Anomaly Based Worm Detection Using Deterministic Finite Automata and Efficient Keygraph Technique
Leelavathi B, Rajesh Babu M
Journal of Data Processing, Vol. 8, No. 2, 2018.
DOI: 10.6025/jdp/2018/8/2/63-73
Link: http://www.dline.info/jdp/fulltext/v8n2/jdpv8n2_2.pdf
Abstract:
Nowadays, detecting unknown computer worms is a challenging task. Anti-virus is the feasible solution for this
problem but anti viruses that contains signature based methods are clueless against new unknown worms and it cannot
detect new worms until it is updated with new worm signatures. This paper focuses on improving the accuracy of detecting the
unknown worm while reducing the feature set extracted using DFA. Gain ratio method is implemented to reduce the amount
of features. Keygraph and data crystallization classifier is proposed for classifying the unknown attacks. The dataset is
collected from the VX Heaven virus collection website which contains the sources and samples of the virus collection.
Extensive experiment was performed for testing the feasibility of detecting unknown worms. The evaluation results shows that
by applying classification algorithms, the detection ratio obtained for specific unknown worms exceeds 98%.