Abstract

In today era modern infrastructures and technologies are more prone to various types of accesses. A method that is commonly used for launching these types of attack is popularly known as malware i. e. viruses, Trojan horses and worms, which, when propagate can cause a great damage to commercial companies, private users and governments. The another reason that enhance malware to infect and spread very rapidly is high-speed Internet connections as it has become more popular now a days, therefore it is very important to eradicate and detect new (benign) malware in a prompt manner. Hence in this work, proposing three data mining algorithms to produce new classifiers with separate features: RIPPER, Naïve Bayes and a Multi Classifier system along with hybrid of clustering techniques and the comparison between these methods to predict which provides better results.

References

• Bhavani Thuraisingham, Latifur Khan, Mohammad M. Masud, Kevin W. Hamlen,"Data Mining for Security Applications ",2008 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, pp. 585-589.
• Boldt, M. ; Dept. of Syst. & Software Eng. , Blekinge Inst. of Technol. , Ronneby ; Jacobsson, A. ; Lavesson, N. ; Davidsson, P. , "Automated Spyware Detection Using End User License Agreements" Information Security and Assurance, 2008. ISA 2008. International Conference on 24-26 April 2008; 978-0-7695-3126-7.
• Dr. R. Geetha Ramani, Suresh Kumar. S , Shomona Gracia Jacob"Rootkit (Malicious Code) Prediction through Data Mining Methods and Techniques" , 978-1-4799-1597-2/13/$31. 00 ©2013 IEEE.
• Guillermo Suarez-Tangle, "Evolution, Detection and Analysis of Malware for Smart Devices" IEEE communications surveys & tutorials, accepted for publication, 2013, pp. 1-27.
• Johannes Kinder, "Detecting Malicious Code by Model Checking" In Second Int. Conf. Detection of Intrusions and Malware & Vulnerability Assessment (DIMVA 2005), Springer, 2005, pp. 174–187.
• Kirti Mathur ,Saroj Hiranwal, "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, pp. 422-428.
• Matthew G. Schultz "Data Mining Methods for Detection of New Malicious Executables", IEEE Symposium on Security and Privacy: S amp; P 2001: proceedings: 14-16 May, 2001, pp. 38-49.
• Parisa Bahraminikoo "Utilization Data Mining to Detect Spyware", IOSR Journal of Computer Engineering (IOSRJCE), Volume 4, Issue 3, 2012, pp. 01-04.
• Robert Moskovitch "Detecting unknown malicious code by applying classification techniques on OpCode patterns" Springer-Verlag "http://link. springer. com/article/10. 1186%2F2190-8532-1-1", 2012, pp. 1-22.
• Yuval Elovici, Asaf Shabtai, Robert Moskovitch, Gil Tahan, and Chanan Glezer" Applying Machine Learning Techniques for Detection of Malicious Code in Network Traffic". Proceedings of the 30th annual German conference on Advances in Artificial Intelligence, KI 2007, pp. 10-13.