Deon Du Plessis

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.

- 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.

The concept of Wireless Sensor Networks (WSNs) has garnered significant global attention due to their wide range of applications. According to the IEEE 802.11 standard, WSNs are wireless networks consisting of sensor nodes (SNs) that interconnect via wireless communication links. These SNs are capable of sensing, processing, and wirelessly transmitting data, even in challenging environments. WSNs are primarily utilized for communication in various domains, including smart cities, healthcare, residential areas, and military applications. However, the deployment of WSNs in environments such as smart cities come with some challenges, particularly traffic congestion. Traffic congestion in smart cities, particularly during peak hours, is often caused by a high volume of vehicles traveling at the same time, resulting in delays, accidents, and inefficiencies under various weather conditions, including sunny and rainy days. Therefore, this paper proposes a novel algorithm called the Fusion-based Intelligent Congestion Management (FICM) algorithm, developed through the integration of the Navigation Reference Spatial Data (NRSD) algorithm and Fusion-based Multimodal Abnormal Detection (FMAD) algorithm. The objective of FICM is to mitigate on-road traffic congestion within smart cities effectively. The algorithm’s performance was evaluated using Network Simulator 3 (NS-3) by comparing its effectiveness with the NRSD and FMAD algorithms. Under sunny weather conditions, the NS-3 simulation results revealed that the FICM algorithm achieved an average False Alarm Rate (FAR) of 0.83%, a Mean Time to Detection (MTTD) of 76.0%, and a Detection Rate (DR) of 84.3%, outperforming both the NRSD and FMAD algorithms. Similarly, under rainy weather conditions, the FICM algorithm demonstrated an average FAR of 14.09%, an MTTD of 57.03%, and a DR of 78.04%, surpassing the performance of the NRSD and FMAD algorithms within the smart city environment.