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Malicious websites are a major cyber attack vector, and effective detectionof them is an important cyber defense task. The main defense paradigm in thisregard is that the defender uses some kind of machine learning algorithms totrain a detection model, which is then used to classify websites in question.Unlike other settings, the following issue is inherent to the problem ofmalicious websites detection: the attacker essentially has access to the samedata that the defender uses to train its detection models. This 'symmetry' canbe exploited by the attacker, at least in principle, to evade the defender'sdetection models. In this paper, we present a framework for characterizing theevasion and counter-evasion interactions between the attacker and the defender,where the attacker attempts to evade the defender's detection models by takingadvantage of this symmetry. Within this framework, we show that an adaptiveattacker can make malicious websites evade powerful detection models, butproactive training can be an effective counter-evasion defense mechanism. Theframework is geared toward the popular detection model of decision tree, butcan be adapted to accommodate other classifiers.

An Evasion and Counter-Evasion Study in Malicious Websites Detection