LLM: LSTM Look-Ahead Moving Target Defense Based on Historical Malicious Scan
For network defenders, LLM provides a scalable, low-overhead moving target defense that adapts to attacker behavior without requiring large address blocks.
LLM uses LSTM to predict attacker scan targets and dynamically mutate IP addresses, reducing reconfiguration overhead by 40% while maintaining security against network scanning attacks.
Network scanning is a critical preliminary step for most adversaries to gain essential information before launching cyber attacks. Moving Target Defense (MTD) based on IP shuffling has emerged as a proactive defense strategy to counteract these reconnaissance efforts. Unlike static, reactive defense techniques, IP shuffling introduces randomness by dynamically reassigning network addresses, making it more challenging for attackers to identify and track targets. However, current IP shuffling methods face three key challenges: 1) limited scalability across different network topologies, 2) inherent reconfiguration overhead even in the absence of an active attack, and 3) the need for large-scale unused address blocks. To address these issues, we propose LSTM Look-ahead Moving Target Defense (LLM). Our approach is the first attempt using a Long Short-Term Memory (LSTM) network to predict future target addresses that attackers will likely scan. Ensemble learning is used to improve robustness to different scanning behaviors. We introduce a dynamic mutation mechanism to enhance adaptability. Compared to the baseline mutation strategy, LLM performs better in both security and overhead.