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All Journal The Indonesian Journal of Computer Science
Baloyi, Coster
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Machine Learning-Based Security Algorithms for Detecting and Preventing DDoS Attacks on the IoT: State-of-the-Art, Challenges, and Future Directions Baloyi, Coster; Mathonsi, Topside; Du Plessis, Deon; Muchenje, Tonderai; Tshilongamulenzhe, Tshimangadzo
The Indonesian Journal of Computer Science Vol. 14 No. 3 (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.v14i3.4853

Abstract

Abstract - The Internet of Things (IoT) represents a vast network of interconnected devices equipped with software, sensors, and other technologies that enable data exchange and autonomous operation with other devices and systems without human intervention over the internet. IoT applications span across various sectors, including agriculture, education, healthcare, and communication. However, Distributed Denial of Service (DDoS) attacks continue to pose significant risks to the IoT network due to current challenges of classification efficiency and response times by the existing algorithms, such as Decision Tree (DT), Linear Regression (LR), and K-means. This paper provides a comprehensive review of DDoS attack types within the IoT networks. Secondly, the paper critically examines and analyses the challenges and opportunities inherent in leveraging Machine Learning (ML) algorithms for detecting, preventing, and mitigating these attacks. Finally, it presents the categories of IoT performance metrics, and their statistics found in the Literature over the Past decade.
Enhanced Security Algorithm for Detecting Distributed Denial of Services Attacks in Cloud Computing Baloyi, Coster; Mathonsi, Topside E.; Plessis, Deon Du; Tshilongamulenzhe, Tshimangadzo
The Indonesian Journal of Computer Science Vol. 14 No. 4 (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.v14i4.4888

Abstract

Cloud Computing has the benefit of offering on-demand scalable services to its customers without having to invest much on hardware infrastructure, resources and software. Most private and public sectors are moving to the Cloud. As a result, Cloud Computing has become an ideal option due to its flexibility, scalability and cost efficiency. The existence of vulnerabilities in the network systems hosting Cloud have raised an opportunity for attackers to launch attacks in Cloud Computing. The intruders attack business applications such as webservers, financial servers, and other servers exploiting Distributed Denial of Service (DDoS) attacks. This paper proposed a Real-Time Network Traffic Attack Detection (RTNTAD) algorithm to detect DDoS attacks using real-time dataset to mitigate DDoS attacks. MATLAB was employed to evaluate the performance of RTNTAD. The proposed RTNTAD algorithm has achieved 99.2% detection rate, 99.5% classification of DDoS attacks, 0.9% connectivity time out and less than 18% false positive.
Co-Authors Du Plessis, Deon Mathonsi, Topside Mathonsi, Topside E. Muchenje, Tonderai Plessis, Deon Du Tshilongamulenzhe, Tshimangadzo