TY - GEN
T1 - Optimizing IoT Cross-rule Vulnerability Detection through Reinforcement Learning-Based Fuzzing
AU - Huynh, Tran Ngoc Bao
AU - Xu, Ting
AU - Wan, Yinxin
AU - Dai, Jun
AU - Sun, Xiaoyan
N1 - Publisher Copyright:
© 2025 Copyright held by the owner/author(s).
PY - 2025/5/6
Y1 - 2025/5/6
N2 - Internet of Things (IoT) devices have become increasingly ubiquitous and essential to daily life. These devices are usually controlled based on trigger-action rules, meaning that the devices will take actions according to the rules when trigger conditions are satisfied. As more devices are deployed in smart home systems, the risk of undesirable interactions and cross-rule vulnerabilities increases. In this paper, we propose a reinforcement learning-based fuzzing approach that can automate the modification of environmental variables to generate test cases and increase the likelihood of discovering cross-rule conflicts in smart home systems. Our approach optimizes conflict detection and discovers hidden conditions that lead to vulnerabilities. The preliminary results show that our model can successfully recognize different types of rule conflict.
AB - Internet of Things (IoT) devices have become increasingly ubiquitous and essential to daily life. These devices are usually controlled based on trigger-action rules, meaning that the devices will take actions according to the rules when trigger conditions are satisfied. As more devices are deployed in smart home systems, the risk of undesirable interactions and cross-rule vulnerabilities increases. In this paper, we propose a reinforcement learning-based fuzzing approach that can automate the modification of environmental variables to generate test cases and increase the likelihood of discovering cross-rule conflicts in smart home systems. Our approach optimizes conflict detection and discovers hidden conditions that lead to vulnerabilities. The preliminary results show that our model can successfully recognize different types of rule conflict.
KW - fuzzing
KW - IoT
KW - reinforcement learning
KW - vulnerability detection
UR - https://www.scopus.com/pages/publications/105035735061
UR - https://www.scopus.com/pages/publications/105035735061#tab=citedBy
U2 - 10.1145/3715014.3724024
DO - 10.1145/3715014.3724024
M3 - Conference contribution
AN - SCOPUS:105035735061
T3 - ACM SenSys 2025 - 23rd ACM Conference on Embedded Networked Sensor Systems, In Transactions to Conference Embedded Artificial Intelligence and Sensing Systems
SP - 594
EP - 595
BT - ACM SenSys 2025 - 23rd ACM Conference on Embedded Networked Sensor Systems, In Transactions to Conference Embedded Artificial Intelligence and Sensing Systems
PB - Association for Computing Machinery, Inc
T2 - 23rd ACM Conference on Embedded Networked Sensor Systems, SenSys 2025
Y2 - 6 May 2025 through 9 May 2025
ER -