International Journal of
Computer Sciences and Engineering

Scholarly, Peer-Reviewed and Fully Refereed Academic Research Journal

Flash News 

Full paper submission has now been opened for August edition. You can upload your full paper using the required templates to the Online Submission System. Deadline for uploading the full papers is 22 August 2018.

Performance Study on Malicious Program Prediction Using Classification Techniques
Open Access   Article

Performance Study on Malicious Program Prediction Using Classification Techniques
K. Thyagarajan1 , N. Vaishnavi2
1 Dept of Computer Science, AVC College, Mayiladuthurai, India.
2 Dept of Computer Science, AVC College, Mayiladuthurai, India.

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-5 , Page no. 59-64, May-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i5.5964

Online published on May 31, 2018

Copyright © K. Thyagarajan, N. Vaishnavi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 
View this paper at   Google Scholar | DPI Digital Library
  XML View PDF Download  
Citation

IEEE Style Citation: K. Thyagarajan, N. Vaishnavi, “Performance Study on Malicious Program Prediction Using Classification Techniques”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.59-64, 2018.

MLA Style Citation: K. Thyagarajan, N. Vaishnavi "Performance Study on Malicious Program Prediction Using Classification Techniques." International Journal of Computer Sciences and Engineering 6.5 (2018): 59-64.

APA Style Citation: K. Thyagarajan, N. Vaishnavi, (2018). Performance Study on Malicious Program Prediction Using Classification Techniques. International Journal of Computer Sciences and Engineering, 6(5), 59-64.
VIEWS PDF XML
64 100 downloads 8 downloads
  
  
           
Abstract :
Data mining is that the method of move queries and extracting patterns, typically antecedently unknown from giant quantities of data using pattern matching several applications in security as well as for national security likewise as for cyber security. Research focus on Detecting Malicious Packet uses weka. Once network routers are a unit subverted to act during a malicious fashion. To observe the existence of compromised routers during a network, then take away them from the routing fabric. Our approach is to separate the matter into three sub-problems: 1) crucial the traffic data to record upon that to base the detection, 2) synchronizing routers to gather traffic data and distributing this data among them thus detection will occur, and 3) taking countermeasures once detection happens. Experimental results show that ready to observe and isolate a spread of malicious router actions with acceptable overhead and quality. Our work has ready to tolerate attacks on key network infrastructure elements.
Key-Words / Index Term :
Data mining, Malicious program, JRip, PART, OneR, Malicious classifier, classification, WEKA tool
References :
[1] I.H. Witten, E. Frank, “Data Mining: Practical Machine Learning Tools and Techniques”, 2nd ed.Morgan Kaufmann, 2005.
[2] M. G. Schultz, E. Eskin, E. Z., and S. J. Stolfo, ”Data mining methods for detection of new malicious executables,” in Proceedings of the IEEE Symp. on Security and Privacy, pp. 38-49, 2001.
[3] W. Cohen, .“Fast effective rule induction,.” Proc. 12th International Conference on Machine Learning, San Francisco, CA: Morgan Kaufmann Publishers, pp. 115-23, 1995.
[4] J. Z. Kolter and M. A. Maloof, “Learning to Detect Malicious Executables in the wild,” in Proceedings of the ACM Symp. on Knowledge Discovery and Data Mining (KDD), pp. 470-478,August 2004.
[5] T. Fawcett, “ROC Graphs: Notes and Practical Considerations for Researchers”, TR HPL-2003-4, HP Labs, USA, 2004.
[6] M. Siddiqui, M. C. Wang, J. Lee, “Detecting Internet worms Using Data Mining Techniques”, Journalof Systemics, Cybernetics and Informatics, volume 6 - number 6, pp: 48-53, 2009.
[7] Johannes kinder, “Detecting Malicious Code by Model Checking”,pure.rhul.ac.uk/portal/files/17566588/mcodedimva05.pdf.
[8] Bhavani Thuraisingham, “Data Mining for Security Applications”, IEEE/IFIP International Conference on Embedded and Ubiquitous Computing,2008 .
[9] Kirti Mathur, “ A Survey on Techniques in Detection and Analyzing Malware Executables”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 4, April 2013.
[10] Guillermo Suarez-Tangle, “Evolution, Detection and Analysis of Malware for Smart Devices” IEEE communications surveys & tutorials, accepted for publication, pp.1-27, 2013.
[11] Parisa Bahraminikoo “Utilization Data Mining to Detect Spyware”, IOSR Journal of Computer Engineering (IOSRJCE),Volume 4, Issue 3, pp.01-04,2012.
[12] F. Leon, M. H. Zaharia and D. Galea, “Performance Analysis of Categorization Algorithms,” International Symposium on Automatic Control and Computer Science, (2004).
[13] E. Frank and I. H. Witten, “Generating Accurate Rule Sets Without Global Optimization,” International Conference on Machine Learning, pages 144-151, (1998).
[14] Gaya Buddhinath and Damien Derry, "A Simple Enhancement to One Rule Classification", Department of Computer Science & Software Engineering. University of Melbourne, Australia, (2006).
[15] Umesh Kumar Singh, Jalaj Patidar and Kailash Chandra Phuleriya, "On Mechanism to Prevent Cooperative Black
Hole Attack in Mobile Ad Hoc Networks", International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.1, pp.11-15, 2015.
[16] Meenakshi Jamgade and Vimal Shukla , "Comparative on AODV and DSR under Black Hole Attacks Detection Scheme Using Secure RSA Algorithms in MANET", International Journal of Computer Sciences and Engineering, Vol.4, Issue.2, pp.145-150, 2016.
[17] L. Khan, M. Awad, and B. Thuraisingham, “A New Intrusion Detection System using Support Vector Machines and Hierarchical Clustering”, The VLDB Journal: ACM/Springer-Verlag, 16(1), page 507-521, 2007.
[18] M. M. Masud, L. Khan, and B. Thuraisingham, “Feature based Techniques for Auto-detection of Novel Email Worms”, In Proc. 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2007), Nanjing, China, May 2007, page 205-216.
[19] M. M. Masud, L. Khan, B. Thuraisingham, , X. Wang, P. Liu, and S. Zhu, “A Data Mining Technique to Detect Remote Exploits”, In Proc. IFIP WG 11.9 International Conference on Digital Forensics, Japan, Jan 27-30, 2008.
[20] Bhavani Thuraisingham, “Data Mining for Security Application”s, IEEE/IFIP International Conference on Embedded and Ubiquitous Computing,2008 .
[21] N. Landwehr, M. Hall, and E. Frank, “Logistic model trees”. For Machine Learning, Vol. 59(1-2), pp.161-205, (2005).
[22] M. G. Schultz, E. Eskin, E. Zadok and S. J. Stolfo, “Data Mining Methods for Detection of New Malicious Executables”, Proceedings of the IEEE Symposium on Security and Privacy, IEEE Computer Society, 2001.