An effective networks intrusion detection approach based on hybrid Harris Hawks and multi-layer perceptron

被引:11
作者
Alazab, Moutaz [1 ]
Abu Khurma, Ruba [2 ]
Castillo, Pedro A. [3 ]
Abu-Salih, Bilal [4 ]
Martin, Alejandro [5 ]
Camacho, David [5 ]
机构
[1] Al Balqa Appl Univ, Fac Artificial Intelligence, Amman, Jordan
[2] Middle East Univ, Fac Informat Technol, MEU Res Unit, Amman, Jordan
[3] Univ Granada, Dept Comp Engn Automat & Robot, Granada, Spain
[4] Univ Jordan, King Abdullah II School Informat Technol, Amman, Jordan
[5] Univ Politecn Madrid, Dept Comp Syst Engn, Madrid, Spain
关键词
Multi-layer perceptron (MLP); Harris Hawks optimization (HHO); Intrusion detection system (IDS); DETECTION SYSTEM; FEATURE-SELECTION; MACHINE; MALWARE;
D O I
10.1016/j.eij.2023.100423
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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), 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%.
引用
收藏
页数:9
相关论文
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