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%.

利用哈里斯鹰优化算法（HHO）来优化多层感知机学习的入侵检测系统（IDS），通过优化偏差和权重参数选择最优参数以减少网络入侵检测错误，实验证明 HHO-MLP 方法能有效识别恶意模式并在准确率、敏感度和特异度方面表现优异。