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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Eavesdropping on Black-box Mobile Devices via Audio Amplifier's EMR

Huiling Chen (College of Computer Science and Electronic Engineering, Hunan University, Changsha, China), Wenqiang Jin (College of Computer Science and Electronic Engineering, Hunan University, Changsha, China), Yupeng Hu (College of Computer Science and Electronic Engineering, Hunan University, Changsha, China), Zhenyu Ning (College of Computer Science and Electronic Engineering, Hunan University, Changsha, China), Kenli Li (College…

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DeepGo: Predictive Directed Greybox Fuzzing

Peihong Lin (National University of Defense Technology), Pengfei Wang (National University of Defense Technology), Xu Zhou (National University of Defense Technology), Wei Xie (National University of Defense Technology), Gen Zhang (National University of Defense Technology), Kai Lu (National University of Defense Technology)

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On the Vulnerability of Traffic Light Recognition Systems to...

Sri Hrushikesh Varma Bhupathiraju (University of Florida), Takami Sato (University of California, Irvine), Michael Clifford (Toyota Info Labs), Takeshi Sugawara (The University of Electro-Communications), Qi Alfred Chen (University of California, Irvine), Sara Rampazzi (University of Florida)

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WIP: A First Look At Employing Large Multimodal Models...

Mohammed Aldeen, Pedram MohajerAnsari, Jin Ma, Mashrur Chowdhury, Long Cheng, Mert D. Pesé (Clemson University)

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