POSTER: Toward Intelligent Cyber Attacks for Moving Target Defense Techniques in Software-Defined Networking

Abstract

Moving Target Defenses (MTD) are proactive security countermeasures that change the attack surface in a system in ways that make it harder for attackers to succeed. These techniques have been shown to be effective, and their application in software-defined networking (SDN) against simple automated attacks is growing in popularity. However, with the increased knowledge of and ease of access to Artificial Intelligence (AI) techniques, AI is starting to be used to enhance cyber attacks, which are becoming increasingly complex. Hence, the evaluation of MTDs against simple automated attacks is no longer enough to demonstrate their effectiveness in increasing system security. With this in mind, we propose a novel framework to evaluate MTD techniques in SDN. To this end, first, we develop a taxonomy of possible intelligent attacks against MTD techniques. Second, we show how our framework can be used to generate datasets to realize these intelligent attacks for evaluating and enhancing MTD techniques. Third, we experimentally demonstrate the feasibility of the proposed machine learning (ML) powered attacks, with an attacker who can determine the MTD trigger time from network traffic using ML, which they can use to maximize their attack window and increase their chances of success.