Network entity characterization and attack prediction
This addresses the challenge of prioritizing alerts and predicting attack recurrence in cybersecurity, offering a practical tool for network defense, though it appears incremental as it builds on existing detection methods.
The paper tackles the problem of predicting future cyber-attacks by characterizing network entities, proposing a system (NERDS) that uses machine learning to estimate the probability of malicious behavior based on historical data, with experimental results showing it can precisely rank entities for applications like alert prioritization and blacklisting.
The devastating effects of cyber-attacks, highlight the need for novel attack detection and prevention techniques. Over the last years, considerable work has been done in the areas of attack detection as well as in collaborative defense. However, an analysis of the state of the art suggests that many challenges exist in prioritizing alert data and in studying the relation between a recently discovered attack and the probability of it occurring again. In this article, we propose a system that is intended for characterizing network entities and the likelihood that they will behave maliciously in the future. Our system, namely Network Entity Reputation Database System (NERDS), takes into account all the available information regarding a network entity (e. g. IP address) to calculate the probability that it will act maliciously. The latter part is achieved via the utilization of machine learning. Our experimental results show that it is indeed possible to precisely estimate the probability of future attacks from each entity using information about its previous malicious behavior and other characteristics. Ranking the entities by this probability has practical applications in alert prioritization, assembly of highly effective blacklists of a limited length and other use cases.