N. Rajkumar, Ms. C. Viji, Assistant professor, SVS College of Engineering, Coimbatore, Tamilnadu, India.
S. Duraisamy, Professor & Head, Sri Krishna College of Engg & Tech, Coimbatore, Tamilnadu, India.
DOI : 01.0401/ijaict.2014.05.07
International Journal of Advanced Information and Communication Technology
Received On : December 13, 2016
Revised On : January 20, 2016
Accepted On : February 17, 2016
Published On : March 05, 2016
Volume 03, Issue 03
Pages : 452-456
Abstract
There are many classification algorithms were employed successfully for the detection of unknown malicious code. Most of these studies extracted features based on byte n-gram patterns in order to represent the inspected files. In this study were present the inspected files using OpCode n-gram patterns which are extracted from the files after disassembly. The OpCode n-gram patterns are used as features for the classification process. The classification process main goal is to detect unknown malware within a set of suspected files which will later be included in antivirus software as signatures. A rigorous evaluation was performed using a test collection comprising of more than 30,000 files, in which various settings of OpCode n- gram patterns of various size representations and eight types of classifiers were evaluated. A typical problem of this domain is the imbalance problem in which the distribution of the classes in real life varies. We investigated the imbalance problem, referring to several real-life scenarios in which malicious files are expected to be about 10% of the total inspected files. Lastly, we present a chronological evaluation in which the frequent need for updating the training set was evaluated. Evaluation results indicate that the evaluated methodology achieves a level of accuracy higher than 96% (with TPR above 0.95 and FPR approximately 0.1), which slightly improves the results in previous byte n-gram representation.
Keywords
N-Grampattern, Opcode, Code Emulation, Pattern Based Scanning.
Cite this article
N. Rajkumar, C. Viji, S. Duraisamy, “Fault Prediction in Object Oriented Systems using Malicious Code Detection” INTERNATIONAL JOURNAL OF ADVANCED INFORMATION AND COMMUNICATION TECHNOLOGY, pp.452-456, March 05, 2016.
Copyright
© 2016 N. Rajkumar, C. Viji, S. Duraisamy. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.