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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Moudni, Houda
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Customized dataset-based machine learning approach for black hole attack detection in mobile ad hoc networks Moudni, Houda; Er-rouidi, Mohamed; Lmkaiti, Mansour; Mouncif, Hicham
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2138-2149

Abstract

This article explores the application of machine learning (ML) algorithms to classify the black hole attack in mobile ad hoc networks (MANETs). Black hole attacks threaten MANETs by disrupting communication and data transmission. The primary goal of this study is to develop an intrusion detection system (IDS) to detect and classify this attack. The research process involves feature selection, the creation of a custom dataset tailored to the characteristics of black hole attacks, and the evaluation of four machine learning models: random forest (RF), logistic regression (LR), k-nearest neighbors (k-NN), and decision tree (DT). The evaluation of these models demonstrates promising results, with significant improvements in accuracy, precision, F1-score, and recall metrics. The findings underscore the potential of machine learning in enhancing the security of MANETs by providing an effective means of attack classification.
Comparative evaluation of machine learning models for intrusion detection in WSNs using the IDSAI dataset Lmkaiti, Mansour; Moudni, Houda; Mouncif, Hicham
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4913-4922

Abstract

This paper provides comparative assessment of three lightweight machine learning (ML) models (logistic regression (LR), random forest (RF), and gradient boosting (GB)), which are employed to detect intrusions in wireless sensor networks (WSNs) using the IDSAI dataset. The goal is to determine the most effective and deployable classifier within the constraints of WSN resources. In order to prevent data leakage and report accuracy, precision, recall, F1-score, and receiver operating characteristic-area under the curve (ROC-AUC) with mean±SD, we implement stratified 5-fold cross validation with in fold preprocessing. The results indicate that RF provides the most optimal generalization and overall performance (accuracy 0.9994 ± 0.0001, precision 0.9995±0.0001, recall 0.9994±0.0001, F1-score 0.9994±0.0001, ROC–AUC 0.9998 ± 0.0000). RF is closely followed by GB (accuracy 0.9990±0.0001, precision 0.9995±0.0001, recall 0.9985±0.0001, F1-score 0.9990 ± 0.0001, ROC-AUC ≈ 1.0000). LR demonstrates limitations in linearly overlapping classes, as evidenced by its high precision but reduced recall (accuracy 0.9167±0.0010, precision 0.9829±0.0002, recall 0.8481±0.0018, F1-score 0.9105 ± 0.0011, ROC–AUC 0.9707 ± 0.0001). In order to evaluate deployability, we characterize the inference throughput on a modest PC: LR ∼ 6.5 × 105 samples/s, GB ∼ 2.2 × 105 samples/s, and RF ∼ 1.3 × 105 samples/s, indicating a tiered intrusion detection system (IDS) (LR at sensors, RF at cluster-heads, and GB at the gateway). We also address the potential dangers of overfitting that may arise from the cleanliness of the dataset and provide a roadmap for future validation on a more diverse set of traffic. The research establishes a baseline for lightweight IDS in actual WSNs that is deployable and reproducible.
Co-Authors Er-Rouidi, Mohamed Lmkaiti, Mansour Mouncif, Hicham