Evaluating Performance and Security of a Hybrid Moving Target Defense in SDN Environments

As cyberattacks are rising, Moving Target Defense (MTD) can be a countermeasure to proactively protect a networked system against cyber-attacks. Despite the fact that MTD systems demonstrate security effectiveness against the reconnaissance of Cyber Kill Chain (CKC), a time-based MTD has a limitation when it comes to protecting a system against the next phases of CKC. In this work, we propose a novel hybrid MTD technique, its implementation and evaluation. Our hybrid MTD system is designed on a real SDN testbed and it uses an intrusion detection system (IDS) to provide an additional MTD triggering condition. This in itself presents an extra layer of system protection. Our hybrid MTD technique can enhance security in the response to multi-phased cyber-attacks. The use of the reactive MTD triggering from intrusion detection alert shows that it is effective to thwart the further phase of detected cyber-attacks. We also investigate the performance degradation due to more frequent MTD triggers. This work contributes to (1) proposing an ML-based rule classification model for predicting identified attacks which helps a decision-making process for security enhancement; (2) developing a hybrid-based MTD integrated with a Network Intrusion Detection System (NIDS) with the consideration of performance and security; and (3) assessment of the performance degradation and security effectiveness against potential real attacks (i.e., scanning, dictionary, and SQL injection attack) in a physical testbed.