Adaptive Differential Evolution Algorithm with Simulated Annealing for Security of IoT Ecosystems
Adaptive Differential Evolution Algorithm with Simulated Annealing for Security of IoT Ecosystems
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DOI：
10.1155/2022/6951849
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发表时间：
2022-04
影响因子：
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通讯作者：
Qianqian Liu;Xiaoyan Zhang;Qiaozhi Hua;Zheng Wen;Haipeng Li
中科院分区：
文献类型：
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作者：
Qianqian Liu;Xiaoyan Zhang;Qiaozhi Hua;Zheng Wen;Haipeng Li
随着物联网在现实世界中的广泛应用，安全对物联网发展的影响日益重要。近年来，人工智能(AI)、计算智能(CI)、深度学习方法等先进技术在不同的安全应用中得到了应用。针对物联网入侵检测系统中的特征选择问题，提出了一种基于模拟退火法的自适应差分进化算法。改进了自适应DE算法的变异、交叉和选择过程，避免陷入局部最优解。在突变过程中，根据双曲正切函数曲线改变突变因子。在交叉操作中引入线性函数，以控制交叉因子。在选择过程中，本文采用SA算法的Metropolis准则，接受劣解作为最优解。为了测试该算法的性能，对来自CEC2017的29个基准函数和6个典型基准函数进行了数值实验。实验结果表明，该算法优于其他四种算法。
With the wide application of the Internet of Things (IoT) in real world, the impact of the security on its development is becoming incrementally important. Recently, many advanced technologies, such as artificial intelligence (AI), computational intelligence (CI), and deep learning method, have been applied in different security applications. In intrusion detection system (IDS) of IoT, this paper developed an adaptive differential evolution based on simulated annealing algorithm (ASADE) to deal with the feature selection problems. The mutation, crossover, and selection processes of the self-adaptive DE algorithm are modified to avoid trapping in the local optimal solution. In the mutation process, the mutation factor is changed based on the hyperbolic tangent function curve. A linear function with generation is incorporated into the crossover operation to control the crossover factor. In the selection process, this paper adopts the Metropolis criterion of the SA algorithm to accept poor solution as optimal solution. To test the performance of the proposed algorithm, numerical experiments were performed on 29 benchmark functions from the CEC2017 and six typical benchmark functions. The experimental results indicate that the proposed algorithm is superior to the other four algorithms.