Sian Kim (Ewha Womans University), Seyed Mohammad Mehdi Mirnajafizadeh (Wayne State University), Bara Kim (Korea University), Rhongho Jang (Wayne State University), DaeHun Nyang (Ewha Womans University)
Intelligent Network Data Plane (INDP) is emerging as a promising direction for in-network security due to the advancement of machine learning technologies and the importance of fast mitigation of attacks. However, the feature extraction function still poses various challenges due to multiple hardware constraints in the data plane, especially for the advanced per-flow 3rd-order features (e.g., inter-packet delay and packet size distributions) preferred by recent security applications. In this paper, we discover novel attack surfaces of state-of-the-art data plane feature extractors that had to accommodate the hardware constraints, allowing adversaries to evade the entire attack detection loop of in-network intrusion detection systems. To eliminate the attack surfaces fundamentally, we pursue an evolution of a probabilistic (sketch) approach to enable flawless 3rd-order feature extraction, highlighting High-resolution, All-flow, and Full-range (HAF) 3rd-order feature measurement capacity. To our best knowledge, the proposed scheme, namely SketchFeature, is the first sketch-based 3rd-order feature extractor fully deployable in the data plane. Through extensive analyses, we confirmed the robust performance of SketchFeature theoretically and experimentally. Furthermore, we ran various security use cases, namely covert channel, botnet, and DDoS detections, with SketchFeature as a feature extractor, and achieved near-optimal attack detection performance.