Shuguo Zhuo, Nuo Li, Kui Ren (The State Key Laboratory of Blockchain and Data Security, Zhejiang University)

NMFTA Best Short Paper Award Winner ($200 cash prize)!

Due to the absence of encryption and authentication mechanisms, the Controller Area Network (CAN) protocol, widely employed in in-vehicle networks, is susceptible to various cyber attacks. In safeguarding in-vehicle networks against cyber threats, numerous Machine Learning-based (ML) and Deep Learning-based (DL) anomaly detection methods have been proposed, demonstrating high accuracy and proficiency in capturing intricate data patterns. However, the majority of these methods are supervised and heavily reliant on labeled training datasets with known attack types, posing limitations in real-world scenarios where acquiring labeled attack data is challenging. In this paper, we present HistCAN, a lightweight and self-supervised Intrusion Detection System (IDS) designed to confront cyber attacks using solely benign training data. HistCAN employs a hybrid encoder capable of simultaneously learning spatial and temporal features of the input data, exhibiting robust patterncapturing capabilities with a relatively compact parameter set. Additionally, a historical information fusion module is integrated into HistCAN, facilitating the capture of long-term dependencies and trends within the CAN ID series. Extensive experimental results demonstrate that HistCAN generally outperforms the compared baseline methods, achieving a high F1 score of 0.9954 in a purely self-supervised manner while satisfying real-time requirements.

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Binary Code Patching: An Ancient Art Refined for the...

Dr. Barton P. Miller (Vilas Distinguished Achievement Professor at The University of Wisconsin-Madison)

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TALISMAN: Tamper Analysis for Reference Monitors

Frank Capobianco (The Pennsylvania State University), Quan Zhou (The Pennsylvania State University), Aditya Basu (The Pennsylvania State University), Trent Jaeger (The Pennsylvania State University, University of California, Riverside), Danfeng Zhang (The Pennsylvania State University, Duke University)

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GraphGuard: Detecting and Counteracting Training Data Misuse in Graph...

Bang Wu (CSIRO's Data61/Monash University), He Zhang (Monash University), Xiangwen Yang (Monash University), Shuo Wang (CSIRO's Data61/Shanghai Jiao Tong University), Minhui Xue (CSIRO's Data61), Shirui Pan (Griffith University), Xingliang Yuan (Monash University)

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