An effective networks intrusion detection approach based on hybrid Harris Hawks and multi-layer perceptron Alazab, Moutaz Khurma, Ruba Abu Castillo Valdivieso, Pedro Ángel Abu-Salih, Bilal Martín, Alejandro Camacho, David Multi-layer perceptron (MLP) Harris Hawks optimization (HHO) Intrusion detection system (IDS) 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. HHOMLP 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), XGBoost. 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%. 2024-04-10T08:36:10Z 2024-04-10T08:36:10Z 2024 info:eu-repo/semantics/article Egyptian Informatics Journal 25 (2024) 100423 [10.1016/j.eij.2023.100423] https://hdl.handle.net/10481/90582 10.1016/j.eij.2023.100423 eng http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional Elsevier