电力物联网终端漏洞关联挖掘优化算法设计

Design of optimization algorithm for vulnerability correlation mining of power Internet of Things terminals

摘要:

受电力物联网(IoT)复杂性与终端漏洞隐蔽性的共同作用，现阶段采用的传统漏洞关联挖掘方法在关联特征参量上存在局部偏差，造成整体挖掘尺度不足，算法全局寻优效率偏低，严重影响电力IoT终端正常运行。为解决上述问题，从IoT结构特点入手，引入黑盒遗传算法，通过电力IoT终端状态感知、终端漏洞关联挖掘规则生成、黑盒遗传算法参量引入、终端漏洞关联挖掘4部分完成整体挖掘方法全局参量的重构优化，提升挖掘精确度与尺度。仿真测试表明，所提方法的挖掘曲线数值较大，且均值偏差指标差异为0.1，说明黑盒遗传算法在电力IoT终端安全漏洞挖掘中具有较高的可行性和有效性，且挖掘稳定性足以满足现阶段终端漏洞挖掘任务需求。

Abstract:

Affected by the complexity of the power Internet of Things(IoT) and the stealth of terminal vulnerabilities, the traditional vulnerability correlation mining methods currently in use exhibit local biases in correlation feature parameters. This leads to insufficient overall mining scale and low global optimization efficiency of the algorithms, which severely impacts the normal operation of power IoT terminals. To address the aforementioned issues, starting from the structural characteristics of IoT, a black-box genetic algorithm is introduced. By completing the global parameter reconstruction and optimization of the overall mining method through four parts: power IoT terminal status perception, terminal vulnerability correlation mining rule generation, introduction of black-box genetic algorithm parameters, and terminal vulnerability correlation mining, the accuracy and scale of mining are enhanced. Simulation tests indicate that the mining curve values of the proposed method are relatively large, and the mean deviation index difference is 0.1. This demonstrates that the black-box genetic algorithm has high feasibility and effectiveness in the mining of security vulnerabilities in power IoT terminals, and the mining stability is sufficient to meet the current terminal vulnerability mining task requirements.