This paper proposes an Intrusion Detection System (IDS) employing the Harris Hawks Optimization algorithm (HHO) to optimize Multilayer Perceptron learning by optimizing bias and weight parameters. HHO-MLP aims to select optimal parameters in its learning process to minimize intrusion detection errors in networks. HHO-MLP has been implemented using EvoloPy NN framework, an open-source Python tool specialized for training MLPs using evolutionary algorithms. For purposes of comparing the HHO model against other evolutionary methodologies currently available, specificity and sensitivity measures, accuracy measures, and mse and rmse measures have been calculated using KDD datasets. Experiments have demonstrated the HHO MLP method is effective at identifying malicious patterns. HHO-MLP has been tested against evolutionary algorithms like Butterfly Optimization Algorithm (BOA), Grasshopper Optimization Algorithms (GOA), and Black Widow Optimizations (BOW), with validation by Random Forest (RF), XG-Boost. HHO-MLP showed superior performance by attaining top scores with accuracy rate of 93.17%, sensitivity level of 89.25%, and specificity percentage of 95.41%.

翻译：本文提出一种基于哈里斯鹰优化算法（HHO）优化多层感知机（MLP）偏置与权重参数的入侵检测系统（IDS）。HHO-MLP旨在通过学习过程中选取最优参数，最小化网络入侵检测误差。该系统基于EvoloPy NN框架实现——该框架为专用于通过进化算法训练MLP的开源Python工具。为对比HHO模型与现有其它进化方法，采用KDD数据集计算了特异性与灵敏度指标、准确率指标以及MSE与RMSE指标。实验表明，HHO-MLP方法能有效识别恶意模式。将该方法与蝴蝶优化算法（BOA）、蚱蜢优化算法（GOA）及黑寡妇优化算法（BOW）等进化算法进行对比，并采用随机森林（RF）与XG-Boost进行验证，结果显示HHO-MLP性能优越：准确率达到93.17%，灵敏度达到89.25%，特异性达到95.41%。