AutoCEW: An Autonomous Cyberspace Early Warning Framework via Ensemble Learning
Qiang Liu, Yifei Gao, Runhao Liu, Jiayao Wang · 2021
Nowadays, cyberspace is increasingly connected with our daily lives. In the mean time, cyber space is rife with security adversaries due to untrusty entities and connections. Therefore, the use of technical means to alert security threats in their early stages is of great importance. To fill the gap between theoretical and practical works in the field of cybersecurity early warning, we propose an Autonomous Cyberspace Early Warning (AutoCEW) framework via ensemble learning in this paper. Specifically, we design the AutoCEW core ecosystem based on artificial intelligence, and the ecosystem contains three core functions, namely the first-stage threat identification using character features, the second-stage threat identification using statistical features, and cyber threat alerts & early warning. Furthermore, we also implement an AutoCEW prototype system and then demonstrate its good performance in terms of high detecting accuracy and low processing latency over real-world, simulation and synthetic traffic data.