Kai Wang (Tsinghua University), Zhiliang Wang (Tsinghua University), Dongqi Han (Tsinghua University), Wenqi Chen (Tsinghua University), Jiahai Yang (Tsinghua University), Xingang Shi (Tsinghua University), Xia Yin (Tsinghua University)

Deep learning (DL) performs well in many traffic analysis tasks. Nevertheless, the vulnerability of deep learning weakens the real-world performance of these traffic analyzers (e.g., suffering from evasion attack). Many studies in recent years focused on robustness certification for DL-based models. But existing methods perform far from perfectly in the traffic analysis domain. In this paper, we try to match three attributes of DL-based traffic analysis systems at the same time: (1) highly heterogeneous features, (2) varied model designs, (3) adversarial operating environments. Therefore, we propose BARS, a general robustness certification framework for DL-based traffic analysis systems based on boundary-adaptive randomized smoothing. To obtain tighter robustness guarantee, BARS uses optimized smoothing noise converging on the classification boundary. We firstly propose the Distribution Transformer for generating optimized smoothing noise. Then to optimize the smoothing noise, we propose some special distribution functions and two gradient based searching algorithms for noise shape and noise scale. We implement and evaluate BARS in three practical DL-based traffic analysis systems. Experiment results show that BARS can achieve tighter robustness guarantee than baseline methods. Furthermore, we illustrate the practicability of BARS through five application cases (e.g., quantitatively evaluating robustness).

View More Papers

Improving In-vehicle Networks Intrusion Detection Using On-Device Transfer Learning

Sampath Rajapaksha (Robert Gordon University), Harsha Kalutarage (Robert Gordon University), M.Omar Al-Kadri (Birmingham City University), Andrei Petrovski (Robert Gordon University),...

Read More

MetaWave: Attacking mmWave Sensing with Meta-material-enhanced Tags

Xingyu Chen (University of Colorado Denver), Zhengxiong Li (University of Colorado Denver), Baicheng Chen (University of California San Diego), Yi...

Read More

Ethical Challenges in Blockchain Network Measurement Research

Yuzhe Tang (Syracuse University), Kai Li (San Diego State University), and Yibo Wang and Jiaqi Chen (Syracuse University)

Read More

CLExtract: Recovering Highly Corrupted DVB/GSE Satellite Stream with Contrastive...

Minghao Lin (University of Colorado Boulder), Minghao Cheng (Independent Researcher), Dongsheng Luo (Florida International University), Yueqi Chen (University of Colorado...

Read More