Volume-6 , Issue-2 , Apr 2018, ISSN 2320-7639 (Online) Go Back

• Open Access   Article

  An Anti-Ransomware Tool Design by Using Behavioral and Static Analysis Methods

  B. Celiktas, N. Unlu, E. Karacuha

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.2 , pp.1-9, Apr-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.19

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  Abstract
  Ransomware, which constantly improves by updating itself and transferring to the network and computing environment, is the most common type of malware used by the attackers recently. Ransomware demands ransom from the user for decrypting the encrypted files. As a result of the payment of the desired amount of ransom, the files can be opened with the decryption key delivered to the user. Various antivirus software using static analysis methods fails to detect the malware because it performs analysis via hash signature samples in databases. Because hash signature samples of zero-day attacks are not recorded in anti-virus databases, detecting malware by using behavioral analysis methods is more effective. Anti-ransomware in the hybrid structure using static analysis methods, along with behavioral analysis methods, will be even more successful in detecting and preventing ransomware with minimum false-positive rate and minimal file loss. As a result of a comprehensive review of related literature and professional reports on ransomware, the attack vectors of the ransomware, the core features, the identification methods and the movements based on the Windows Operating Systems have been found. This study presents the behavior of the ransomware in detail and explains how should an anti-ransomware tool be created to detect and prevent ransomware on Windows Operating Systems.

  Key-Words / Index Term
  Ransomware; Encryption; Static Analysis; Behavioral Analysis; Attack Vectors

  References
  [1] N. Sacife, H. Carter, P. Traynor and K. R.B Butler, “CryptoLock (and Drop It): Stopping Ransomware Attacks on User Data”, IEEE 36th International Conference on Distributed Computing Systems, 2016.
  [2] A. Bhardwaj, V. Avasthi, H. Sastry and G. V. B. Subrahmanyam, "Ransomware Digital Extortion: A Rising New Age Threat", Indian Journal of Science and Technology, Vol 9(14), 2016.
  [3] M. Wecksten, J. Frick, A. Sjostrom and E. Jarpe, “A Novel Method for Recovery from Crypto Ransomware Infections”, 2nd IEEE International Conference on Computer and Communications, 2016.
  [4] M. H. U. Salvi, & M. R. V. Kerkar, “Ransomware: A cyber-extortion”, Asian Journal of Convergence in Technology, 2(3), 2016.
  [5] A. Zahra and A. S.Munam, “IoT Based Ransomware Growth Rate Evaluation and Detection Using Command and Control Blacklisting”, Proceedings of the 23rd International Conference on Automation & Computing, University of Huddersfield, Huddersfield, UK, 7-8, 2017.
  [6] CheckPoint, “Ransomware: Attack Trends, Prevention, And Response”, White Paper, 2017.
  [7] A. Liska and T. Gallo, “Ransomware: Defending Against Digital Extortion”, O’Reilly Media, Inc., 2016.
  [8] V. Kotov, M. S. Rajpal, “In-Depth Analysis of the Most Popular Malware Families”, Bromium, Understanding Crypto-Ransomware Report, 2014.
  [9] N. Hampton and Z. A. Baig, “Ransomware: Emergence of the cyber-extortion menace,” Aust. Inf. Secure. Manag. Conf., vol. 2015, pp. 47–56, 2015.
  [10] A. Adamov, A. Carlsson, "The state of ransomware. Trends and mitigation techniques", vol. 00, no., pp. 1-8, Doi:10.1109/ EWDTS. 2017.8110056, 2017.
  [11] N. Hampton and Z. A. Baig, “Ransomware: Emergence of the cyber-extortion menace,” Aust. Inf. Secure. Manag. Conf., vol. 2015, pp. 47–56, 2015.
  [12] Kaspersky Lab, “Kaspersky Security Bulletin, Story of The Year: The Ransomware Revolution”, Report, 2016.
  [13] B. Heater, “How ransomware conquered the world”, PC Magazine Digital Edition, 2016.
  [14] Symantec, “CryptoDefense, the CryptoLocker Imitator, Makes Over $34,000 in One Month”, Symantec Security Response, 2014.
  [15] A. Liska, T. Gallo, “Ransomware: Defending Against Digital Extortion”, O`Reilly Media, Inc. First Ed., 2016.
  [16] T. Anjana, “Discussion On Ransomware, WannaCry Ransomware, and Cloud Storage Services Against Ransom Malware Attacks”, IJRTI, Vol.2, Issue 6, ISSN: 2456-3315, 2017.
  [17] Webroot, “MSP Guide: Stopping Crypto Ransomware Infections in SMBs, 16 Easy Actions for MSPs”, White Paper, 2017
  [18] A. Anubhav and R. Ellur, “Cerber: Analyzing a Ransomware Attack Methodology To Enable Protection”, Threat Research, Advanced Malware, FireEye, 2016.
  [19] U.K. Singh, C. Joshi, and S.K. Singh. "Zero-day Attacks Defense Technique for Protecting System against Unknown Vulnerabilities", International Journal of Scientific Research in Computer Science and Engineering, 5(1), 2017.
  [20] Malwarebytes, “Cybercrime tactics and techniques”, Report, Q1 2017.
  

  Citation
  B. Celiktas, N. Unlu, E. Karacuha, "An Anti-Ransomware Tool Design by Using Behavioral and Static Analysis Methods," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.1-9, 2018

• Open Access   Article

  Different Approaches for Frequent Itemset Mining

  P.V. Nikam, D.S. Deshpande

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.2 , pp.10-14, Apr-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.1014

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  Abstract
  Data mining is the retrieval of hidden analytical information from huge databases, is a controlling new technology with great possible to help organizations as well as research focus on the mainly essential information in their data warehouses. Data mining tools forecast future development and performances, allowing businesses to create proactive, idea for decision making systems. Frequent Itemset Mining (FIM) is one of the traditional data mining problems in mainly of the data mining approaches. It requires very huge computations and input and output traffic capacity. Also resources like single processor’s memory and CPU are very limited, which degrades the presentation of algorithm. In this research work system proposed one such distributed approach which will run on Hadoop cluster – one of the recent most popular distributed frameworks which basically focus on parallel processing. The proposed framework takes into account extends characteristics of the Apriori algorithm related to the frequent itemset invention and throughout a block-based partitioning uses a dynamic workload management. The algorithm greatly improves the performance and gets high scalability compared to the existing approaches. Proposed algorithm is implemented and tested on large scale datasets distributed system on heterogeneous cluster.

  Key-Words / Index Term
  Frequent Itemset, Apriori, FP Growth, Modified Apriori

  References
  [1] K. S. N. Prasad, S. Ramakrishna, "Frequent Pattern Mining and Current State of the Art", International Journal of Computer Applications, Vol. 26, No. 7, pp. 33-39, 2011.
  [2] C. Saravanabhavan, R. M. S. Parvathi, "Utility FP-Tree: An Efficient Approach to Mine Weighted Utility Itemsets", European Journal of Scientific Research, Vol. 50 No. 4, pp. 466-480, 2011.
  [3] R. V. Prakash, S. Govardhan, S. S. V. N. Sarma, "Mining Frequent Itemsets from Large Data Sets using Genetic Algorithms", IJCA-Artificial Intelligence Techniques - Novel Approaches & Practical Applications, No. 4, Vol. 7, pp. 38-43, 2011.
  [4] S. Kannimuthu, K. Premalatha, S. Shankar, "iFUM - Improved Fast Utility Mining", International Journal of Computer Applications, Volume 27– No.11, pp. 32-36, 2011.
  [5] P. S. Sandhu, D. S. Dhaliwal, S. N. Panda, "Mining utility-oriented association rules: An efficient approach based on profit and quantity", International Journal of the Physical Sciences Vol. 6, No. 2, pp. 301-307, 2011.
  [6] V. M. Kuthadi, "A New Data Stream Mining Algorithm for Interestingness-Rich Association Rules", Journal of Computer Information Systems, pp. 14-27, 2013.
  [7] R. Agrawal, T. Imielinski, A. Swami, “Mining association rules between sets of items in large databases,” ACM SIGMOD Rec., vol. 22, no. 2, pp. 207–216, 1993.
  [8] M. Y. Lin., P. Y. Lee, S. C. Hsueh “Apriori based frequent itemset mining algorithms on Mapreduce,” ICUIMC’12, 6th international conference on Information Management and communication,Malaysia, article no. 76, Feb 2012.
  [9] L. Zhou, Z. Zhong, J. Chang, “Ballanced Parallel FP-growth with Mapreduce,” Information Computing and telecommunications, 5713090, Nov. 2010.
  [10] Y. J. Tsay, T. J. Hsu, J. R. Yu “FIUT: A new method for mining frequent itemsets,” Inf. Sci., vol. 179, no. 11, pp. 1724–1737, May 2009.
  [11] S. Moens, E. Aksehirli, B. Goathals, “Frequent Itemset Mining for Big Data,” IEEE conference in Big Data, Silicon Valley, USA, 978-1-4799-1293-3, Dec. 2013.
  [12] M. Riondato, J. A. DeBrabant, R. Fonseca, and E. Upfal, “PARMA:A parallel randomized algorithm for approximate association rules mining in MapReduce,” Proc. 21st ACM Int. Conf. Inf. Knowl. Manage.,Maui, HI, USA, pp. 85–94,2012.
  [13] Goethals B. “Survey on frequent pattern mining,” Univ. of Helsinki, 19:840-52.
  [14] S. Jarkad, J. E. Nalavade, “Approach for Big Data Mining in Hadoop Framework : An Overview,” IJIRSET, Jan 2017.
  [15] Y. Xun, J. Zhang, and X. Qin, “FiDoop: Parallel Mining of Frequent Itemsets Using Mapreduce,” IEEE Trans. Systems, Man, and Cybernetics, vol.46, no.3, Mar. 2016.
  [16] C. V. Suneel, K. Prasanna, M. R. Kumar, “Frequent Data Partitioning using Parallel Mining Itemset and Mapreduce”, IJSRCSEIT, Vol.2,Issue 4,2017, pg.641-644.
  

  Citation
  P.V. Nikam, D.S. Deshpande, "Different Approaches for Frequent Itemset Mining," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.10-14, 2018

• Open Access   Article

  Improving Clustering Accuracy using Feature Extraction Method

  T. SenthilSelvi, R. Parimala

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.2 , pp.15-19, Apr-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.1519

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  Abstract
  Clustering is the technique employed to group documents containing related information into clusters, which facilitates the allocation of relevant information. Clustering performance is mostly dependent on the text document features. The first challenge concerns difficulty with identifying significant term features to represent original content by considering the hidden knowledge. The second challenge is related to reducing data dimensionality without losing essential information. Clustering techniques were proposed to use feature extraction methods Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA) to improve the clustering efficiency and quality. Documents are pre-processed, converted to vector space model and then clustered using the proposed algorithm. The goal of this work is to design a suitable model for clustering text document that is capable of improving clustering performance. In this paper, the problems are discussed with empirical evidence. Experimental results show that the proposed method is effective for the text clustering task.

  Key-Words / Index Term
  Clustering; Euclidean Distance; Document frequency; Dimensionality reduction; Principal components

  References
  [1] C. Boutsidis, M.W. Mahoney, P. Drineas,"Unsupervised Feature Selection for the k-Means clustering problem", In the NIPS`09 Proceedings of the 22nd International Conference on Neural Information Processing Systems, Canada, pp.153-161, 2009.
  [2] D. Greene, P. Cunningham, "Practical Solutions to the Problem of Diagonal Dominance in Kernel Document Clustering", In the 23rd International Conference on Machine Learning, Pittsburgh, PA, pp.377-384, 2006.
  [3] Z. Miner, L. Csat, "Kernel PCA Based Clustering for Inducing Features in text Categorization", In the ESANN`2007 Proceedings- European Symposium on Artificial Neural Networks, Bruges,Belguim., pp.349-354, 2007.
  [4] R. Mall, J.A.K.Suykens,"Kernel Spectral Document Clustering Using Unsupervised Precision-Recall Metrics.", 2015 International Joint Conference on Neural Network,Killarney, Ireland, pp. 1-7, 2015.
  [5] R. Jenssen, T.Eltoft, M.Girolami and D. Erdogmus ,"Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm.", In the NIPS`06 Proceedings of the 19th International Conference on Neural Information Processing Systems, Canada, pp.633-640, 2006.
  [6] T. Shi, M. Belkin, B. Yu, “Data spectroscopy: eigenspaces of convolution operators and clustering”, The Annals of Statistics, Vol. 37, No.6B, pp.3960-3984, 2009.
  [7] L.Kaufmann, “Advances in Kernel Methods — Support Vector Learning -Solving the quadratic programming problem arising in support vector classification, MIT Press, Cambridge, MA, pp.147–168, 1999.
  [8] Y.Yang, J.O. Pedersen, "A Comparative study of feature selection in Text Categorization", In the Proceedings of the Fourteenth International Conference on Machine Learning (ICML`97), USA, pp.412-420, 1997.
  [9] I. Feinerer, K.Hornik, D. Meyer, "Text Mining Infrastructure in R”, Journal of Statistical Software, Vol.25, Issue 5, pp.1-54, 2008..
  [10] A. Karatzoglou, A. Smola, K. Hornik, A. Zeileis, "kernlab - An S4 Package for Kernel Methods in R", Journal of Statistical Software Vol.11, Issue 9, pp.1-20, 2004.
  

  Citation
  T. SenthilSelvi, R. Parimala, "Improving Clustering Accuracy using Feature Extraction Method," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.15-19, 2018

• Open Access   Article

  TRP Maximization Technique based Efficient Scheduling in Grid Environment

  V. Indhumathi

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.2 , pp.20-26, Apr-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.2026

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  Abstract
  The problem of heterogeneous job scheduling in grid environment is well studied. Number of approaches considers different factors of resource and process in terms of completion, waiting and throughput. However, the methods suffer to achieve higher performance in scheduling in grid environment. To improve the performance, an efficient TRP maximization algorithm is presented in this paper. First, the method identifies list of processes and their required resource with their hold time. Based on the information obtained, a set of two hop sequences are generated for each resource available. For each two hop sequence, the method compute throughput maximization support, resource utilization maximization factor and process completion maximization factor. Using all these, a scheduling maximization support has been estimated. Based on the support measure a single one has been selected for each resource available. The same will be iterated for each level of scheduling and the method produces higher efficiency in scheduling. The performance of scheduling has been improved with less waiting time.

  Key-Words / Index Term
  Grid Environment, Resource Utilization, Maximization, TRP, Job Scheduling

  References
  [1] Kwok, Y.K., Maciejewski, A.A., Siegel, H.J., Ahmad, I., Ghafoor, A.: A semi-static approach to mapping dynamic iterative tasks onto heterogeneous computing system. Journal of Parallel and Distributed Computing 66, 77–98 (2006)
  [2] Dogana, A., Özgüner, F.: Scheduling of a meta-task with QoS requirements in heterogeneous computing systems. Journal of Parallel and Distributed Computing 66(2), 181–196 (2006)
  [3] J. Preethi1 , R. Jayasudha2,Load Balancing and Bandwidth in Grid Computing Environments , International Journal of scientific research in computer science, engineering and technology, vol.2, issue 2, 2017.
  [4] Y. H. Lee, S. Leu, R. S. Chang, "Improving job scheduling algorithms in a grid environment", Future generation computer systems, vol. 27, no. 8, pp. 991-998, 2011.
  [5] P. Keerthika, N. Kasthuri, "A hybrid scheduling algorithm with load balancing for computational grid", International Journal of Advanced Science and Technology, vol. 58, pp. 13-28, 2013.
  [6] Chang Ruay-Shiung, Lin Chih-Yuan, Lin Chun-Fu, "An adaptive scoring job scheduling algorithm for grid computing", Information Sciences, vol. 207, pp. 79-89, 2012.
  [7] M. Yaghoobi, A. Fanian, H. Khajemohammadi, T. A. Gulliver, "A non-cooperative game theory approach to optimize workflow scheduling in grid computing", Communication Computers and Signal Processing (PACRIM), 2013.
  [8] Z. Mousavinasab, R. Entezari-Maleki, A. Movaghar, "A bee colony task scheduling algorithm in computational grids" in Digital Information Processing and Communications, Springer Berlin Heidelberg, pp. 200-210, 2011.
  [9] J.Y Maipan-Uku1 , I. Rabiu2 , Dr. Amit Mishra3, Immediate/Batch Mode Scheduling Algorithms For Grid Computing: A Review,International Journal Of Research, 2017.
  [10] Amalarethinam, G.D.I. & Kfatheen V.S., (2014). Max-min Average Algorithm for Scheduling Tasks in Grid Computing Systems. International Journal of Computer Science and Information Technologies. 3, pp. 3659-62.
  [11] Cao, L., Liu, X., Wang, H., & Zhang, Z. (2014). OPT-Min-Min Scheduling Algorithm of Grid Resources. Journal of Software, 9 (7), pp. 1868-1875.
  [12] Hemamalini, M., & Srinath, M. V. (2015). Memory Constrained Load Shared Minimum Execution Time Grid Task Scheduling Algorithm in a Heterogeneous Environment. Indian Journal of Science and Technology, 8 (15)
  

  Citation
  V. Indhumathi, "TRP Maximization Technique based Efficient Scheduling in Grid Environment," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.20-26, 2018

• Open Access   Article

  Fuzzy Logic Based Fault Detection in Distributed Sensor Networks

  L. B. Bhajantri

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.2 , pp.27-32, Apr-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.2732

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  Abstract
  Distributed Sensor Networks (DSNs) are emerging as a promising area in the real world environment. The nodes are deployed randomly in the DSN environment. The deployed nodes are leads to failure of nodes due to certain environmental conditions. The failure nodes are causes the packet dropping from source node to sink node or disconnection of network. The faulty nodes are also causes the degradation of Quality of Service (QOS) of the network. This paper proposes a fuzzy logic based efficient fault detection for routing in DSNs. This approach incorporates the performance parameters of the network in terms of energy consumption, throughput, and latency.

  Key-Words / Index Term
  Distributed Sensor Network, Fuzzy Logic, Fuzzy Inferences, and Fuzzy Membership

  References
  [1] S., S, Ankit. T, Brooks. R., R, “An Overview of Distributed Sensors Network”.
  Available:
  http://books.google.com/books/about/Distributed−sensor−networks.html?id=Nff5
  [2] S. S, Iyengar. S., S, “Taxonomy of Distributed Sensors Networks".
  Available:
  http://systems.ihpmicroelectronics.com/uploads/downloads/DS1_2007_ 06_Taxonomy.pdf.
  [3] Q. Hairong, S. S., Iyengar,I, C. Krishnendu, ”Distributed Sensor Networks- A Review of Recent Research”, Journal of Distributed Sensor Networks for Real Time System with Adaptive Configuration, Vol. 338, No, 6, pp. 655 - 668, 2007.
  [4] Y. C., Chong, P. S., Kumar, “Sensor Networks: Evolution, Opportunities, and Challenges”, In Proceedings of the IEEE, Vol. 91, No. 8, pp. 1247 - 1256, 2003.
  [5] L. Paradis, Q. Han, “A Survey of Fault Management in Wireless Sensor Networks”, Journal of Network and Systems Management, Vol. 15, No. 2, pp. 171 - 190, 2007.
  [6] S. Chang, T. Huang, “A Fuzzy Knowledge Based Fault Tolerance Algorithm in Wireless Sensor Networks”, In proceedings of International Conference on Advanced Information Networking and Applications, pp.891-896, 2012.
  [7] S. J., Datgheib, “An Efficient Approach to Detect Faulty Readings in Long-thin Wireless Sensor Network using Fuzzy Logic”, In proceedings of Future Communication Networks, pp. 88-92, 2012.
  [8] I. Banerjee, P. Chanak, H. Rahaman, T. Samanta, “Effective Fault Detection and Routing Scheme for Wireless Sensor Networks”, Journal of Computers and Electrical Engineering, vol.40, pp.291-306, 2014.
  [9] P.Jiang, “A new Method for Node Fault Detectionin Wireless Sensor Networks”, Journal on Sensors, vol.9, pp.1282-1294, 2009.
  [10] P. Chanak, I. Banerjee, R. S., Sherratt, “Energy-Aware Distributed Routing Algorithm to Tolerate Network Failure in Wireless Sensor Networks”, Journal of Adhoc Networks, vol.56, pp. 158-172, 2017.
  [11] P. Chanak, I. Banerjee, “Fuzzy Rule Based Faulty Node Classification and Management Scheme for Large Scale Wirless Sensor Networks”, Journal of Expert Systems with Applications, vol.45, pp. 307-321, 2017.
  [12] T. Muhammed, R. A., Shaikh, “An Analysis of Fault Detection Strategies in Wireless Sensor Networks”, Journal of Network and Computer Applications, vol.78, pp. 267-287, 2017.
  [13] L.B., Bhajantri, N.Nalini, “Bayesian Network Based Fault Tolerance in Distributed Sensor Networks”, Journal of Telecommunications and Information Technology, vol.3, pp.1-9, 2014.
  [14] Jin-Shyan. L, Wei-Liang. C, “Fuzzy-Logic-Based Clustering Approach for Wireless Sensor Networks Using Energy Predication, IEEE Journal of Sensors, vol.12, no.9, pp. 281-2897, 2012.
  [15] Mourad. H, Hervé. G, “A Routing Algorithm based on Fuzzy Logic Approach to Prolong the Lifetime of Wireless Sensor Networks”, International Journal of Open Scientific Research, vol.1, no.5, pp. 24-35, 2013.
  [16] J. Shell, S. Coupland, E. Goodyer, “Fuzzy Data Fusion for Fault Detection in Wireless Sensor Networks”, In proceedings of workshop on Computational Intelligence, pp. 1-6, 2010.
  [17] M. Rogaia, A. Nauman, P. William, C. Frank, “An Energy Efficient Fuzzy Logic Cluster Formation Protocol in Wireless Sensor Networks”, In proceedings of 3rd International Conference on Ambient Systems, Networks and Technologies, vol.10, pp. 255 – 262, 2012.
  [18] J. Maryam, H. T., Abolfazl, “A Routing Algorithm based on Fuzzy Clustering and Minimum Cost Tree (FCMCT) in Wireless Sensor Network” Journal of Advances in Computer Research, vol.5, no.3, pp. 113-123, 2014.
  [19] S. Nishant, S. Upinderpal, “A Location Based Approach to Prevent Wormhole Attack in Wireless Sensor Networks”, International Journal of Advanced Research in Computer Science and Software Engineering, vol.4, no.1, pp. 840-845, 2014.
  [20] T. Herbert, L. Donald, “Schilling Principles of Communication, Systems”, McGraw-Hill 1986.
  [21] G. Miao, N. Himayat, Y. G., Li, “Energy Efficient Link Adaptation in Frequency Selective Channels”, IEEE Transaction on Communication, vol.58, no.2, pp. 2010.
  [22] W. Heinzelman, A. Chandrakasan, H. Balakrishnan, “Energy Efficient Communication Protocol for Wireless Micro Sensor Networks”, In the proceedings of the IEEE Hawai International conference on system sciences, vol.8, pp.8020-8030,2000.
  

  Citation
  L. B. Bhajantri, "Fuzzy Logic Based Fault Detection in Distributed Sensor Networks," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.27-32, 2018

• Open Access   Article

  An Efficient Spam Filtering using Supervised Machine Learning Techniques

  Deepika Mallampati

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.2 , pp.33-37, Apr-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.3337

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  Abstract
  Email spam or junk e-mail (unsolicited e-mail “usually of a commercial nature sent out in bulk”) is one of the major problem of the today`s Internet, carrying financial damage to companies and annoying individual users. Among the approaches developed to stop spam, filtering is an important and popular one. Common uses for mail filters comprise organizing incoming email and removal of spam and computer viruses. In proposed work, we employed supervised machine learning techniques to filter the email spam messages. Extensively used supervised machine learning techniques namely C 4.5 Decision tree classifier, Multilayer Perceptron, Naïve Bayes Classifier are used for learning the features of spam emails and the model is built by training with known spam emails and legitimate emails.

  Key-Words / Index Term
  Spam, SpamFilter, Supervised Machine Learning

  References
  [1] Aladdin Knowledge Systems, “Anti-spam white paper,
  www.csisoft.com/security/aladdin/esafe_antispam”, Retrieved December 28, 2011.
  [2] F. Smadja, H. Tumblin, "Automatic spam detection as a text classification task", in: Proc. of Workshop on Operational Text Classification Systems, 2002.
  
  [3] Ann Nosseir , Khaled Nagati and Islam Taj-Eddin, “Intelligent Word-Based Spam Filter Detection Using Multi-Neural Networks”, IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 2, No 1, March 2013 ISSN (Print): 1694-0814 | ISSN (Online): 1694-0784.
  
  [4] R. Kishore Kumar, G. Poonkuzhali, P. Sudhakar,” Comparative Study on Email Spam Classifier using Data Mining Techniques”, Proceedings of the International MultiConference of Engineers and Computer Scientists 2012 Vol I, IMEC2012, March 14-16,2012, Hong Kong, ISBN: 977-988-19251-1-4.
  
  [5] Rafiqul Islam and Yang Xiang, member IEEE, “Email Classification Using Data Reduction Method” created June 16, 2010.
  
  [6] Asmeeta Mali, “Spam Detection Using Baysian with Pattren Discovery”, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-2, Issue-3, July 2013.
  
  [7] Vandana Jaswal, “ Spam Detection System Using Hidden Markov Model”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 7, July 2013 ISSN: 2277 128X.
  [8] Saadat Nazirova, “Survey on Spam Filtering Techniques”, Communications and Network, 2011, 3, 153 160, doi:10.42 36/cn.2011.33019 Published Online August 2011 (http: //www.SciRP.org /journal/cn).
  
  [9] Neha Singh,”Dendritic Cell Algorithm and Dempster Belief Theory Using Improved Intrusion Detection System “, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 7, July 2013 ISSN: 2277 128X.
  
  [10] Julie Greensmith, “The Dendritic Cell Algorithm”, Thesis submitted to the University of Nottingham for the degree of Doctor of Philosophy October 2007.
  

  Citation
  Deepika Mallampati, "An Efficient Spam Filtering using Supervised Machine Learning Techniques," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.33-37, 2018

• Open Access   Article

  Neural Network through Face Recognition

  A.K.Gupta, S.Gupta

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.2 , pp.38-40, Apr-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.3840

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  Abstract
  The aim is to utilise image processing to figure out lip movements and provide lice interaction with the system based on it. The multimodal HCI is displayed which enables a client to take a shot at a PC utilizing developments and motions made with the specific user’s mouth. Calculations for lip development and lip signal acknowledgement are introduced in points of interest. Client confront pictures are caught with a standard webcam. Face identification depends on a course of helped classifiers. Mouth position is utilized to track lip developments that enables a client to control a screen cursor. Three lip signals which are mouth opening, standing out tongue, and framing puckered lips respectively are perceived. An acknowledgment of lip is performed by simulated neural system.

  Key-Words / Index Term
  human-computer interface, image processing; lip gestures, artificial neural network.

  References
  [1]Aggarwal J. K.; Cai Q. (1999): Human Motion Analysis: A Review, CVIU(73), No. 3, pp. 428-440.
  [2]Baecker R. M.; Grudin J.; Buxton W. A. S.; Greenberg S. (Eds.) (1995). Readings in human– computer interaction. Toward the Year 2000, 2nd edn. Morgan Kaufmann, San Francisco.
  [3]Clausi D. A. (2002): An analysis of co-occurrence texture statistics as a function of grey-level quantization, Canadian Journal of Remote Sensing, 28(1), pp. 45-62.
  [4]De Dios J.J.; Garcia, N. (2004): Fast face segmentation in component color space, Int. Conf. on Image Processing, ICIP, 1, pp. 191-194.
  [5]Haralick R. M.; Shanmugam K.; Dinstein I. (1973): Textural Features for Image Classification, IEEE Transactions on Systems, Man, and Cybernetics, 3(6), pp. 610–621.
  [6]Leung S.; Wang S.; Lau W. (2004): Lip image segmentation using fuzzy clustering incorporating an elliptic shape function, IEEE Transactions on Image Processing, 13(1), pp. 51-62.
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  Citation
  A.K.Gupta, S.Gupta, "Neural Network through Face Recognition," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.38-40, 2018

• Open Access   Article

  Ensemble based J48 and random forest based C6H6 air pollution detection

  Gagandeep Kaur, Harmanpreet Kaur

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.2 , pp.41-50, Apr-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.4150

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  Abstract
  Air pollution has become a critical challenge for today’s world. An efficient monitoring of air pollution gases can help to reduce the pollution in the air. Air pollution cause us many diseases such as cancer etc. Benzene (C6H6) turn out to be more challenging issue in our society, because its sensors are costly to deploy and also not feasible to add too many sensors in urban areas. Therefore, in this paper an efficient monitoring of C6H6 gas has been done by using the ensemble approach. It is feasible to estimate C6H6 by using machine learning because there exists relationship between gases. Extensive experiments have been carried out to evaluate the effectiveness of the proposed technique. It has been found that the proposed technique significantly improves the performance of existing machine learning techniques.

  Key-Words / Index Term
  C6H6 • Air pollution • Random forest • Machine learning

  References
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  Citation
  Gagandeep Kaur, Harmanpreet Kaur, "Ensemble based J48 and random forest based C6H6 air pollution detection," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.41-50, 2018

• Open Access   Article

  XY Cut Modular approach for Segmenting pages

  Simple Batra

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.2 , pp.51-56, Apr-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.5156

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  Abstract
  the purpose of this experimental research is to present algorithm for reading contents of documented image. Most of the information that is available today in the world is in printed medium. Printed data has hindered storing, exchanging and processing of this information electronically. Converting them from printed to electronic medium is time consuming as well as expensive as well. These factors have motivated people to develop automated systems to perform such task. Optical Character Recognition (OCR) is an important technique which is very popular among research and technical communities. As a result of these research and development activities several OCR applications are being made available in the market. In this paper, we propose two separate modules to determining the paragraphs and lines in of a document page which is independent of languages.

  Key-Words / Index Term
  OCR, document image, X-Y cut segmentation, over segmentation, under segmentation

  References
  [1]. Faisal Shafait, Daniel Keysers, Thomas M. Breuel, “Pixel-Accurate Representation and Evaluation of Page Segmentation in Document Images”, IEEE, Proceedings of the 18th International Conference on Pattern Recognition (ICPR`06) 2006
  [2]. Antonacopoulos1, B. Gatos2 and D. Karatzas, “Page Segmentation Competition” Proceedings of the Seventh International Conference on Document Analysis and Recognition (ICDAR 2003) 2003 IEEE.
  [3]. Faisal Shafait, Daniel Keysers, and Thomas M. Breuel, “Performance Evaluation and Benchmarking of Six Page Segmentation Algorithms” DRAFT, November 30, 2007
  [4]. Song Mao and Tapas Kanungo, “Empirical Performance Evaluation Methodology and Its Application to Page Segmentation Algorithms”, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 23, NO. 3, MARCH 2001
  [5]. Zhixin Shi and Venu Govindaraju, “Multi-scale Techniques for Document Page Segmentation”, Proceedings of the 2005 Eight International Conference on Document Analysis and Recognition (ICDAR’05) 2005 IEEE
  [6]. Jean-Luc MeunierXerox Research Centre Europe, « Optimized XY-Cut for Determining a Page Reading Order”, Proceedings of the 2005 Eight International Conference on Document Analysis and Recognition (ICDAR’05) 2005 IEEE
  

  Citation
  Simple Batra, "XY Cut Modular approach for Segmenting pages," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.51-56, 2018

• Open Access   Article

  A Framework for Verifying the Authenticity of Banknote on the Automated Teller Machine (ATM) Using Possibilistic C-Means Algorithm

  Oluwabunmi Ayankemi ONI

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.2 , pp.57-63, Apr-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.5763

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  Abstract
  With the introduction of cash-less society policy by the Central Bank of Nigeria, the Nigerian Banking sector over the years has been experiencing significant changes and development in its Information and Communication Technology (ICT). The degree at which bank customers are embracing e-banking services especially Automated Teller Machine (ATM) is increasing exponentially, which have helped in decongesting the banking halls. One worrisome issue associated with this development is that some ATMs are dispensing fake notes especially the higher denominations. This research proposes a framework that will assist in verifying the authenticity of the banknotes on the ATMs using segmentation of the extracted features from banknotes with Possibilistic c-means algorithm. Images of the banknotes will be acquired using a scanner or camera and the acquired image which will be in RGB will be converted to grayscale. The banknote will be denoised after which edge detection operation will be performed. Image segmentation operation will be performed through the PCM which will identity the features of the study banknote that are to be compared with those of the original pre-stored picture in the system. Marches in the banknotes features determine its genuinty.

  Key-Words / Index Term
  Possibilistic C-Means, ATM, Banknotes, E-Banking, Segmentation, Clustering, Framework

  References
  [1]. Kavitha A., China Appala R. Naidu, Srinivasulu P., Meghana K. (2015) - Identify the Fake Currency in Automatic Teller Machine. International Journal of Innovative Research in Computer and Communication Engineering Vol. 3, Issue 11, November 2015 pg11411-11417
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  Citation
  Oluwabunmi Ayankemi ONI , "A Framework for Verifying the Authenticity of Banknote on the Automated Teller Machine (ATM) Using Possibilistic C-Means Algorithm," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.57-63, 2018