Big Data Analytics and its Tools
Research Paper | Journal Paper
Vol.7 , Issue.4 , pp.876-880, Apr-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i4.876880
Abstract
In current scenario the Big Data has a rapid growth and has become a most popular term in the world of internet. The size of generated data is so huge and complex that traditional data processing application tools and platforms are inadequate to deal with it. This article is a result of a systematic analysis that discusses Big Data concepts and applications in various domains. The goal is to explore and understand the current research, opportunities, and challenges relating to the utilization of Big Data and analytics. The contribution of this paper is to provide an analysis of the available literature on big data analytics. Accordingly, some of the various big data tools, methods, and technologies which can be applied are discussed, and also shows the strength and limitations of Hadoop and HPCC systems based on some specific criteria.
Key-Words / Index Term
Bigdata, Bigdata Analytics, IoT.
References
[1]Shilpa, Manjit Kaur,” BIG Data and Methodology-A review”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, PP.991-995, October 2013.
[2]Smorodin, G, “Big Data-driven world needs Big Data-driven ideology”, Big Data as the Big Game Changer, PP.991-995,2015.
[3]Mrs. Mereena Thomas,” A Review paper on BIG Data”, International Research Journal of Engineering and Technology (IRJET), Volume: 02,PP.1030-1034, Dec-2015.
[4]Kevin Taylor-Sakyi,” Understanding Big Data”, https://www.researchgate.net/publication/291229189,PP.01-08, 13 June 2016.
[5]AK Bharti, Neha Verma, Deepak Kumar Verma, "A Review on Big Data Analytics Tools in Context with Scalability", International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.273-277, 2019. DOI: https://doi.org/10.26438/ijcse/v7i2.273277
[6]Aditya B. Patel, Manashvi Birla, Ushma Nair,“Addressing Big Data Problem Using Hadoop and Map Reduce” PP.01-21,6-8Dec,2012.
[7]AK Bharti, Rashmi Negi, Deepak Kumar Verma, "A Review on Performance Analysis and Improvement of Internet of Things Application", International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.367-371, 2019. DOI: https://doi.org/10.26438/ijcse/v7i2.367371
[8]Xiaomeng Su,”Introduction to Big Data”,Institutt for informatikk og e-loering ved NTNU,PP.01-11.
[9]Sun, D., G. Zhang, S. Yang, Zheng W., S. U.Khan and K. Li, “Re-stream: Realtime and Energy-efficient Resource Scheduling in Big Data Stream Computing Environments”, Information Sciences, No. 319, pp. 92-112, 2015.
[10]Liu, X., N. Iftikhar and X. Xie, “Survey of Real-Time Processing Systems for Big Data”, 18th Int. Database Engineering and Applications Symposium, New York, pp. 356–361, USA, 2014
[11]Bakshi, K,”Considerations for big data: Architecture and approach” 2012.
Citation
Deepak Kumar Verma, Ashakti, "Big Data Analytics and its Tools," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.876-880, 2019.
Review on Various Face Artefact Detection Mechanism
Review Paper | Journal Paper
Vol.7 , Issue.4 , pp.881-887, Apr-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i4.881887
Abstract
To automatically recognition of face is wide utilized in a few applications like confirmation of portable payment. Programmed face recognition has raised issues concerning face artefact detection (biometric sensor introduction assaults), in which a photo or video of an approved individual`s face will be utilized to pick up access. There are assortments of face attack discovery strategies are proposed, their speculation capacity has not been sufficiently tended to. The goal of this paper is to review and recognize various face attack detection ways and to sort them into entirely unexpected classes.
Key-Words / Index Term
face attack, image processing, DCT
References
[1]. Aggarwal, A. & Verma, M.K., 2016. Multimodal Biometric Systems – A Survey. International Journal of Advanced Research in Computer Science and Software Engineering, 6(3), pp.437–441.
[2]. Ahuja, M.S. & Chabbra, S., A Survey of Multimodal Biometrics. International journal of Computer Science and its Applications, pp.157–160.
[3]. Das, T.K. & Bhunre, P.K., 2015. LNCS 8956 - A Secure Image Hashing Technique forattack Detection. , pp.335–338.
[4]. Farid, H., 2009. Imageattack Detection [. , (March), pp.16–25.
[5]. Furon, T., 2005. A Survey of Watermarking Security. , pp.201–215.
[6]. Gopal, N. & Selvakumar, R.K., 2016. Multimodal Biometric Identification System - An Overview. International Journal of Engineering Trends and Technology (IJETT), 33(7), pp.351–355.
[7]. Gupta, P., 2012. Cryptography based digital image watermarking algorithm to increase security of watermark data. , 3(9), pp.1–4.
[8]. Imran, M. & Ghafoor, A., 2012. A PCA-DWT-SVD based Color Image Watermarking. , pp.1147–1152.
[9]. Kaushik, R., Kumar, R. & Mathew, J., 2015. On Imageattack Detection Using Two Dimensional Discrete Cosine Transform and Statistical Moments. , 70, pp.130–136.
[10]. Ma, Z., 2017. Digital Rights Management : Model , Technology and Application. , pp.156–167.
[11]. Mane, P.V.M., Review of Multimodal Biometrics : Applications , challenges and Research Areas. , 3(5), pp.90–95.
[12]. Oommen, R.S., A Survey of Faceattack Detection Techniques for Digital Images.
[13]. Ozdemir, S., 2007. Secure and Reliable Data Aggregation for. , pp.102–109.
[14]. Panchal, T., 2013. Multimodal Biometric System. International Journal of Advanced Research in Computer Science and Software Engineering, 3(5), pp.1360–1363.
[15]. Rani, S., 2015. Available Online at www.ijarcs.info Watermarking using DWT and PCA. , 6(6), pp.117–120.
[16]. Self-embedding, W. et al., 2013. Efficient Method for Content Reconstruction. , 22(3), pp.1134–1147.
[17]. Shaikh, J., 2016. Review of Hand Feature of Unimodal and Multimodal Biometric System. International Journal of Computer Applications, 133(5), pp.19–24.
[18]. Sheikh, Z.G. & Thakare, V.M., 2016. Wavelet Based Feature Extraction Technique for Face Recognition and Retrieval : A Review. , pp.49–54.
Citation
Amanpreet, Anil Kumar, "Review on Various Face Artefact Detection Mechanism," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.881-887, 2019.
Information Technology in Education Sector in Jammu and Kashmir
Review Paper | Journal Paper
Vol.7 , Issue.4 , pp.888-889, Apr-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i4.888889
Abstract
With the development of technology and increase in knowledge of society, our state requires learning skills that could help it to hold pace with the development of technology. Educational structures in a community and consequently schooling will not be capable of becoming independent from other social institutions. Education within the twenty-first century is the middle from which all changes and trends arise. Information generation in schooling desires a tradition. This change needs to be found at the side of the use of hardware assets. The machine wishes to be knowledgeable to use facts technology; otherwise, purchase and switch of era and funding could be nothing but losing sources. Although these technologies aren`t unbiased in any experience they need to be used as method for communicating records, in the present social structures. However, because the procedure of change and transformation is within the nature of human social establishments, the academic device is also vulnerable to a few changes. But the essential trouble is that what techniques ought to be adopted so that schooling systems in developing countries do not only follow advanced international locations but develop and progress base on their very own needs within the course of development. In this paper, after explanation about the role of Information Technology and its place in training in underdeveloped states of India, Jammu and Kashmir, a discussion is presented on a way to input the sector of facts society and the way to use Information technology.
Key-Words / Index Term
Information Technology, academic, hardware, development and Jammu and Kashmir.
References
1. M.G. Kelly and M.C Anear. national educational technology standards for teachers, preparing teachers to usetechnology. Eugene, OR: International society for technology in educational (ISTE).2002.
2. Daintith ,John, ed.(2009),”IT”,A dictionary of physics ,Oxford University Press ,ISBN 9780199233991,retrieved 1 August 2012.
3. Butler,Jeremy G.,A History of information technology and systems, University of Aizona, retreived 2 august 2012.
4. R.C.Mishra. management of educational research, India: kulbhushannangia (APH Publishing corporation).2005.372 F. Hamidi et al. / Procedia Computer Science 3 (2011) 369–373Author name / Procedia Computer Science 00 (2010) 000–000.
5. M. Ataran, globalization.information technology and training. Institute for Cultural Research, aftabemehr,tehran,p.23.2002.
6. I.Jung. ICT-Pedagogy integration in teacher traning: application cases worldwide;Educational society 94-101.
7. G.Beauchamp and J.Parkinson. Publis attitudes towards school science as they transfer from an ICT-rich primaryschool to a secondary school with fewer ICT resources: Does ICT matter? Published online: 3 january 2008# springer science + Business media, LLc 2007.
Citation
Naira shah1 and Wasim Akram Zargar, "Information Technology in Education Sector in Jammu and Kashmir," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.888-889, 2019.
Mobile Apps Revolutinizing Indian Agriculture
Research Paper | Journal Paper
Vol.7 , Issue.4 , pp.890-893, Apr-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i4.890893
Abstract
Agribusiness is one of the primary source of income throughout the world. In fact, we all depend on agriculture directly or indirectly. Communication and Information matters a lot in the development of any field, In agriculture also right Information at the right time matters a lot to the farmers. Modern ICT tools have fulfilled this dream. Smartphone is one of the most popular modern ICT tool that is playing a strategic role in the development of modern farmer. Smartphone and its applications has come with great innovations. Several mobile applications have been developed by government, private companies and non government organization to help farmers to reduce stress, acquire relevant information on good agriculture practices, weather, quality input, markets tendency, etc. This study focuses on how techie our Indian farmers are, and how mobile apps are helping them in easing their daily farm related tasks and increasing their income.
Key-Words / Index Term
Mobile apps , Modern ICT , Digital agriculture
References
[1] S. Mittal, "Modern ICT for Agricultural Development and Risk Management in Smallholder Agriculture in India". International Maize and Wheat Improvement Center(CIMMYT),pp.10-11,2012
[2] https://www.itu.int/en/ITUD/Statistics/Pages/stat/default.aspx
[3] https://news.itu.int/itu-statistics-leaving-no-one-offline/
[4] https://main.trai.gov.in/sites/default/files/PR_No.13of2019_0.pdf
[5] C. Qiang, S. Kuek, A. Dymond "Mobile Applications for Agriculture and Rural Development". Retrieved from World Bank, ICT sector Unit,pp. 7-8,2012
[6] World Bank, "ICT for sustainable agriculture" ,pp.8-9,2013
[7] https://abcofagri.com/use-technology-agriculture-agriculture-mobile-applications/
[8] https://yourstory.com/mystory/e374fa4df7-top-5-best-android-app
[9] https://krishijagran.com/agripedia/10-mobile-apps-for-the-farmers/
[10] R.L. Meena, B. Jirli, M. Kanawat, N.K. Meena "Mobile Applications for Agriculture and Allied Sector", International Journal of Current Microbiology and Applied Sciences,vol 7, https://doi.org/10.20546/ijcmas.2018.702.281
[11] https://play.google.com/store/apps/details?id=com.criyagen
[12] https://play.google.com/store/apps/details?id=com.IFFCOKisan
[13] https://play.google.com/store/apps/details?id=com.agrimedia
[14] https://play.google.com/store/apps/details?id=com.rml.Activities
[15] https://play.google.com/store/apps/details?id=com.purplechai.admin.kissanyojnaapp
[16] https://play.google.com/store/apps/details?id=in.cdac.bharatd.agriapp&hl=en_IN
[17] https://play.google.com/store/apps/details?id=com.app.khetibadi
[18] https://play.google.com/store/apps/details?id=com.mixorg.krishidarshan.activities
[19] https://play.google.com/store/apps/details?id=in.farmguide.farmerapp.central
[20] https://play.google.com/store/apps/details?id=in.gov.enam
[21] C. Dwivedi, “Leveraging the growth of the nation by upliftment of its rural counterpart – An ICT based approach”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1197, 2018.
[22] M.B. Chandak, "Role of ICT in developing Smart Agriculture Systems: Digital India Initiatives", International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.124, 2018
Citation
Ranjita Rathore, Manju Mandot, "Mobile Apps Revolutinizing Indian Agriculture," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.890-893, 2019.
A Framework for Efficient Healthcare Resources Utilization using Semi-supervised Machine Learning Algorithm
Research Paper | Journal Paper
Vol.7 , Issue.4 , pp.894-900, Apr-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i4.894900
Abstract
Electronic Health Records are providing high amount of genetic data and clinical information through the exceptional advances in biotechnology and health sciences. The application of machine learning and data mining methods in biosciences is crucial, more than that very important to transform cleverly all available information into precious knowledge. Diabetes mellitus is defined as a collection of metabolic disorders exerting major pressure on human health worldwide. Large amounts of data generated due to the widespread researches in all areas of diabetes. This study is to present a systematic approach of the applications of machine learning algorithm along with data mining techniques and tools in the field of diabetes research especially in Health Care Resource Utilization (HCRU). There were so many machine learning algorithms used here. Supervised machine learning algorithm, unsupervised machine learning algorithm and Semi-supervised Machine Learning Algorithm (SMLA).This research shows that SMLA such that Transductive Support Vector Machine (TSVM) fits the best for the research in healthcare resource utilization by considering the type of diabetes patient’s medical datasets.
Key-Words / Index Term
Diabetes mellitus, Machine Learning, Healthcare resource utilization, Support Vector Machine
References
[1] Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases. Proceedings of the ACM SIGMOD conference on management of data; 1993. p. 207–16.
[2] Agrawal R, Srikant R. Fast algorithms for mining association rules in large data-bases. Proceedings of the 20th International Conference on Very Large Databases; 1994. p. 478–99.
[3] Kavakiotis I, Tzanis G, Vlahavas I. Mining frequent patterns and association rules from biological data. In: Elloumi M, Zomaya AY, editors. Biological knowledge discovery handbook: preprocessing, mining and post processing of biological data. Wiley Book series on bioinformatics: computational techniques and engineering, New Jersey, USA: Wiley-Blackwell, John Wiley & Sons Ltd.; 2014
[4] Han J, Kamber M, Pei J. Data mining: concepts and techniques. The Morgan Kaufmann series in data management systems; 2011.
[5] Alpaydin E. Introduction to machine learning. Cambridge Massachusetts London England: The MIT Press; 2004.
[6] Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res 2003; 3:1157–82.
[7] Witten IH, Frank E, Hall MA. Data mining: practical machine learning tools and techniques. 3rd ed. Burlington, MA: Morgan Kaufmann; 2011.
[8] American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 2009; 32 (Suppl. 1):S62–7.
[9] Cox EM, Elelman D. Test for screening and diagnosis of type 2 diabetes. Clinical Diabetes 2009;4(27):132–8.
[10] Krentz AJ, Bailey CJ. Oral ant diabetic agents: current role in type 2 diabetes mellitus. Drugs 2005;65(3):385–411.
[11] Tsave O,et al. Structure-specific adipogenic capacity of novel, well-defined ternary Zn(II)-Schiff base materials. Biomolecular correlations in zinc-induced differentiation of 3T3-L1 pre-adipocytes to adipocytes. J Inorg Biochem Nov 2015; 152:123–37.
[12] Halevas E, et al. Design, synthesis and characterization of novel binary V (V)-Schiff base materials linked with insulin-mimetic vanadium-induced differentiation of 3T3-L1 fibro-blasts to adipocytes. Structure–function correlations at the molecular level. J Inorg Biochem Jun 2015; 147:99–115.
[13] Tsave O, et al. The adipogenic potential of Cr (III). A molecular approach exemplifying metal-induced enhancement of insulin mimesis in diabetes mellitus II. J Inorg Biochem Oct 2016; 163:323–31.
[14] “Records in DBLP”. Statistics. DBLP. Retrieved 2016–07-16; 2016.
[15] Agarwal V et al. Learning statistical models of phenotypes using noisy labeled training data. J Am Med Inform
[16] Hoyt R, Linnville S, Thaler S, Moore J. Digital family history data mining with neural networks: a pilot study. Perspect Health Information Management Jan 1 2016.
[17] Anderson JP et al. Reverse engineering and evaluation of prediction models for progression to type 2 diabetes: an application of machine learning using electronic health records. J Diabetes SciTechnology Dec 20 2015; 10(1):6–18.
[18] Anderson AE et al. Electronic health record phenotyping improves detection and screening of type 2 diabetes in the general United States population: a cross-sectional, unselected, retrospective study. J Biomed Inform Apr 2016; 60:162–8.
[19] Bashir S, Qamar U, Khan FH. IntelliHealth: a medical decision support application using a novel weighted multi-layer classifier ensemble framework. J Biomed In-form Feb 2016; 59:185–200.
[20] Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values.Talayeh Razzaghi, Oleg Roderick, Ilya Safro, and Nicholas Marko Published: May 19, 2016.
[21] J. H. Friedman. Multivariate adaptive regression splines. Annals of Statistics, 19(1):1–67, 1991.[9] A. Gawande. The hot spotters. New Yorker, January 2011.
[22] A. K. Jain and Richard C. Dubes. Algorithms for Clustering Data. Prentice-Hall, Upper Saddle River, NJ, USA, 1988.
[23] ChaitraliDangare, S. and Sulaba Apte,S.Improved study of disease prediction using data mining classification techmiques. Int.J.Comp.Appl. 2012, 47(10):75-88.
Citation
C.Natarajan, J.M.Gnanasekar, N.Janorious Hermia, "A Framework for Efficient Healthcare Resources Utilization using Semi-supervised Machine Learning Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.894-900, 2019.
Energy Storage Devices and Its Hybridization for Designing Supply System
Survey Paper | Journal Paper
Vol.7 , Issue.4 , pp.901-909, Apr-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i4.901909
Abstract
Energy sources undergo large momentary changes in power input/output monthly or even annual cycles. Electricity production need not be drastically scaled up and down to meet momentary consumption. Therefore, in order to become these sources completely suitable as primary sources of energy, energy storage is an essential factor. There are storage devices that are used for very large energy storage (i.e. pumped hydro, CAES) or for comparatively smaller storage (i.e. batteries, UC, flywheel, fuel cell). A large range of storage technologies exists with each one possessing different peculiarity and are proposed for different applications. Also, it is highly uneconomical and inefficient to design any energy storage system only based on peak power demand. So, hybrid combination of such devices would lead to availability of a secondary source which in turn will play role in different parts of demand profile and can also work as the primary source for time being.
Key-Words / Index Term
Load profile, Energy storage, hybridization
References
[1] Ministry of power Govt. of India.
http://www.powermin.nic.in/JSP_SERVLETS/internal.jsp.
[2]. C.Naish, I.McCubbin, O.Edberg, M.Harfoot, “Outlook
Of Energy Storage Technologies”, Department of Eco-
nomic and Scientific Policy, February 2008.
[3]. D.Connolly,“A Review Of Energy Storage Technologies
For The Integration Of Fluctuating Renewable Energy”,
Irish Research Council for Science, Engineering, and
Technology, University of Limerick , October 2010.
[4]. I.Hadjipaschalis, A.Poullikkas, V.Efthimiou, “Overview
of current and future energy storage technologies for
electric power applications”, Journal of Renewable and
Sustainable Energy Reviews, pp. 1513-1522, September
2009.
[5]. Arnaud Badel, “SMES using High Temperature Super- conductor for Pulse Power Supply”, Institute Poly technique De Grenoble, University DE GRENOBLE, September 2010.
[6]. J.Makansi, J.Abboud, “Energy Storage-The Missing Link in the Electricity value chain”, Published on May 2002 by: Energy Storage Council United Kingdom.
[7]. B. Maher. (2001) Ultra-Capacitors, Gateway to a New
Thinking in Power Quality. Maxwell Technologies,[Online] Available: http://www.maxwell.com.
[8]. L. Solero, A. Lidozzi, and J.A. Pomilio, “Design of
multiple input power converter for hybrid vehicles,” 19th Annual IEEE Applied Power Electronics Conference and Exposition, vol. 2, 2004, pp. 1145-1151.
[9] Varsha A. Shah, Kriti S. Sachdev, Prasanta Kundu,Ranjan Maheshwari,” Design and control of hybrid power supply for HEV”,2013 world Electric Vehicle Symposium and exhibition(EVS27),2013.
Citation
Kriti K. Arora, C.D.Kotwal, "Energy Storage Devices and Its Hybridization for Designing Supply System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.901-909, 2019.
Rail Fence Cipher Based Encryption Technique For Secure Data Transfer
Research Paper | Journal Paper
Vol.7 , Issue.4 , pp.910-914, Apr-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i4.910914
Abstract
Network security has become one of the major talking points in today’s technological world. Although several research activities were carried out pertaining to security in order to ensure confidentiality, authenticity, integrity, non-repudiation etc., there remained some loopholes which need to be taken care of. There are chances that cyber attackers or hackers may tamper or alter the texts and cause a severe leakage of confidential information of IT organizations, business firms, etc. Hence, it is of utmost importance to protect vital information from such attackers or hackers by using some standard techniques. In our paper, we have discussed the cryptographic techniques with proper encryption and decryption. We have suggested the use of Rail Fencing Cypher along with ASCII codes and mapping tables for end to end encryption of plain text comprising of several characters and then decrypting the encrypted text into plain text.
Key-Words / Index Term
Cryptography, Rail Fence Cipher, Mapping, Encryption, Decryption
References
[1] Sudipta Sahana, Asmita Bhattacharya, Rittik Mondal, Rohan Chattopadhaya, Titas Das, “SECURING AND HIDING TEXTS USING ARCHIMEDEAN SPIRAL TECHNIQUE WITH IMAGE STEGANOGRAPHY”, International Journal of Computer Engineering and Applications, Volume IX, Issue IV, ISSN 2321-3469
[2] Sudipta Sahana, Goutami Dey, Madhurhita Ganguly, Priyankar Paul, Subhayan Paul, “Adaptive Steganography Based Enhanced Cipher HidingTechnique for Secure Data Transfer”, IOSR Journal of Computer Engineering (IOSR-JCE)e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 2, Ver. V, PP 55-60
[3] Sudipta Sahana, Madhusree Majumdar, Shiladitya Bose, Anay Ghoshal, “Security Enhancement Approach For Data Transfer Using Elliptic Curve Cryptography And Image Steganography”, International Journal of Advanced Research in Computer and Communication EngineeringVol. 4, Issue 4, April 2015
[4] Sarita Kumari, “A research Paper on Cryptography Encryption and Compression Techniques”, International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 6 Issue 4 April 2017, Page No. 20915-20919
[5] Prof. Mukund R. Joshi, Renuka Avinash Karkade , “Network Security with Cryptography”, International Journal of Computer Science and Mobile Computing, Vol.4 Issue.1, January- 2015, ISSN 2320–088X
[6] Ujjwal Barman, Suchismita Gupta, Sudipta Sahana, “Substitution Technique Based Noble Approach Towards Base64 Crypting System Incorporating Rail Fence Cipher”, CCET JOURNAL OF SCIENCE AND ENGINEERING EDUCATIONCCET JOURNAL OF SCIENCE AND ENGINEERING EDUCATION, Vol. - 3, Page-60-65, Year-2018, ISSN 2455-5061
[7] Prof. Swapnil Chaudhari, Mangesh Pahade, Sahil Bhat, Chetan Jadhav, Tejaswini Sawant, “A Research Paper on New Hybrid Cryptography Algorithm”, INTERNATIONAL JOURNAL FOR RESEARCH & DEVELOPMENT IN TECHNOLOGY, Volume-9,Issue-5(May-18) ISSN (O) :- 2349-3585
[8] Abhipsa Kundu, Sudipta Sahana, “Dynamic Size Based Cipher Aided Image Steganography Technique for Network Security Enhancement”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 10, October 2014, ISSN: 2277 128X
Citation
Debolina Dalui, Sudipta Sahana, "Rail Fence Cipher Based Encryption Technique For Secure Data Transfer," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.910-914, 2019.
Insider Threats Detection Methods : A Survey
Survey Paper | Journal Paper
Vol.7 , Issue.4 , pp.915-923, Apr-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i4.915923
Abstract
We are living in the age of advanced digital era. We could not even have thought of living without digital gadgets. Almost all the public and private sectors are working with digital data. There is a need to secure this confidential digital data from insider and outsider cyber-attacks. This research paper includes the survey of insider threat detection methods. Insider threats detection are more difficult because insiders are having all privileges or credentials to access the resources and no one will suspect on them. It is easy to transfer the digital data and access can be given to handle this data remotely through compromised insiders. Insider threats results in digital data theft, data leakage and data loss which impacts on profit level and damage the organization image in the market. Survey covers emerging technologies used for detection of insider threats. This research paper identifies the trends of tools, methods used for insider threat detection. It presents information year wise in statistical tabular format. This paper gives insight for future work and challenges to mitigate the cyber-attacks by insider threats.
Key-Words / Index Term
Insider threats, cyber-attacks, detection methods
References
[1] J. Epstein, “Security Lessons Learned from Society,” IEEE Secur. Priv. Mag., vol. 6, no. 3, pp. 80–82, May 2008.
[2] B. M. Bowen, M. Ben Salem, S. Hershkop, A. D. Keromytis, and S. J. Stolfo, “Designing Host and Network Sensors to Mitigate the Insider Threat,” IEEE Secur. Priv. Mag., vol. 7, no. 6, pp. 22–29, Nov. 2009.
[3] D. Caputo, M. Maloof, and G. Stephens, “Detecting Insider Theft of Trade Secrets,” IEEE Secur. Priv. Mag., vol. 7, no. 6, pp. 14–21, Nov. 2009.
[4] S. L. Pfleeger and S. J. Stolfo, “Addressing the Insider Threat,” IEEE Secur. Priv. Mag., vol. 7, no. 6, pp. 10–13, Nov. 2009.
[5] F. Duran, S. H. Conrad, G. N. Conrad, D. P. Duggan, and E. B. Held, “Building A System For Insider Security,” IEEE Secur. Priv. Mag., vol. 7, no. 6, pp. 30–38, Nov. 2009.
[6] G. M. Coates, K. M. Hopkinson, S. R. Graham, and S. H. Kurkowski, “A Trust System Architecture for SCADA Network Security,” IEEE Trans. Power Deliv., vol. 25, no. 1, pp. 158–169, Jan. 2010.
[7] S. L. Pfleeger, J. B. Predd, J. Hunker, and C. Bulford, “Insiders Behaving Badly: Addressing Bad Actors and Their Actions,” IEEE Trans. Inf. Forensics Secur., vol. 5, no. 1, pp. 169–179, Mar. 2010.
[8] R. Beyah and A. Venkataraman, “Rogue-Access-Point Detection: Challenges, Solutions, and Future Directions,” IEEE Secur. Priv. Mag., vol. 9, no. 5, pp. 56–61, Sep. 2011.
[9] M. Mansoori and Ray Hunt, “An ISP Based Notification and Detection System to Maximize Efficiency of Client Honeypots in Protection of End Users,” Int. J. Netw. Secur. Its Appl., vol. 3, no. 5, pp. 59–73, Sep. 2011.
[10] Y. Chen, S. Nyemba, and B. Malin, “Detecting Anomalous Insiders in Collaborative Information Systems,” IEEE Trans. Dependable Secure Comput., vol. 9, no. 3, pp. 332–344, May 2012.
[11] X. Li, Y. Xue, and B. Malin, “Detecting Anomalous User Behaviors in Workflow-Driven Web Applications,” 2012, pp. 1–10.
[12] P. Legg et al., “Towards a Conceptual Model and Reasoning Structure for Insider Threat Detection,” p. 19, 2013.
[13] S. Omar, A. Ngadi, and H. H. Jebur, “An Adaptive Intrusion Detection Model based on Machine Learning Techniques,” Int. J. Comput. Appl., vol. 70, no. 7, pp. 1–5, May 2013.
[14] M. Bishop et al., “Insider Threat Identification by Process Analysis,” in 2014 IEEE Security and Privacy Workshops, San Jose, CA, 2014, pp. 251–264.
[15] A. Azaria, A. Richardson, S. Kraus, and V. S. Subrahmanian, “Behavioral Analysis of Insider Threat: A Survey and Bootstrapped Prediction in Imbalanced Data,” IEEE Trans. Comput. Soc. Syst., vol. 1, no. 2, pp. 135–155, Jun. 2014.
[16] Z. Malek and D. B. Trivedi, “The Rule Based Intrusion Detection Model for User Behavior,” Int. J. Adv. Res. Comput. Sci. Softw. Eng., p. 4, 2015.
[17] K. Padayachee, “Aspectising honeytokens to contain the insider threat,” IET Inf. Secur., vol. 9, no. 4, pp. 240–247, Jul. 2015.
[18] I. Atoum and A. Otoom, “Effective Belief Network for Cyber Security Frameworks,” Int. J. Secur. Its Appl., vol. 10, no. 4, pp. 221–228, Apr. 2016.
[19] H. Bao, R. Lu, B. Li, and R. Deng, “BLITHE: Behavior Rule-Based Insider Threat Detection for Smart Grid,” IEEE Internet Things J., vol. 3, no. 2, pp. 190–205, Apr. 2016
[20] M. Ali et al., “SeDaSC: Secure Data Sharing in Clouds,” IEEE Syst. J., vol. 11, no. 2, pp. 395–404, Jun. 2017.
[21] B. Bose, B. Avasarala, S. Tirthapura, Y.-Y. Chung, and D. Steiner, “Detecting Insider Threats Using RADISH: A System for Real-Time Anomaly Detection in Heterogeneous Data Streams,” IEEE Syst. J., vol. 11, no. 2, pp. 471–482, Jun. 2017.
[22] A. M. Ali and P. Angelov, “Anomalous behaviour detection based on heterogeneous data and data fusion,” Soft Comput., vol. 22, no. 10, pp. 3187–3201, May 2018.
[23] X. Huang, Y. Lu, D. Li, and M. Ma, “A Novel Mechanism for Fast Detection of Transformed Data Leakage,” IEEE Access, vol. 6, pp. 35926–35936, 2018.
[24] L. Liu, O. De Vel, C. Chen, J. Zhang, and Y. Xiang, “Anomaly-Based Insider Threat Detection Using Deep Autoencoders,” in 2018 IEEE International Conference on Data Mining Workshops (ICDMW), Singapore, Singapore, 2018, pp. 39–48.
[25] S. Elshafei and A. Abdelnaby, “Using semantic variations in clustering insiders behavior,” in 2018 6th International Symposium on Digital Forensic and Security (ISDFS), Antalya, 2018, pp. 1–5.
[26] M. Dahmane and S. Foucher, “Combating Insider Threats by User Profiling from Activity Logging Data,” in 2018 1st International Conference on Data Intelligence and Security (ICDIS), South Padre Island, TX, 2018, pp. 194–199. [26] Yakubu Ajiji Makeri, "The role of Cyber Security and Human-Technology Centric for Digital Transformation", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.6, pp.53-59, 2018.
[27] S. Garg, K. Kaur, N. Kumar, and J. J. P. C. Rodrigues, “Hybrid Deep-Learning-Based Anomaly Detection Scheme for Suspicious Flow Detection in SDN: A Social Multimedia Perspective,” IEEE Trans. Multimed., vol. 21, no. 3, pp. 566–578, Mar. 2019.
[28] W. Shen, J. Qin, J. Yu, R. Hao, and J. Hu, “Enabling Identity-Based Integrity Auditing and Data Sharing With Sensitive Information Hiding for Secure Cloud Storage,” IEEE Trans. Inf. Forensics Secur., vol. 14, no. 2, pp. 331–346, Feb. 2019.
[29] P. Santra, "An Expert Forensic Investigation System for Detecting Malicious Attacks and Identifying Attackers in Cloud Environment", International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.5, pp.1-26, 2018.
[30] Afzal Ahmad, Mohammad Asif, Shaikh Rohan Ali, "Review Paper on Shallow Learning and Deep Learning Methods for Network security", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.5, pp.45-54, 2018.
[31] Poonam Devi , "Attacks on Cloud Data: A Big Security Issue", International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.2, pp.15-18, 2018.
[32] Yakubu Ajiji Makeri, "The role of Cyber Security and Human-Technology Centric for Digital Transformation", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.6, pp.53-59, 2018.
Citation
Ujwala Sav, Ganesh Magar, "Insider Threats Detection Methods : A Survey," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.915-923, 2019.
Map Reduce concept based Sentiment Analysis Approach
Research Paper | Journal Paper
Vol.7 , Issue.4 , pp.924-927, Apr-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i4.924927
Abstract
In the digital word there produce a large amount of data every second related to web based content or blogging data or the data generated from reviews. When we want to do the analysis of any data we need to know about the sentiments of the user who are directly or indirectly in the use of related data for this type of data processing we need sentiment analysis in fast manner, by the use of Map reduce architecture we split the related collected data into small clusters and analysis the data in very less time. Micro blogging locales have a great many individuals sharing their contemplations every day due to its trademark short and straightforward way of articulation. We propose and research a worldview to store the assessment taken away a prominent ongoing micro blogging administration, Twitter, spot clients present constant responses on and sentiments around everything. This paper mainly focuses on the concept of twitter sentiment analysis, here we have presented a system architecture how we can collect the data from the different sources and can process the data. We have focused the concept of Hadoop Map Reduce architecture for data processing in our research work. In the result section we have presented the analysis of sentiments collected from different source in tabular format as well as the graphical representation is given. A contextual analysis is introduced to represent the utilization and viability of the suggested framework.
Key-Words / Index Term
Twitter,Sentiment anlysis,blogging,Hadoop,Map-reduce
References
[1]L. Colazzo, A. Molinari and N. Villa. “Collaboration vs. Participation: the Role of Virtual Communities in a Web 2.0 world”, International Conference on Education Technology and Computer, 2009,pp.321-325.
[2]nlp.stanford.edu/courses/cs224n/2011/reports/patlai.pdf
[3]National Daily, Economic Times: Articles. Economic Times .indiatimes.com,Collections
[4]K. Dave, S. Lawrence, and D.M. Pennock. “Mining the peanut gallery: Opinion extraction and semantic classification of product reviews”. In Proceedings of the 12th International Conference on World Wide Web (WWW), 2003, pp.519–528.
[5]A. Pak and P. Paroubek. “Twitter as a Corpus for Sentiment Analysis and Opinion Mining”. In Proceedings of the Seventh Conference on International Language Resources and Evaluation, 2010,pp.1320–1326.
[6]R. Parikh and M. Movassate, “Sentiment Analysis of User- Generated Twitter Updates using Various Classification Techniques”, CS224N Final Report, 2009
[7]A. Go, R. Bhayani, L.Huang. “Twitter Sentiment Classification Using Distant Supervision”. Stanford University, Technical Paper,2009.
[8]J. Read. “Using emoticons to reduce dependency in machine learning techniques for sentiment classification”. In Proceedings of ACL-05, 43nd Meeting of the Association for Computational Linguistics. Association for Computational Linguistics,2005
[9]L. Barbosa, J. Feng. “Robust Sentiment Detection on Twitter from Biased and Noisy Data”. COLING 2010: Poster Volume, pp.36-44.
[10]Bhanu Prakash Lohani, Vimal Bibhu, Ajit Singh, "Review of Evolutionary Algorithms based on parallel computing paradigm"SSRG International Journal of Computer Science and Engineering 4.6 (2017): 1-4
[11]S. Batra and D. Rao, ”Entity Based Sentiment Analysis on Twitter”, StanfordUniversity,2010
[12]A. Bifet and E. Frank, ”Sentiment Knowledge Discovery in Twitter Streaming Data”, In Proceedings of the 13th International Conference on Discovery Science, Berlin, Germany: Springer,2010, pp.1–15.
[13] V. Bibhu, P. K. Kushwaha and B. P. Lohani, "A review of security of the cloud computing over business with implementation," 2016 International Conference on Innovation and Challenges in Cyber Security (ICICCS-INBUSH), Noida, 2016, pp. 192-198.doi: 10.1109/ ICICCS.2016.7542342
Citation
Bhavya Makkar, Ayush Kaushik, Bhanu P. Lohani, Vimal Bibhu ,Pradeep K.Kushwaha, "Map Reduce concept based Sentiment Analysis Approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.924-927, 2019.
Hybrid Optimized Algorithms for Solving Clustering Problems in Data Mining
Survey Paper | Journal Paper
Vol.7 , Issue.4 , pp.928-932, Apr-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i4.928932
Abstract
In this paper, Cluster analysis is a group objects like observations, events etc based on the information that are found in the data describing the objects or their relations. The main goal of the clustering is that the objects in a group will be similar or related to one other and different from (or unrelated to) the objects in other groups. Extracting relevant information from large database is attaining huge significance. Clustering of relevant information from large database becomes difficult. The major objective of this work is to proposed novel clustering methods for solving clustering problem. It is used to separate the data set into a significant set of reciprocally limited clusters with respect to relationship of data and it is used to create the more number of data in the same manner surrounded by a group and extra various among groups. Data clustering is a vital concept of mining as it partitions the given dataset into meaningful set of clusters based on data similarity. This concept enhances the computation efficiency in the data analysis processes
Key-Words / Index Term
Clustering, ABC Algorithm, PSO and FA Algorithm, MOSSSA-HAC, MOSSCS-MHAC Algorithms
References
1. Abraham, Ajith, Swagatam Das, and Amit Konar, 2007. "Kernel based automatic clustering using modified particle swarm optimization algorithm." InProceedings of the 9th annual conference on Genetic and evolutionary computation, pp. 2-9. ACM, 2007.
2. Hassanzadeh, Tahereh, and Mohammad Reza Meybodi, 2012. "A new hybrid approach for data clustering using firefly algorithm and K-means." Artificial Intelligence and Signal Processing (AISP), 16th CSI International Symposium on. IEEE, 2012.
3. Tapas Kanungo, David M Mount, Nathan S Netanyahu, Christine D Piatko, Ruth Silverman, Angela Y Wu, 2002. "An efficient k-means clustering algorithm: Analysis and implementation." IEEE transactions on pattern analysis and machine intelligence 24.7: 881-892.
4. Xiaohui Yan, Yunlong Zhu, Wenping Zou, and Liang Wang, 2012. "A new approach for data clustering using hybrid artificial bee colony algorithm." Neurocomputing 97 : 241-250.
5. Yunfeng Xu, Ping Fan, and Ling Yuan, 2013. "A simple and efficient artificial bee colony algorithm." Mathematical Problems in Engineering 2013.
6. Sangeetha, J., and V. Sinthu Janita Prakash. "An Efficient Inclusive Similarity Based Clustering (ISC) Algorithm for Big Data." Computing and Communication Technologies (WCCCT), 2017 World Congress on. IEEE, 2017.
7. Tanır, Deniz, and Fidan Nuriyeva. "An effective method determining the initial cluster centers for K-means for clustering gene expression data." Computer Science and Engineering (UBMK), 2017 International Conference on. IEEE, 2017.
8. International Journal of Scientific Research in Computer Sciences and Engineering (ISSN: 2320-7639)
9. International Journal of Scientific Research in Network Security and Communication (ISSN: 2321-3256) .
10. Gan, G., J. Wu, and Z. Yang, 2009. "A genetic fuzzy k-Modes algorithm for clustering categorical data." Expert Systems with Applications 36.2 : 1615-1620.
Citation
S. Karthikeyan, A.Dhakshina Moorthy , "Hybrid Optimized Algorithms for Solving Clustering Problems in Data Mining," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.928-932, 2019.