一种基于小生境遗传算法的电网信息安全风险评估模型

信息安全保障对于电力信息物理系统安全稳定运行至关重要,其关键在于对电力信息物理系统进行全方位实时监控,并对采集到的海量监测数据进行分析以做出准确的安全风险评估结果。作为用于模式分类的进化算法,基因表达式编程(gene expression programming,GEP)算法由于其可以执行全局搜索而受到广泛关注,但其在高维度数据集下的运算极为耗时。针对上述问题,提出了一种基于小生境提高样本多样性的改进基因表达式编程算法用于电网信息安全风险评估,该算法首先利用粗糙集的思想,通过分辨函数求解最优属性对数据样本进行约简,再利用小生境模型提高约简样本个体的多样性以加快GEP算法运算的收敛速度,进而通过遗传算法实现全局搜索并得到安全风险等级评估结果。仿真实验表明,与传统的安全风险评估算法相比,提出的改进GEP算法具有较高的属性约简率和全局收敛率,可以快速实现海量监测数据下的电网信息安全风险评估。

A Risk Assessment Model Based on Niche Genetic Algorithm for Power System Information Security

Information security assurance is essential for the safe and stable operation of power cyber-physical systems. The key is to conduct all-round real-time monitoring of power cyber-physical systems and analyze the collected massive monitoring data to make accurate security risk assessments. As an evolutionary algorithm for pattern classification, the gene expression programming (GEP) has received widespread attention due to its ability to perform global search, but its operation on high-dimensional data sets is extremely time-consuming. This paper proposes an enhanced GEP algorithm for power grid information security risk assessment. The algorithm first uses the idea of rough set, solves the optimal attribute through the discrimination function to reduce the data sample, and then uses the niche genetic algorithm improves the diversity of the reduced sample individuals to accelerate the convergence speed of the GEP algorithm, and then realizes the global search through the genetic algorithm and obtains the level assessment of the security risk. Simulation results show that, compared with traditional security risk assessment algorithms, the enhanced GEP algorithm proposed in this paper has a higher attribute reduction rate and global convergence rate, and can quickly implement risk assessment from massive monitoring data of grid security information.