MS-ZeroWall: Detecting Zero-Day Multi-Step Attack in Smart Home Using VAE and HMM
Taotao Li, Zhen Hong, W. Feng, Li Juan Yu, Zhenyu Wen · IEEE Transactions on Vehicular Technology · 2024
The development of technologies in the smart home provides convenience to our living life, however, the resourceconstrained characteristics lead to its frequent exposure to various attacks. Previous techniques for attack detection in the Internet of Things (IoT) are primarily fitted to simple attacks (i.e., single-step attack) but are insufficient analysis for dynamic detection of so-called complicated attacks (i.e., multistep attacks). Usually, there are three challenges for deploying a multi-step attack prediction system under IoT: 1) resourceconstrained, 2) frequently unknown multi-step threats, and 3) high demand for real-time. To address these challenges, in this paper, we propose a multi-step attack prediction architecture (namely, MS-ZeroWall) applied in the smart home. Firstly, the MS-ZeroWall does not need expensive software, which can be easily deployed on resource-constrained IoT. Then, it captures the characteristics of known threats through the variational autoencoder (VAE) and uses a VAE-based dual-domain defense strategy (DVAE) to achieve unknown multi-step threat identification. In addition, MS-ZeroWall automatically model any multi-step attack by combining the hidden Markov model (HMM) and VAE, and it uses an aggregated HMM (AHMM) approach to improve the multi-step attacks prediction under low time-delay windows so as to satisfy real-time. We evaluate the MS-ZeroWall on a publicly available multi-step attack dataset, with anF1- score of over 0.96 on unknown multi-step threat identification, and an average accuracy improvement of 12.3% on low-latency multi-step attack prediction.