Leveraging Artificial Intelligence for Blackhole Attack Detection in MANETs: A Comparative Study
Journal Title: Information Dynamics and Applications - Year 2024, Vol 3, Issue 4
Abstract
Blackhole attacks represent a significant threat to the security of communication networks, particularly in emerging network architectures such as Mobile Ad Hoc Networks (MANETs). These attacks, characterized by their ability to obscure malicious behavior, evade conventional detection methods due to their loosely defined signatures and their ability to bypass traditional filtering mechanisms. This study investigates the application of machine learning techniques, specifically Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Decision Tree (DT), for the detection and mitigation of blackhole attacks in MANETs. Simulations conducted in MATLAB 2023a examined network configurations with node densities of 50, 100, 250, and 500 nodes to assess the performance of these classifiers in comparison to conventional detection approaches. The results demonstrated that both SVM and CNN achieved near-perfect detection accuracy of 100% across all network configurations, outperforming traditional methods. SVM was chosen due to its efficacy in handling high-dimensional data, CNN for its ability to learn complex, nonlinear hierarchical features, and DT for its interpretability. The findings underscore the potential of these machine learning models in enhancing the precision of blackhole attack detection, thereby improving network security. Future research is recommended to explore the scalability and training efficiency of these models, particularly through the integration of advanced techniques such as model fusion and deep learning architectures. This study contributes to the growing body of literature on radar wave radio (RWR)-based and machine learning-based attack detection and highlights the potential of artificial intelligence (AI) solutions in transforming traditional emitter identification methods, offering significant improvements to network protection systems.
Authors and Affiliations
Zainab Bashar Ibrahim, Mayada Faris Ghanim
Keywords
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- EP ID EP761607
- DOI https://doi.org/10.56578/ida030404
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