Unsupervised intrusion detection model based on temporal convolutional network - LIAO Jinju; DING Jiawei; FENG Guanghui (Dianxin kexue)
Most existing intrusion detection models rely on long short-term memory (LSTM) networks to consider time-dependencies among data. However, LSTM’s sequential data processing significantly increases computational complexity and memory consumption during training. Therefore, unsupervised intrusion detection model based on multi-head attention mechanism and temporal convolutional network (UDMT) was proposed. UDMT didn’t rely on LSTM networks. Instead, it used temporal convolutional network and multi-head attention mechanism in the generative adversarial network generator and discriminator networks to enable more computation parallelization, and reduced computational complexity. Moreover, UDMT was capable of detecting both known and zero-day attacks without relying on labeled attack data. In addition, UDMT can adopt different privacy layer modes, and the configuration was flexible to meet the requirements of different detection rates and detection delays. Experiment results show that the proposed UDMT has higher detection rate and lower detection latency than two state-of-the-art intrusion detection models.