Chiew Foong Kwong

Muhammad Umair Basharat, Jawad Hussain, Waqas Khalid et al.

The growing adoption of IoT and cloud computing, combined with rapid advancements in digital technologies, has considerably increased the cyber-attack surface, resulting in increasingly complex and persistent attacks. Traditional security methods, primarily based on perimeter defenses, are insufficient to meet these developing threats, especially within the context of a Zero Trust Security (ZTS) architecture. This study investigates the application of sophisticated artificial intelligence (AI) and machine learning (ML) techniques, including the use of the Synthetic Minority Oversampling Technique (SMOTE), to improve anomaly detection and threat intelligence systems. This study focuses on how Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT) classifiers might increase threat detection accuracy in IoT environments. The research endeavors to improve cybersecurity resilience by mitigating false positives and providing actionable intelligence through supervised learning algorithms. The KDD Cup 1999 dataset is used in the study to assess how well these models perform in simulating various network intrusions and regular traffic. The application of SMOTE significantly enhanced the performance of these models by addressing class imbalance, leading to improved detection accuracy. Furthermore, as supplementary methods for detecting malicious URLs and advanced persistent threats (APTs), edge-based machine learning and blockchain technology are investigated. This study addresses the shortcomings of conventional security systems and supports the growing demand for reliable threat detection in a world that is becoming more interconnected. It also advances the creation of more proactive and adaptable cybersecur

Qianyu Liu, Chiew Foong Kwong, Sun Wei et al.

5G heterogeneous networks (HetNets) can provide higher network coverage and system capacity to the user by deploying massive small base stations (BSs) within the 4G macro system. However, the large-scale deployment of small BSs significantly increases the complexity and workload of network maintenance and optimisation. The current handover (HO) triggering mechanism A3 event was designed only for mobility management in the macro system. Directly implementing A3 in 5G-HetNets may degrade the user mobility robustness. Motivated by the concept of self-organisation networks (SON), this study developed a self-optimised triggering mechanism to enable automated network maintenance and enhance user mobility robustness in 5G-HetNets. The proposed method integrates the advantages of subtractive clustering and Q-learning frameworks into the conventional fuzzy logic-based HO algorithm (FLHA). Subtractive clustering is first adopted to generate a membership function (MF) for the FLHA to enable FLHA with the self-configuration feature. Subsequently, Q-learning is utilised to learn the optimal HO policy from the environment as fuzzy rules that empower the FLHA with a self-optimisation function. The FLHA with SON functionality also overcomes the limitations of the conventional FLHA that must rely heavily on professional experience to design. The simulation results show that the proposed self-optimised FLHA can effectively generate MF and fuzzy rules for the FLHA. By comparing with conventional triggering mechanisms, the proposed approach can decrease the HO, ping-pong HO, and HO failure ratios by approximately 91%, 49%, and 97.5% while improving network throughput and latency by 8% and 35%, respectively.