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All Journal The Indonesian Journal of Computer Science
Mthulisi Velempini
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Detecting Distributed Denial of Service Attacks in Mobile Edge Computing using Modified Extreme Machine Learning Mapunya, Sekgoari; Mthulisi Velempini
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4538

Abstract

Mobile Edge Computing (MEC) is a promising technology which enables 5G and reduces latency. By bringing cloud computing capabilities closer to end users, MEC enables latency-sensitive applications to perform more efficiently. However, security attacks pose significant challenges to the objectives of 5G with Distributed Denial of Service (DDoS) attacks being a major threat. These attacks can overwhelm target systems with excessive data preventing access to and disrupting network services. Effective mitigation strategies are required to protect MEC technology. Given the high data volume generated by such attacks, this paper utilizes a modified Firefly Algorithm to select relevant features. These selected features are then used to train a proposed variant of Extreme Learning Machine (ELM), where weights are initialized using Neighbourhood-Based Differential Evolution. MATLAB simulations demonstrate that the proposed modified ELM outperforms traditional approaches, providing an effective solution to DDoS attacks in MEC.
A Robust Bayesian Dynamic Stackelberg Game Theory Detection Scheme for Man-in-the-Middle Attack in Mobile Edge Computing Networks Moila, Lerato; Mthulisi Velempini
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4723

Abstract

Mobile Edge Computing (MEC) networks are emerging technologies transforming how data is processed, stored, and delivered at the edge network, enhancing performance and reducing latency. However, the technology introduces significant cybersecurity challenges, specifically Man-in-the-Middle (MitM) attacks. These attacks compromise sensitive data and can disrupt normal services. This study proposes a robust detection scheme based on Bayesian Dynamic Stackelberg Game Theory to address these vulnerabilities. By incorporating Bayesian inference, the scheme considers uncertainties in the attacker’s behaviour and the network environment, enabling the defender to update its strategies dynamically based on observed actions. The simulation results show that the proposed scheme significantly improves the detection scheme for MitM attacks in MEC networks, outperforming other schemes considered in the study. The findings show that integrating Game Theory with Bayesian analysis provides a promising approach for developing adaptive and resilient cybersecurity strategies in the evolving landscape of edge computing.
Co-Authors Mapunya, Sekgoari Moila, Lerato