Detecting and Handling WoT Violations by Learning Physical Interactions from Device Logs
The Web of Things (WoT) system standardizes the integration of ubiquitous IoT devices in physical environments, enabling various software applications to automatically sense and regulate the physical environment. While providing convenience, the complex interactions among software applications and physical environment make the WoT system vulnerable to violations caused by improper actuator operations, which may cause undesirable or even harmful results, posing serious risks to user safety and security. In response to this critical concern, many previous efforts have be made. However, existing works primarily focus on analyzing software application behaviors, with insufficient consideration of the physical interactions, multi-source violations, and environmental dynamics in such ubiquitous software systems. As a result, they fail to comprehensively detect the impact of actuator operations on the dynamic environment, thus limiting their effectiveness.
To address these limitations, we propose SysGuard, a violation detecting and handling approach. SysGuard employs the dynamic probabilistic graphical model (DPGM) to model the physical interactions as the physical interaction graph (PIG). In the offline phase, SysGuard takes device description models and history device logs as input to capture physical interactions by learning the PIG. In this process, a large language model (LLM) based causal analysis method is further introduced to filter out the device dependencies unrelated to physical interaction by analyzing the device interaction scenarios recorded in device logs. In the online phase, SysGuard processes user-customized violation rules, and monitors runtime device logs to predict violation states and generates handling policies by inferring the PIG. Evaluation on two real-world WoT systems shows that SysGuard significantly outperforms existing state-of-the-art works, achieving high performance in both violation detecting and handling. It also confirms the runtime efficiency and scalability of SysGuard. Ablation experiment on our constructed dataset demonstrates that the LLM-based causal analysis significantly improve the performance of SysGuard, with the accuracy increasing in both violation detecting and handling.
Tue 24 JunDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
16:00 - 17:40 | Anomaly DetectionIdeas, Visions and Reflections / Research Papers / Industry Papers at Pirsenteret 150 Chair(s): Gias Uddin York University, Canada | ||
16:00 20mTalk | Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning Research Papers Yuqing Wang University of Helsinki, Finland, Mika Mäntylä University of Helsinki and University of Oulu, Serge Demeyer University of Antwerp and Flanders Make vzw, Mutlu Beyazıt University of Antwerp and Flanders Make vzw, Joanna Kisaakye University of Antwerp, Belgium, Jesse Nyyssölä University of Helsinki DOI | ||
16:20 10mTalk | CLSLog: Collaborating Large and Small Models for Log-based Anomaly Detection Ideas, Visions and Reflections Pei Xiao Peking University, Tong Jia Institute for Artificial Intelligence, Peking University, Beijing, China, Chiming Duan Peking University, Minghua He Peking University, Weijie Hong Peking university, Xixuan Yang School of Software and Microelectronics, Peking University, Yihan Wu National Computer Network Emergency Response Technical Team/Coordination Center of China, Ying Li School of Software and Microelectronics, Peking University, Beijing, China, Gang Huang Peking University | ||
16:30 10mTalk | From Few-Label to Zero-Label: An Approach for Cross-System Log-Based Anomaly Detection with Meta-Learning Ideas, Visions and Reflections Xinlong Zhao Peking University, Tong Jia Institute for Artificial Intelligence, Peking University, Beijing, China, Minghua He Peking University, Yihan Wu National Computer Network Emergency Response Technical Team/Coordination Center of China, Ying Li School of Software and Microelectronics, Peking University, Beijing, China, Gang Huang Peking University | ||
16:40 20mTalk | CAShift: Benchmarking Log-Based Cloud Attack Detection under Normality Shift Research Papers Jiongchi Yu Singapore Management University, Xiaofei Xie Singapore Management University, Qiang Hu Tianjin University, Bowen Zhang Singapore Management University, Ziming Zhao Zhejiang University, Yun Lin Shanghai Jiao Tong University, Lei Ma The University of Tokyo & University of Alberta, Ruitao Feng Southern Cross University, Frank Liauw Government Technology Agency Singapore DOI Pre-print | ||
17:00 20mTalk | Detecting and Handling WoT Violations by Learning Physical Interactions from Device Logs Research Papers Bingkun Sun Fudan University, Shiqi Sun Northwestern Polytechnique University, Jialin Ren Fudan University, Mingming Hu Fudan University, Kun Hu School of Computer Science, Fudan University, Liwei Shen Fudan University, Xin Peng Fudan University DOI | ||
17:20 20mTalk | L4: Diagnosing Large-scale LLM Training Failures via Automated Log Analysis Industry Papers Zhihan Jiang The Chinese University of Hong Kong, Junjie Huang The Chinese University of Hong Kong, Guangba Yu The Chinese University of Hong Kong, Zhuangbin Chen Sun Yat-sen University, Yichen LI The Chinese University of Hong Kong, Renyi Zhong The Chinese University of Hong Kong, Cong Feng Huawei Cloud Computing Technology, Yongqiang Yang Huawei Cloud Computing Technology, Zengyin Yang Computing and Networking Innovation Lab, Huawei Cloud Computing Technology Co., Ltd, Michael Lyu Chinese University of Hong Kong | ||
This room is located outside Clarion Hotel
This room is located in the Pirsenteret (The Pier Center) convention center. It is just outside the hotel, on the back, towards the fjord.
You should be able to go through the emergency exit at Clarion, just on the side of the Cosmos 3 wing, which will be bring you close to Pirsenteret.
The entrance to the center is from here:
https://maps.app.goo.gl/dU3qH6kAimXGBNHe7
Once inside, go all straight and you will find signage to reach the room. The room is known as room 150 inside the center.