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.

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Xiaochen Zhu (National University of Singapore & Massachusetts Institute of Technology), Xinjian Luo (National University of Singapore & Mohamed bin Zayed University of Artificial Intelligence), Yuncheng Wu (Renmin University of China), Yangfan Jiang (National University of Singapore), Xiaokui Xiao (National University of Singapore), Beng Chin Ooi (National University of Singapore)

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Yunpeng Tian (Huazhong University of Science and Technology), Feng Dong (Huazhong University of Science and Technology), Haoyi Liu (Huazhong University of Science and Technology), Meng Xu (University of Waterloo), Zhiniang Peng (Huazhong University of Science and Technology; Sangfor Technologies Inc.), Zesen Ye (Sangfor Technologies Inc.), Shenghui Li (Huazhong University of Science and Technology), Xiapu Luo…

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Lifang Xiao (Institute of Information Engineering, Chinese Academy of Sciences), Hanyu Wang (Institute of Information Engineering, Chinese Academy of Sciences), Aimin Yu (Institute of Information Engineering, Chinese Academy of Sciences), Lixin Zhao (Institute of Information Engineering, Chinese Academy of Sciences), Dan Meng (Institute of Information Engineering, Chinese Academy of Sciences)

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Compiled Models, Built-In Exploits: Uncovering Pervasive Bit-Flip Attack Surfaces...

Yanzuo Chen (The Hong Kong University of Science and Technology), Zhibo Liu (The Hong Kong University of Science and Technology), Yuanyuan Yuan (The Hong Kong University of Science and Technology), Sihang Hu (Huawei Technologies), Tianxiang Li (Huawei Technologies), Shuai Wang (The Hong Kong University of Science and Technology)

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