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All Journal The Indonesian Journal of Computer Science
Mathonsi, Topside
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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.
A Model to Amplify Transmission Quality of Satellite Television Lebogang Maja; Deon du Plessis; Mathonsi, Topside; 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.v14i3.4861

Abstract

One of the various applications of communication satellite technologies is broadcasting satellite television (TV). In TV broadcasting, satellite communication is the easiest way to transmit many services and offers a variety of choices across a varied region, thereby overcoming the need for the complex infrastructure of terrestrial transmitters that a terrestrial network needs to broadcast its signals throughout a wide range area like countries or continents and providing quality digital TV viewing. However, Satellite TV broadcasting has a deficiency of outage effect caused by rain fade that instigate due to bad raining weather which at once will cuts signal transmission from the transmitter satellite to the receiver dish. this study was undertaken to explore the challenges that satellite TV broadcasting faces, which is caused by the rain fade effect. Thereafter, a model to amplify the transmission quality of satellite television is designed. The proposed Gau-satcomm algorithm, ITU-R model, and SAM model had an average BER of 5%, 8%, and 10%, respectively. Additionally, the Gau-Satcomm algorithm, SAM model, and ITU-R model experienced 4%, 9%, and 11% attenuation, respectively.  Furthermore, the study compared outage probability across three algorithms at frequencies over 10 GHz, the proposed Gau-satcomm algorithm, the ITU-R algorithm, and the SAM algorithm minimized outages by 10%, 7%, and 5%, respectively. Therefore, the proposed Gau-Satcomm outperforms these traditional algorithms in regard to average BER, a reduced average attenuation, and outage probability.
A Review of Vulnerability Detection Algorithms in Software Code Zelda P. Ramahlo; Mathonsi, Topside; Tshimangadzo M. Tshilongamulenzhe
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.v14i4.4972

Abstract

Detecting software vulnerabilities is essential to keeping modern systems safe in the face of increasingly sophisticated cyber threats. This paper offers a clear and accessible overview of how vulnerabilities are currently identified, reviewing traditional, machine learning (ML), and hybrid approaches. Traditional techniques such as static and dynamic analysis are still widely used but often suffer from high false positive rates and struggle to adapt to new and evolving threats. In contrast, recent ML developments, especially those involving Random Forest (RF) and Convolutional Neural Networks (CNN), have shown significant promise in improving detection accuracy, feature extraction, and classification. Decision Tree methods remain valued for their transparency, while CNNs and other deep learning tools excel at recognizing structural and spatial patterns in code. Combining these strengths in hybrid models integrating effective feature selection with powerful pattern recognition has the potential to deliver more accurate results and reduce false alarms. However, persistent challenges remain, including limited dataset diversity, weak resilience against adversarial attacks, and the need for real-time adaptability. By bringing together the latest research and practical insights, this review aims to guide developers, security analysts, and organizations in creating more robust, automated, and adaptive security tools capable of meeting the fast-changing demands of software vulnerability management.
Co-Authors Baloyi, Coster Deon du Plessis Du Plessis, Deon Lebogang Maja Muchenje, Tonderai Tshilongamulenzhe, Tshimangadzo Tshimangadzo M. Tshilongamulenzhe Zelda P. Ramahlo