Paper #4

Intelligent Scanning and Remediation Algorithms for Information Security Vulnerabilities in IoT Terminals

Pengcheng He and Weiju Kong

Abstract: The number of IoT terminal devices is growing rapidly. However, due to limited computing resources, diverse architectures, and difficulties in updating algorithm models, traditional vulnerability detection and fault repair mechanisms are inapplicable. Therefore, this paper constructs a security vulnerability scanning algorithm for IoT terminals. The IV-PARM model constructed in this paper combines multiple features, including data from three modalities: firmware semantics, runtime behavior, and network traffic, to build a heterogeneous graph neural network. Device vulnerability association inference, combined with risk perception, is generated based on a reinforcement learning model. An adaptive and lightweight repair strategy is used to guide the update of IoT terminals. The model framework of this paper adopts a three-level collaborative approach, which can reduce the resource overhead of the terminal for vulnerability detection while ensuring the accuracy of vulnerability detection. This paper conducts experiments on 1200 real samples and 120,000 behavior logs. The experimental results show that the IV-PARM model can achieve an accuracy of 85.7% in vulnerability detection, with a false positive rate of only 2.3% and a repair success rate as high as 91%. On the other hand, in terms of latency, the average latency of the model is less than 45 milliseconds, and the memory usage is less than 15 MB. The experimental results in this paper demonstrate that the proposed model has significant advantages in terms of effectiveness, robustness, and inference.

Keywords: Adaptive repair; Graph neural network; IoT security; Intelligent vulnerability detection; Reinforcement learning.