Customized Dataset-Based Machine Learning Approach for Black Hole Attack Detection in Mobile Ad Hoc Networks

Aug 3, 2026

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The study applies machine learning algorithms to classify black hole attacks in mobile ad hoc networks (MANETs), where malicious nodes falsify shortest-path advertisements to intercept and drop packets. A custom dataset is built through feature selection to reflect the characteristics of black hole behavior, then four models are evaluated: random forest, logistic regression, k-nearest neighbors, and decision tree. Results show meaningful gains across accuracy, precision, F1-score, and recall, supporting machine learning as an effective intrusion detection system for strengthening MANET security.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Introduction

Mobile ad hoc networks and security challenges

Black hole attacks and limitations of traditional defenses

Motivation for machine learning-based detection

Literature Review

Related works and prior classifier comparisons

Proposed Approach

Feature selection and custom dataset design

Machine learning models for attack classification

Results and Discussion

Performance metrics analysis

Conclusion and Future Work

Customized dataset-based machine learning approach for black hole attack detection in mobile ad hoc networks Houda Moudni1, Mohamed Er-Rouidi2, Mansour Lmkaiti3, Hicham Mouncif3 1TIAD Laboratory, Faculty of Science and Technology, Sultan Moulay Slimane University, Beni Mellal, Morocco 2Modeling and combinatorial laboratory, Department of Mathematics and Computer Science, Polydisciplinary Faculty, Cadi Ayyad University, Safi, Morocco 3LIMATI Laboratory, Polydisciplinary Faculty, Sultan Moulay Slimane University, Beni Mellal, Morocco Article history: Received Jul 30, 2024 Revised Sep 20, 2024 Accepted Oct 1, 2024 Keywords: Black hole attack Intrusion detection system Machine learning Mobile ad hoc networks Security Corresponding Author: 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 inaccuracy, 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. This is an open access article under the CC BY-SA license. Houda Moudni TIAD Laboratory, Faculty of Science and Technology, Sultan Moulay Slimane University Av Med V, BP 591, Beni-Mellal 23000, Maroko Email: [h.moudni@usms.ma](h.moudni@usms.ma) Article Info ABSTRACT 1. INTRODUCTION Mobile ad hoc networks (MANETs) [1] are self-configuring networks composed of mobile nodes that form a dynamic network infrastructure without relying on a centralized authority. MANETs have gained significant attention due to their flexibility in providing communication in various environments, making them ideal for emergency and military applications. However, the decentralized nature and dynamic topology of MANETs make them particularly vulnerable to a wide range of security threats, including routing attacks, denial of service, and black hole attacks [2]–[4] . Black hole attacks are particularly severe as they involve a malicious node falsely advertising the shortest path to the destination, subsequently intercepting and dropping all data packets passing through it. This not only disrupts network communication but also compromises data integrity and confidentiality. Traditional security mechanisms, such as encryption and authentication protocols, while effective in some scenarios, often fall short in addressing the sophisticated and adaptive nature of such attacks in MANETs. Existing solutions, including heuristic-based and rule-based intelligent intrusion detection systems (IDSs) , are often limited by their inability to adapt to new and evolving attack patterns. Given these constraints, there is an urgent need for intelligent systems that can dynamically detect and classify such attacks, enabling timely and effective countermeasures. Recent advances in machine learning (ML) [5]–[7] offer a promising approach to this challenge. ML algorithms can analyze large volumes of network data, identify patterns associated with attacks, and distinguish them from normal network behavior. By learning from past incidents, these algorithms can provide robust, adaptive defenses against even novel attack strategies. The primary goal of this research is to design and implement an IDS that leverages machine learning algorithms to detect and classify black hole attacks in MANET

FAQs

What problem does the document address?

It addresses detecting and classifying black hole attacks in MANETs using machine learning-based intrusion detection.

How is the custom dataset created?

The approach selects the most relevant features that capture black hole attack characteristics, then uses them to form a custom dataset tailored to the attack behavior.

Which machine learning models are evaluated and what is the outcome?

Random forest, logistic regression, k-nearest neighbors, and decision tree are evaluated; the study reports improved performance across accuracy, precision, F1-score, and recall.

Aug 3, 2026

Upload by Miles