Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal The Indonesian Journal of Computer Science
Plessis, Deon Du
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
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.
Enhanced Detection of IoT-Based DoS Attacks Using A Hybrid ANN-RF Classification Model Ndaba, Solomon Bulelani; Mathonsi, Topside E.; Plessis, Deon Du
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.v14i4.4965

Abstract

Denial of Service (DoS) attacks pose a significant threat to the integrity and availability of Internet of Things (IoT) networks, where interconnected devices are increasingly targeted due to their vulnerabilities. These attacks overwhelm systems with excessive traffic, disrupting legitimate services and potentially compromising sensitive data. Traditional detection methods often rely on predefined signatures, which struggle to keep pace with the evolving tactics employed by attackers. This study introduces a novel hybrid detection algorithm that integrates Artificial Neural Networks (ANN) and Random Forest (RF) classifiers, termed ANN-RF, to enhance the detection of DoS attacks in IoT environments. The ANN-RF model was evaluated based on critical performance metrics, including detection accuracy, False Positive Rate (FPR), and latency. Experimental results obtained through MATLAB demonstrate that the ANN-RF model achieves a detection accuracy of 93% and a low FPR of 5% when detecting 30 attacks, significantly outperforming standalone ANN and RF models, which recorded accuracies of 82% and 87%, and FPRs of 15% and 10%, respectively. Additionally, the ANN-RF model consistently maintains high detection accuracy, reducing false alarms and enhancing reliability as the number of attacks increases. Thus, the proposed ANN-RF model has strong potential to enhance real-time security in IoT networks by offering a scalable, accurate, and adaptive solution for DoS attack detection, with practical applications across domains such as smart homes, healthcare, and industrial control systems.
Co-Authors Baloyi, Coster Mathonsi, Topside E. Ndaba, Solomon Bulelani Tshilongamulenzhe, Tshimangadzo