Security Enhancement Technology for Power Internet of Things Protocol and Vulnerability Mining of Edge Devices Based on Fuzz Testing

• Main Authors: Liu, Donglan, Liu, Xin, Chang, Yingxian, Chen, Jianfei, Zhang, Fangzhe, Wang, Rui, Zhang, Hao, Sun, Lili, Zhao, Fuhui, Sun, Mengqian
• Format: Conference Proceeding
• Language: English
• Subjects: Cryptography; Elliptic curve cryptography; fuzz testing; Fuzzing; Internet of Things; lightweight cryptography; new trusted technologies; Optimization methods; Performance evaluation; power Internet of Things protocol; Protocols; safety enhancement technology; vulnerability mining technology
• ISSN: 2377-844X
• DOI: 10.1109/ICEIEC58029.2023.10199571

In order to solve the security problems such as the lack of secure key management in the electric Internet of Things protocol, this paper studies the security enhancement technology of electric Internet of Things protocol based on lightweight cryptography technology and new trusted technology. A key exchange scheme based on lightweight cryptography technology is proposed to adapt to the resource-limited scenario of the Internet of Things. To protect keys and data on restricted devices and remote servers, explore lightweight cryptography and new trusted technologies. Performs cryptographic operations without exposing the private key and is resistant to tampering and probing attacks. To realize end-to-end communication and key management between Internet of Things devices and remote servers. At the same time, based on the fuzz testing technology, the unknown vulnerability mining technology of the edge device protocol of the electric Internet of Things is carried out. Firstly, the fuzz testing case intelligent optimization method is studied, and the test case set is reduced based on artificial intelligence algorithms such as genetic algorithm, protocol state machine and stain algorithm, so as to improve the test efficiency. Then, the unknown vulnerability discovery method based on state monitoring is studied, and the test case can be judged whether abnormal occurs by monitoring the state of returned data packets of the tested object, so as to determine whether there are vulnerabilities. Finally, the method of vulnerability intelligent location based on automatic location analysis is studied to accurately locate the test cases that cause vulnerability.