BOC-PDO:使用二元对立蜂窝草原犬优化算法的入侵检测模型

Bilal H. Abed-alguni, Basil M. Alzboun, Noor Aldeen Alawad
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引用次数: 0

摘要

入侵检测数据集很可能包含大量冗余、不相关和嘈杂的特征,这些特征会降低应用于这些数据集的机器学习技术和分类器的性能。特征选择方法用于减少入侵检测数据集中的特征数量,并剔除那些不重要的特征。最强大的结构化群体方法之一是蜂窝自动机方法,该方法用于增强基于群体的优化算法的多样性和收敛性。在这项工作中,蜂窝自动机方法、基于混合对立的学习和 K-近邻分类器与草原犬优化算法(PDO)相结合,形成了一种新的入侵检测框架,称为二元对立蜂窝草原犬优化算法(BOC-PDO)。建议的框架包含四个主要特征。首先,利用蜂窝自动机模型来增加 PDO 中可行解的数量。第二,使用四个 S 型和四个 V 型二进制转移函数将 BOC-PDO 中的连续解转换为二进制解。第三,在 BOC-PDO 优化循环的末端使用基于混合对立的学习方法,以提高探索能力。第四,将 K-近邻分类器作为 BOC-PDO 的主要学习模型。在评估 BOC-PDO 与 8 种流行的二进制优化算法和 4 种机器学习方法的有效性时,采用了 11 个知名的入侵检测数据集。根据整体仿真结果,BOC-PDO 在 11 个入侵检测数据集中的准确率最高、目标值最佳、所选特征最少。此外,与其他测试算法相比,BOC-PDO 的模拟结果通过 Friedman 和 Wilcoxon 统计检验确定了可靠性和一致性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

BOC-PDO: an intrusion detection model using binary opposition cellular prairie dog optimization algorithm

BOC-PDO: an intrusion detection model using binary opposition cellular prairie dog optimization algorithm

Intrusion detection datasets are highly likely to contain numerous redundant, irrelevant, and noisy features that slow the performance of the machine learning techniques and classifiers that may be applied to them. The feature selection approach is used for reducing the number of features in intrusion detection datasets and eliminating those that are not important. One of the most powerful structured population approaches is the Cellular Automata approach, which is used to enhance the diversity and convergence of population-based optimization algorithms. In this work, the Cellular Automata approach, Mixed opposition-based learning, and the K-Nearest Neighbor classifier are incorporated with the Prairie dog optimization algorithm (PDO) in a new intrusion detection framework called Binary Opposition Cellular Prairie dog optimization algorithm (BOC-PDO). The proposed framework contains four key features. First, the Cellular Automata model is utilized to enhance the population of feasible solutions in the PDO. Second, four S-shaped and four V-shaped Binary Transfer Functions are used to convert the continuous solutions in BOC-PDO to binary ones. Third, the Mixed opposition-based learning approach is used at the end of the optimization loop of BOC-PDO to improve capacity for exploration. Fourth, the K-Nearest Neighbor classifier is used as the main learning model in BOC-PDO. Eleven famous intrusion detection datasets were employed in the evaluation of the effectiveness of BOC-PDO compared to eight popular binary optimization algorithms and four machine learning approaches. According to the overall simulation results, BOC-PDO scored the highest accuracy, best objective value, and fewest selected features for each of the eleven intrusion detection datasets. Besides, the reliability and consistency of the simulation results of BOC-PDO compared to the other tested algorithms were established using Friedman and Wilcoxon statistical tests.

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