Adaptive Worm Detection Model Based on Multi Classifiers
作者： T. S. Barhoom , H. A. Qeshta
关键词:
摘要: Security has become ubiquitous in every area of malware newly emerging today pose a growing threat from ever perilous systems. As result to that, Worms are the upper part threats attacking computer system despite evolution worm detection techniques. Early unknown worms is still problem. In this paper, we proposed "WDMAC" model for worm's using data mining techniques by combination classifiers (Naive Bayes, Decision Tree, and Artificial Neural Network) multi be adaptive detecting known/ depending on behavior-anomaly approach, achieve higher accuracies rate, lower classification error rate. Our results show that achieved rates classification, where known at least 98.30%, with rate 1.70%, while about 97.99%, 2.01%.
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