the encrypted secret key is shared between the two images. The share1 is kept as the users` ID card and the share2 is stored in the database. In the verification procedure, new finger vein and fingerprint images are obtained and verified with the images stored in the database. It is computationally hard to obtain the biometric image from any individual stored sheets. This paper explores the possibility of using visual steganography for efficient biometric security in the multimodal approach.

Uni-modal approach, Multimodal approach, Visual Steganography, finger vein, fingerprint, AES

[1] Hatim A. Aboal samh, “A Multi Biometric System using combined Vein and Fingerprint Identification”, International Journal of Circuits, Systems and Signal Processing, pp 29-36, Issue 1, Vol.5, 2011.
[2] T.Sheeba, M. Justin Bernard, “Survey on Multimodal Biometric Authentication Combining Fingerprint and Finger vein”, International Journal of Computer Applications (0975-8887), Vol.51,No. 5, 2012.
[3] Neha Chhabra, “Visual Cryptographic Steganography in Images”, International Journal of Computer Science and Network Security, pp 126-131, Vol.12, No.4, 2012.
[4] K. Sankareswari, S. Arul Jothi, “Hybrid Approach for Securing Biometric Templates Using Visual Cryptography”, International Journal of Advance Research in Computer Science and Management Studies, pp 61-65, Vol.3, Issue 9, 2015.
[5] Jinfeng Yang, Yihua Shi, “Towards Finger Vein Image Restoration and Enhancement for Finger Vein Recognition”, Elsevier, Information Sciences, pp 33-52, 268, 2014.
[6] Wenming Yang, Xiaola Huang, Fei Zhou, Oingmin Liao, “Comparative and competitive coding for personal identification by using finger vein and finger dorsal texture fusion”, Information Sciences, pp 20-32, 268, 2014.
[7] Lu Yang, Gang ping Yang, Yilong Yin, Rongyang Xiao, “Sliding Window-Based Region of Interest Extraction for Finger Vein Images”, Sensors, 2013.
[8] Humairah Hamid, V.K. Narang, Priti Singh, “Review on Vein Pattern Based Biometric Systems”, International Journal of Innovative Research in Science, Engineering and Technology, Vol.6, Issue 5, 2017.
[9] Shaik Riyaz Ulhaq, Shaik Imityaz, Selvakumar, L.Gopinath, “Multimodel Biometric Template Authentication of Fingervein and Signature using Visual Cryptography”, International Journal of Engineering and Techniques, Vol. 3, Issue 3, 2017.
[10] A. L. Kabade, "Canny edge detection algorithm", International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE), 5(5):1292-1295, 2016.
[11] Kayode A. Akintoye, M. Rahim M. Shafry, Abdul Hanan Abdullah, “ A Novel Approach for Finger Vein Pattern Enhancement using Gabor and Canny Edge Detector”, International Journal of Computer Applications (0975 – 8887), Vol. 157, No 2, 2017.
[12] Shanthakumar, Janardhan Naidu, “An Efficient Personnel Authentication Through Multi modal Biometric System”, International Journal of Scientific Engineering and Applied Science (IJSEAS), Vol.2, Issue 1, ISSN: 2395-3470, pp. 534-543, 2016.
[13] Sangeetha Narwal , Daljit Kaur, “Comparison between Minutiae Based and Pattern Based Algorithm of Fingerprint Image”, International Journal of Information Engineering and Electronic Business, 2, pp. 23-29, 2016.
[14] Mohammad Mohsen Ahmadinejad, Elizabeth Sherly, “A Comparative Study on PCA and KPCA Methods for Face Recognition”, International Journal of Science and Research (IJSR), ISSN: 2319-7064, Vol. 5, Issue 6, 2016.
[15] Kondreddi Gopi, J.T. Pramod, “Fingerprint Recognition Using Gabor Filter and Frequency Domain Filtering”, IOSR Journal of Electronics and Communication Engineering, Vol. 2, Issue 6, pp.17-21, 2012.
[16] T. Srinivasa Rao, E. Srinivasa Reddy, “A Multimodal Biometric Authentication Technique using Fused Features of Finger, Palm and Speech”, International Journal of Computer Sciences and Engineering, Vol. 5, Issue 8, E-ISSN:2347-2693, 2017.

P. Anitha, M. Grace, "Multimodal Approach on Finger Vein and Fingerprint by Using Visual Steganography for Efficient Biometric Security," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.141-146, 2017.

Knowledge plays a vital role in every sphere of human life. Data Mining supports to acquire knowledge by discovering pattern / correlations among data. This information is applied in various applications like business, education, social media, medical, Agriculture etc. Data mining field has attained enormous success from its inception to the present level. Also it faces many issues especially while handling social media data. Social media is one of the important sources that provide huge volume of data that are unstructured and heterogeneous. Handling this data is really a very big challenge to the researchers. At present, a number of data mining algorithms and techniques are available with their own merits and demerits. Finding a suitable algorithm for a particular application is a very big challenge. This paper imparts many issues in data mining and also focuses scope of the data mining in social media which will be helpful in the further research.

Data mining, social media, clustering, classification

[1] Hemlata Sahu, “ A Brief Overview on Data Mining Survey” in International Journal of Computer Technology and Electronics Engineering (IJCTEE) Volume 1, Issue 3, PP: 114-121
[2] MohammadNoor Injadat, Fadi Salo, Ali Bou Nassif “Data Mining Techniques in Social Media: A Survey”, NEUCOM17295, Volume 214, PP:654-670, 2016.
[3] Aakanksha Bhatnagar, Shweta P. Jadye, Madan Mohan Nagar” Data Mining Techniques & Distinct Applications: A Literature Review” in International Journal of Engineering Research & Technology (IJERT), Vol. 1, Issue 9, PP:1-3, 2012.
[4] Dinesh Bhardwaj1, Sunil Mahajan2, “ANALYSIS OF DATA MINING TRENDS, APPLICATIONS, BENEFITS AND ISSUES”, in International Journal of Computer Science and Communication Engineering, Volume 5 issue 1, PP:53-57, 2016.
[5] Dr. Poonam Chaudhary , “Data Mining System, Functionalities and Applications: A Radical Review” in International Journal of Innovations in Engineering and Technology (IJIET), Volume 5, Issue 2 ,PP:449-455, 2015
[6] Neelamadhab Padhy1, Dr. Pragnyaban Mishra 2, and Rasmita Panigrahi3, “The Survey of Data Mining Applications And feature scope”, in International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, Issue.3, 2012.
[7] Smita1, Priti Sharma, “ Use of Data Mining in Various Field: A Survey Paper”, in IOSR Journal of Computer Engineering (IOSR-JCE), Volume 16, Issue 3, PP 18-21, 2014.
[8] Mrs. Bharati M. Ramageri,” DATA MINING TECHNIQUES AND APPLICATIONS”, in Indian Journal of Computer Science and Engineering, Vol. 1 Issue. 4, PP: 301-305.
[9] Annan Naidu Paidi “ Data Mining: Future Trends and Applications” in International Journal of Modern Engineering Research (IJMER), Vol.2, Issue.6, PP:4657-4663, 2012.
[10] Umamaheswari. K, S. Niraimathi “A Study on Student Data Analysis Using Data Mining Techniques”, in International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 8, PP:117-120, 2013.
[11] Nikita Jain, Vishal Srivastava “DATA MINING TECHNIQUES: A SURVEY PAPER” in IJRET: International Journal of Research in Engineering and Technology, Volume: 02, Issue: 11, PP:116-119, 2013.
[12] Ranshul Chaudhary1, Prabhdeep Singh2, Rajiv Mahajan3, “A SURVEY ON DATA MINING TECHNIQUES” in International Journal of Advanced Research in Computer and Communication Engineering, Vol. 3, Issue 1, PP:5002-5003, 2014.
[13] S.G.S Fernando et.al “Empirical Analysis of Data Mining Techniques for Social Network Websites” in COMPUSOFT, An international journal of advanced computer technology, Volume-III, Issue-II PP:582-592, 2014.
[14] M. Vedanayaki*, “A Study of Data Mining and Social Network Analysis” in Indian Journal of Science and Technology, Vol 7(S7), PP:185–187, 2014.
[15] Raj Kumar “Classification Algorithms for Data Mining: A Survey” in International Journal of Innovations in Engineering and Technology (IJIET) Vol. 1, Issue 2, PP: 7-14, 2012.
[16] S.Neelamegam.”Classification algorithm in Data mining: An Overview” in International Journal of P2P Network Trends and Technology (IJPTT), Volume 4, Issue 8, PP:369 – 374, 2013
[17] Adedoyin-Olowe, M., Gaber, M. M., & Stahl, F, “A Survey of Data Mining Techniques for Social Media Analysis” in Journal of Data Mining & Digital Humanities, PP:1-27, 2014.
[18] Thabit Zatari, “ Data Mining in Social Media “ in International Journal of Scientific & Engineering Research, ISSN 2229-5518 Volume 6, Issue 7, PP:152-154, 2015.
[19] Dr.B.Umadevi1, P.Surya2, “A Review on Various Data Mining Techniques in Social Media”, in International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5, Issue 4, PP: 8082-8086, 2017.
[20] Rahman, M. M, “Mining Social Data to Extract Intellectual Knowledge”, in International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.10, PP:15-24, 2012.
[21] XimingWang • Panos M. Pardalos,” A Survey of Support Vector Machines with Uncertainties”, © Springer-Verlag Berlin Heidelberg 2015, Ann. Data. Sci. (2014) 1(3–4) PP:293–309, 2014
[22] MISS. NAZNEENTARANNUM S. H. RIZVI, “A SYSTEMATIC OVERVIEW ON DATA MINING: CONCEPTS AND TECHNIQUES” in International Journal of Research in Computer & Information Technology (IJRCIT), Vol. 1, Special Issue 1, PP:136-139, 2016.
[23] Anmol Kumar1, Amit Kumar Tyagi2, Surendra Kumar Tyagi3, “Data Mining: Various Issues and Challenges for Future :A Short discussion on Data Mining issues for future work”, in International Journal of Emerging Technology and Advanced Engineering, Volume 4, Special Issue 1, PP:1-8, 2014.
[24] Dipti Verma and Rakesh Nashine,” Data Mining: Next Generation Challenges and Future Directions” in International Journal of Modeling and Optimization, Vol. 2, No. 5, PP: 603-608, 2012.
[25] Sagar S. Nikam, “ A Comparative Study of Classification Techniques in Data Mining Algorithms” in ORIENTAL JOURNAL OF COMPUTER SCIENCE & TECHNOLOGY, Vol. 8, No. (1): PP: 13-19, 2015
[26] B.R. Patel, "Comparative analysis of classification algorithm in EDM for improving student performance", International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.171-175, 2017.
[27]. Nesma Settouti, Mohammed El Amine Bechar and Mohammed Amine Chikh, “Statistical Comparisons of the Top 10 Algorithms in Data Mining for Classification Task”,in International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 4, No.1, PP:46-51, 2016
[28] GemaBello-Orgaza,JasonJ.Jungb,∗,DavidCamacho, “Social bigdata: Recent achievements and new challenges”, http://dx.doi.org/10.1016/ j.inffus.2015.08.005 1566-2535/© 2015 Elsevier,
[29] Shweta Verma, Vivek Badhe, “ Survey on Big Data and Mining Algorithm”, IN IJSRSET, , Volume 2 | Issue 2 | , PP: 1338-1344, 2016.
[30] Dr.M.Chidambaram, R.Umasundari, “A Survey on Feature Selection in Data Mining”, in International Journal of Innovative Research in Computer Science & Technology (IJIRCST) Volume-4, Issue-1,
PP: 13 -14, 2016
[31] Sunny Sharma, “A Study on Data Mining Horizons”, in International Journal of Recent Trends in Engineering & Research (IJRTER), Volume 02, Issue 04; PP: 322-326, 2016.
[32] VAISHALI SARATHY, 2SRINIDHI.S, 3KARTHIKA.S, “SENTIMENT ANALYSIS USING BIG DATA FROM SOCIALMEDIA”, in Proceedings of 23rd IRF International Conference, PP: 40 -45, 2015, Chennai, India.
[33] Parmeet Kaur, “ AN OVERVIEW OF DATA MINING TOOLS”, in International Journal of Engineering Applied Sciences and Technology, Vol. 1, Issue 6, PP: 41-46, 2016.
[34] G Nandi1, A Das1 & 2, “ ONLINE SOCIAL NETWORK MINING: CURRENT TRENDS AND RESEARCH” in IJRET: International Journal of Research in Engineering and Technology ,Volume: 03 Issue: 04, PP: 346 – 350, 2014.
[35] Wei Fan “Mining Big Data: Current Status, and Forecast to the Future” SIGKDD Explorations Volume 14, Issue 2, PP:1-5
[36] H. K. Chan1, E. Lacka2, R. W. Y. Yee3, M. K. Lim4, “A Case Study on Mining Social Media Data”, in the Proceedings of the 2014 IEEE IEEM, 978-1-4799-6410-9/14/$31.00 ©2014 IEEE , PP: 593- 596
[37] Dave King JDA, “Introduction to the Mining and Analyzing Social Media Minitrack”, in the proceedings of the 46th Hawaii International Conference on System Sciences, PP :3108-3110, 2013
[38] Albert Ching-man Au Yeung and Tomoharu Iwata, “Research on Social Network Mining and Its Future Development” in Feature Articles: Communication Science Reaches Its 20th Anniversary, NTT Communication Science Laboratories, Soraku-gun, 619-0237 Japan, Vol. 9 No. 11, PP:1-4, 2011.
[39] Vidya Shree S I1, Pooja M R2, “ A Review on Data Extraction using Web Mining Techniques”, in International Journal of Advanced Research in Computer and Communication Engineering, Vol. 5, Issue 4, PP :194-197, 2016.
[40] Mosley Jr, R. C, “Social media analytics: Data mining applied to insurance Twitter posts” in Casualty Actuarial Society E-Forum, Winter, vol 2 (p. 1).2012
[41] David Jensen and Jennifer Neville, “Data Mining in Social Networks”, Papers of the Symposium on Dynamic Social Network Modeling and Analysis. National Academy of Sciences,Washington, DC: National Academy Press. PP:1-13, 2002.
[42] Shubhie Agarwal, Seema Maitrey, Pankaj Singh Yadav, "A Comparative Analysis of Data Mining Techniques in Wireless Sensor Network", International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.126-131, 2016.
[43] David Heckerman, “Bayesian Networks for Data Mining” in Data Mining and Knowledge Discovery, Volume 1, Issue 1, 1997.
Spring 2016
[44] A. Akay, A. Dragomir, “A Novel Data-Mining Approach Leveraging Social Media to Monitor Consumer Opinion of Sitagliptin”, IEEE J. Biomed. Heal. INFORMATICSournal Biomed. Heal. Informatics. PP:389–396. 2015
[45] Cong Liao1, Anna Squicciarini1, Christopher Griffin2, Sarah Rajtmajer, “A hybrid epidemic model for deindividuation and antinormative behavior in online social networks” - Soc. Netw. Anal. Min. (2016) 6:13 DOI 10.1007/s13278-016-0321-5
[46] Bogdan Batrinca • Philip C. Treleaven, “Social media analytics: a survey of techniques, tools and platforms”, Published online: 26 July 2014 with open access at Springerlink.com, AI & Soc (2015) 30, PP: 89–116, DOI 10.1007/s00146-014-0549-4
[47] Arif Nurwidyantoro,” Event Detection in Social Media: a Survey”, Published in: ICT for Smart Society (ICISS), 2013 International Conference, Date Added to IEEE Xplore: 03 September 2013, INSPEC Accession Number: 13735772.
[48] 1Ms. Pranjali S. Jadhav, 2Dr. Shirish S. Sane, “UNDERSTANDING STUDENTS’ LEARNING EXPERIENCE BY DATA MINING OF SOCIAL MEDIA”, VOLUME-3, ISSUE-4, PP:23-30, 2016
[49] Daljeet Kaur Ȧ *and Aman Paul Ȧ, “ Performance Analysis of Different Data mining Techniques over Heart Disease dataset”, in International Journal of Current Engineering and Technology, Vol.4, No.1, PP: 220- 224, 2014.
[50] K. Jayasudha, “AN OVERVIEW OF DATA MINING IN ROAD TRAFFIC AND ACCIDENT ANALYSIS”, in Journal of Computer Applications, Vol – II, No.4, PP:32-37, 2009.
[51] P.Veeramuthu, “Application of Higher Education System for Predicting Student Using Data mining Techniques”, in International Journal of Innovative Research in Advanced Engineering (IJIRAE) ,Volume 1 Issue 5 , PP: 36-38, 2014.
[52] Mohamed Yassine,” A Framework for Emotion Mining from Text in Online Social Networks”, in the proceedings of 2010 IEEE International Conference on Data Mining Workshops, PP: 1136 -1142, 2010
[53] Aarti Sharma, Rahul Sharma,Vivek Kr. Sharma,Vishal Shrivatava, ” Application of Data Mining – A Survey Paper”, in (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2) , PP: 2023-2025, 2014.
[54] R.Adaikalam, “ A Survey on Data Mining Techniques for Analysis of Social Network” in International Journal of Advance Research in Computer Science and Management Studies, Volume 4, Issue 3, PP :65-70, 2016.

C.Amali Pushpam, J.Gnana Jayanthi, "Over view on Data Mining in Social Media," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.147-157, 2017.

In recent times Security has started to be the key factor in data transmission. Recent advances in networking and wireless sensing has enabled the discovery in networking and wireless sensing and has enabled the discovery of new algorithms and techniques for wireless sensor networks. A Wireless Sensor Network (WSN) comprises several sensor nodes such as magnetic, thermal, and infrared and the radar is setup in a particular geographical area. The capabilities of WSN include to manipulate and control the physical and environmental entities such as – humidity, temperature, sound, pressure, light etc. and pass this information to various other sensors present in the network in order to pass the information from the source to the sink. These wireless sensor networks have diverse applications ranging from medical care to military or educational purposes but these networks are also prone to many adversaries and attacks. Some of the most common attacks on a wireless sensor network are spoofing or replayed routing information. Certain techniques and algorithms have been introduced or developed which might not make a WSN attack-proof in all situations but may be very affective in certain situations. Selective forwarding attack is one of the most harmful attack as it can harm the complete network. A selective forwarding attack is a type of attack in which the nodes capture some data by interfering in the transmission path and steal some precious information which could be anything from secret passwords to encrypting keys and pass the rest to the destined node. The ability of capturing the required data and passing the rest of the information to the sink makes it undetectable in a network. In WSN certain techniques and algorithms have been introduced to detect selective forwarding attacks.

Authentication; Privacy; Sybil; Cryptographic Methodologies; Wireless Sensor Network; WSN; Sensor; Limitations; Sink Hole; Black Hole; Selective Forwarding Attack.

[1] Shafiqul Abidin “WSN : Confidentiality, Integrity, Authenticity and Freshness (CIAF)”.
[2] Geethu P C,Rameez Mohammed A.”Defence Against Selective Forwarding Attack in Wireless Sensor Networks”.IEEE-2013,4th ICCCNT-2013,july 4-6,Tiruchengode, India.
[3] Naser M Alajmi, Khaled M. Elleithy.”Selective Forwarding Detection(SFD) in Wireless Sensor Networks”.
[4] Binod Kumar Mishra,Mohan C. Nikam,Prashant Lakkadwala.”Security Against Black Hole Attack In Wireless Sensor Network-A Review”. 2014 Fourth Conference on Communication Sustems And Network Technologies”IEEE-2014.
[5] V. Subramonian, H-M. Huang, S. Datar, and C. Lu, “Priority Scheduling in tinyos case study,”.Department of Computer Science,Washington University, St. Louis. MO.
[6] Shafiqul Abidin, “A Novel Construction of Secure RFID Authentication Protocol”, International Journal of Security, Computer Science Journal, Malaysia, Vol. 8, Issue 8, pp33-36, October 2014.
[7] S.Slijepcevic, M. Potkonjak, V. Tsiatsis, S. Zimbeck, M.B. Srivastava, “On communication security in wireless adhoc sensor networks,” in proceedings of 11th IEEE International Workshop on Enablimg Technologies: Infrasturcture for Collaborative Enterprises(WETICE’02),2002,pp.139-144.
[8] Mayank Saraogi, “Security In Wireless Sensor Networks “, University of Tennessee, Knoxville.
[9] Abhishek Jain, Kamal Kant and M. R. Tripathy, “Security Solutions for Wireless Sensor Networks” 2012, Second International Conference on Advanced Computing & Communication Technologies.
[10] I. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, “Wireless sensor networks: A survey”. Computer Networks, 38(4):393-422.
[11] P. Sengar, N. Bhardwaj, "A Survey on Security and Various Attacks in Wireless Sensor Network", International Journal of Computer Sciences and Engineering, Vol.5, Issue.4, pp.78-84, 2017.
[10] J. Hill , R Szewczyk, A. Woo, S. Hollar, D.E. Culler, K. Pister, “System architecture directions for networked sensos”. Proceedings of 9th International Conference On Architectural Support for Programing Languages and Operating Systems, New York, ACM Press 2000,pp 93-104.
[12] Manu Ahuja and Shafiqul Abidin “Performance Analysis of Vehicular Ad-hoc Network”, International Journal of Computer Applications, USA, Vol 151 - No. 7, pp 28-30, October 2016.
[13] Bulbenkiene, V., Jakovlev, S., Mumgaudis, and G., Priotkas, G., “Energy loss model in Wireless Sensor Networks,” IEEE Digital Information Processing and communication (ICDIPC), 2012 Second International conference, PP 36-38, 10-12 July 2012.

Shafiqul Abidin, Mohd Izhar, "Attacks on WSN and its Limitations," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.158-161, 2017.

Wireless sensor network has become a leading area of research because of its various applications such as environmental monitoring, target detection and tracking, health monitoring, industrial process monitoring, energy efficiency, disaster management and military security system. In a Wireless sensor network, the capacity of a battery of small size sensors is restricted and once the sensor nodes work, it consumes extra energy which is depending on the batteries. Sensor node performs the operation of computation and communication through which consumption of energy get exhausted at the faster rate. Energy efficiency is an important requirement in a wireless sensor network. Most of the energy is consumed in the communication part of the sensor node. Medium access control protocol plays an important role in energy consumption for wireless sensor network. MAC protocols have the capability to maintain real time functionality and achieve energy efficiency. The S-MAC protocol is used for increasing sleep duration, overhearing and decreasing idle listening, the collision of packets or eliminating hidden terminal problem which is a help to increase energy efficiency. The aim of this paper is to study S-MAC protocol in wireless sensor network. The objective of this work is to find the best-suited performance of S-MAC protocol in the term of energy efficiency, throughput, latency, etc. in wireless sensor network.

Wireless Sensor Network, S-MAC Protocol, Energy Efficiency

[1]. Deepika Bishnoi1, Dr. Sunil Nandal, “A Comparative Study of Contention-Based Mac Protocol for Wireless Sensor Network”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), Vol.5, Issue VI, June 201.
[2]. Bhavana Narain, Anuradha Sharma, Sanjay Kumar and Vinod Patle, “Energy Efficient MAC Protocol for Wireless Sensor Network: A Survey,” International Journal of Computer Science & Engineering Survey (IJCSES), Vol. 2, No.3, August 2011.
[3]. Meghan GUNN, Simon G. M. KOO, “A Comparative Study of Medium Access Control Protocols for Wireless Sensor Networks”, Int. J. Communications, Network and System Sciences, vol. 2, September 2009.
[4]. Wei Ye, John Heideman and Deborah Estrin, “Medium Access Control With Coordinated Adaptive Sleeping for Wireless Sensor Networks,” IEEE. ACM Transaction on Networking, Vol. 12, No. 3, June 2004.
[5]. Awatef Balobaid, “A Survey and Comparative Study on Different Energy Efficient MAC-Protocols for Wireless Sensor Networks,” International Conference on Internet of Things and Applications (IOTA) Maharashtra Institute of Technology, Pune, India 22 - 24 January, 2016.
[6]. Moshaddique Al Ameen, S. M. Riazullslam and Kyungsup Kwak, “Energy Saving Mechanism for MAC Protocols in Wireless Sensor Networks,” International Journal of Distributed Sensor Networks, October 2010.
[7]. Wei Ye, John Heidemann, Deborah Estrin, “An Energy-Efficient MAC Protocol for Wireless Sensor Networks,” Twenty-First Annual Joint Conferences of the IEEE Computer and Communications Societies Proceedings. IEEE. Vol. 3, 2002.
[8]. Thibault Bernard and Hac`ene Fouchal, “A Low Energy Consumption MAC Protocol for WSN,” Ad-hoc and Sensor Networking Symposium, IEEE. 2012.
[9]. Subhasis Dash, Amulya Ratna Swain, Anuja Ajay, “Realible energy Aware Multi Token Based MAC Protocol for WSN,” 26th IEEE. International Conference on Advanced Information Networking and Applications, 2012.
[10]. Khalil F. Ramadan, M. I. Dessouky, Mohammed Abd-Elnaby, Fathi E. Abd El-Samie, “Energy–Efficient Dual-Layer MAC Protocol with Adaptive Layer Duration for WSNS,” IEEE. Vol.16, 2016.
[11]. P.T.Kalaivaani and A.Rajeswari, “An Energy Efficient analysis Of S-MAC and H-MAC Protocols for Wireless Sensor Networks,” International Journal of Computer Networks & Communications (IJCNC) Vol.5, No.2, March 2013.
[12]. U. Korupolu, S. Kartik, GK. Chakravarthi, "An Efficient Approach for Secure Data Aggregation Method in Wireless Sensor Networks with the impact of Collusion Attacks", International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.3, pp.26-29, 2016.
[13]. Rachid Aouami, Mounir Rifi, Mohamed Ouzzif, “Comparative analysis of contention Oriented Power Saving based medium access control Protocols for Wireless Sensor Networks,” IEEE. 2004.
[14]. Chiara Buratti, Andrea Conti, Davide Dardari, and Roberto Verdone, “An Overview on Wireless Sensor Networks Technology and Evolution”, Sensor, Vol. 9, 2009.
[15]. Anitha K, Usha S,“A Scheduled Based MAC Protocols for Wireless Sensor Network: A Survey”, International Journal of Advanced Networking & Applications.
[16]. Kazem Sohraby, Daniel Minoli, and Taieb Znati, “Wireless Sensor Networks Technology, protocol, and Applications, Wiley-Interscience, 2007.
[17]. 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.

Deepika Bishnoi, Sunil Kumar Nandal, "Performance Analysis of S-MAC Protocol in Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.162-166, 2017.

With the rising popularity of location-based services, trajectory data mining became an important research topic. There exists many data mining algorithms for systematic processing, managing and mining of trajectory data. Trajectory data mining has many applications such as location recommandations, social link prediction, movement behaviour analysis etc. Here proposes a contextual trajectory analysis model which provides a flexible way to characterize the complex moving nature of humans. It embed multiple contextual information for efficiently modeling data. It includes user-level, trajectory-level, location-level, and temporal-level contexts. It can be used to predict the future location of a user based on the previous travelling pattern. Social link prediction aims to find out whether there exists reciprocal link between two users. Here also propose a method for social link prediction from trajectory data by analyzing the nearest neighbour. This method considers the tf-idf metrics as the baseline.

Trajectory,contextual information,social link prediction

[1]Ningnan Zhou, Wayne Xin Zhao, Xiao Zhang, Ji-RongWen, Shan Wang, “A General Multi-Context Embedding Model for Mining Human Trajectory Data” IEEE Trans on Knowledge and Data Engineering , Vol. 28, No. 8, pp.1945-1958, 2016.
[2] H. Pham, C. Shahabi, and Y. Liu, “Ebm: An entropy-based model to infer social strength from spatiotemporal data,” in Proc. ACM SIGMOD Int. Conf. Manage. Data, pp. 265–276, 2013.
[3] Q. Li, Y. Zheng, X. Xie, Y. Chen, W. Liu, and W.-Y. Ma, “Mining user similarity based on location history,” in Proc. Annu. ACM Int. Symp. Adv. Geographic Inf. Syst., pp. 34:1–34:10, 2008.
[4] X. Xiao, Y. Zheng, Q. Luo, and X. Xie, “Finding similar users using category-based location history,” in Proc. Annu. ACM Int. Symp. Adv. Geographic Inf. Syst., pp. 442–445, 2010.
[5]E. Cho, S. A. Myers, and J. Leskovec ,” Friendship and mobility: User movement in location-based social networks”, in Proc. ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, pp. 1082– 1090, 2011.
[6] B. Perozzi, R. A.-Rfou, and S. Skiena, “Deepwalk: Online learning of social representations,” in Proc. ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, pp. 701–710, 2014.
[7] D. Wang, D. Pedreschi, C. Song, F. Giannotti, and A.-L. Barabasi, “Human mobility, social ties, and link prediction,” in Proc. ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, pp. 1100–1108, 2011.
[8] W. Mathew, R. Raposo, and B. Martins, “ Predicting future locations with hidden markov models,” in Proc. Int. Joint Conf. Pervasive Ubiquitous Comput., pp. 911–918, 2012.
[9] G.Sivaiah, P.K.Rao,”A comprehensive survey on providing efficient directions using GPS and driver’s ability”, International Journal of Computer Sciences and Engineering, Volume-2, pp. 79-82, 2014.

Revathy S B, Remya R, "Social link prediction using category based location history in trajectory data," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.167-170, 2017.

Area based administrations are rapidly winding up monstrously famous. Notwithstanding administrations in light of clients` present area; numerous potential administrations depend on clients of their area history. “Harmful clients may lie about the spatial-impermanent provenance without an effectively masterminded security structure for clients to display their past zones. In this paper, I demonstrate the Spatial-Fleeting provenance Confirmation with Common Evidences (STAMP) conspires”. STAMP is intended for specially appointed portable clients that are producing area proofs for each other in a disseminated setting. In any case, it can stretch much more without trusted versatile clients and remote access focuses. “STAMP ensures the uprightness and non-transferability of the territory proofs and guarantees customers` security. A semi-trusted Confirmation Specialist is used to pass on cryptographic keys in extension which screen the clients against intrigue by a light-weight entropy-based trust in examination air”. This model of execution on the Android stage demonstrates that STAMP is effortlessness as far as computational and capacity assets. Broad recreation tests demonstrate that entropy-based trust display can accomplish high conspiracy discovery precision.

Location proof, privacy, spatial-temporal provenance, trust.

[1] S. Saroiu and A. Wolman, "Empowering new versatile applications with area proofs," in Proc. ACM HotMobile, 2009, Art. No. 3.
[2] W. Luo and U. Hengartner, "VeriPlace: A security mindful area verification engineering," in Proc. ACM GIS, 2010, pp. 23– 32.
[3] Z. Zhu and G. Cao, "Towards security saving and intriguing protection in area verification refreshing framework," IEEE Trans. Versatile Comput., vol. 12, no. 1, pp. 51– 64, Jan. 2011.
[4] N. Sastry, U. Shankar, and D. Wagner, "Secure check of area claims," in Proc. ACM WiSe, 2003, pp. 1– 10.
[5] Y. Desmedt, "Significant security issues with the `unforgeable` (feige)- fiat-shamir verifications of personality and how to defeat them," in Proc. SecuriCom, 1988, pp. 15– 17.
[6] B. Waters and E. Felten, "Secure, private verifications of area," Department of Computer Science, Princeton University, Princeton, NJ, USA, Tech. Rep., 2003.
[7] X. Wang et al., "STAMP: Ad hoc spatial-worldly provenance affirmation for portable clients," in Proc. IEEE ICNP, 2013, pp. 1– 10.
[8] A. Pfitzmann and M. Köhntopp, "Secrecy, imperceptibility, and pseudonymity-a proposition for wording," in Designing Privacy Enhancing Technologies. New York, NY, USA: Springer, 2001.
[9] D. Singelee and B. Preneel, "Area confirmation utilizing secure separation bouncing conventions," in Proc. IEEE MASS, 2005.

S. Harika, Renuka Kondabala, "Secure Verification of Location Claims for Mobile Users," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.171-176, 2017.