An effective networks intrusion detection approach based on hybrid Harris Hawks and multi-layer perceptron
Metadatos
Afficher la notice complèteAuteur
Alazab, Moutaz; Khurma, Ruba Abu; Castillo Valdivieso, Pedro Ángel; Abu-Salih, Bilal; Martín, Alejandro; Camacho, DavidEditorial
Elsevier
Materia
Multi-layer perceptron (MLP) Harris Hawks optimization (HHO) Intrusion detection system (IDS)
Date
2024Referencia bibliográfica
Egyptian Informatics Journal 25 (2024) 100423 [10.1016/j.eij.2023.100423]
Patrocinador
Ministerio Español de Ciencia e Innovación, under project number PID2020-115570GB-C22 MCIN/AEI/10.13039/501100011033; Cátedra de Empresa Tecnología para las Personas (UGR-Fujitsu)Résumé
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%.