Network entity characterization and attack prediction

TL;DR

This paper introduces NERDS, a machine learning-based system for characterizing network entities and predicting their likelihood of malicious behavior, aiding in attack detection, alert prioritization, and blacklist management.

Contribution

The paper presents a novel system that combines network entity data with machine learning to accurately estimate future attack probabilities, improving cybersecurity defenses.

Findings

High accuracy in predicting malicious behavior

Effective prioritization of alerts based on risk levels

Enhanced blacklisting strategies for limited resources

Abstract

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.