Addressing the attack attribution problem using knowledge discovery and multi-criteria fuzzy decision-making

In network traffic monitoring, and more particularly in the realm of threat intelligence, the problem of \attack attribution" refers to the process of effectively attributing new attack events to (un)-known phenomena, based on some evidence or traces left on one or several monitoring platforms. Real-world attack phenomena are often largely distributed on the Internet, or can sometimes evolve quite rapidly. This makes them inherently complex and thus difficult to analyze. In general, an analyst must consider many different attack features (or criteria) in order to decide about the plausible root cause of a given attack, or to attribute it to some given phenomenon. In this paper, we introduce a global analysis method to address this problem in a systematic way. Our approach is based on a novel combination of a knowledge discovery technique with a fuzzy inference system, which somehow mimics the reasoning of an expert by implementing a multi-criteria decision-making process built on top of the previously extracted knowledge. By applying this method on attack traces, we are able to identify largescale attack phenomena with a high degree of confidence. In most cases, the observed phenomena can be attributed to so-called zombie armies - or botnets, i.e. groups of compromised machines controlled remotely by a same entity. By means of experiments with real-world attack traces, we show how this method can effectively help us to perform a behavioral analysis of those zombie armies from a long-term, strategic viewpoint.