Software Defined Networks (SDN) offer dynamic reconfigurability and scalability, revolutionizing traditional networking. However, countering Distributed Denial of Service (DDoS) attacks remains a formidable challenge for both traditional and SDN-based networks. The integration of Machine Learning (ML) into SDN holds promise for addressing these threats. While recent research demonstrates ML’s accuracy in distinguishing legitimate from malicious traffic, it faces difficulties in handling emerging, low-rate, and zero-day DDoS attacks due to limited feature scope for training. The ever-evolving DDoS landscape, driven by new protocols, necessitates continuous ML model retraining. In response to these challenges, we propose an ensemble online machine-learning model designed to enhance DDoS detection and mitigation. This approach utilizes online learning to adapt the model with expected attack patterns. The model is trained and evaluated using SDN simulation (Mininet and Ryu). Its dynamic feature selection capability overcomes conventional limitations, resulting in improved accuracy across diverse DDoS attack types. Experimental results demonstrate a remarkable 99.2% detection rate, outperforming comparable models on our custom dataset as well as various benchmark datasets, including CICDDoS2019, InSDN, and slow-read-DDoS. Moreover, the proposed model undergoes comparison with industry-standard commercial solutions. This work establishes a strong foundation for proactive DDoS threat identification and mitigation in SDN environments, reinforcing network security against evolving cyber risks.

The novelty of Federated Learning (FL) has emerged as a promising alternative to centralized machine learning systems in the context of Anomaly-Based Intrusion Detection Systems (AIDS) deployed on Internet of Things (IoT) devices. Unlike traditional centralized models, FL allows on-device model training and updates, reducing privacy concerns and issues like single points of failure and high false alarm rates (FAR). This approach, termed ’Fed-AIDS,’ offers a more secure and efficient solution. However, the development of Fed-AIDS models faces challenges related to limited training data and the diverse nature of IoT datasets. Additionally, FL’s decentralized nature introduces weight divergence issues arising from non-Independently and Identically Distributed (non-IID) clients. To address these challenges and optimize Fed-AIDS modeling, interdisciplinary research efforts are vital. The primary objective of this study is to conduct an up-to-date review by adopting a Systematic Literature Review (SLR) approach to analyze existing studies of Fed-AIDS modeling procedures for IoT devices. Data from the published studies were retrieved from Scopus database, which covered major publishers like IEEE, Elsevier and others. Specifically, our review conducted from the following Fed-AIDS perspectives: workflow and tools, training dataset, complexities of non-IID data in Fed-AIDS models, classification tasks, aggregation tasks, and model validation metrics. Based on the research findings, the study highlights a series of challenges and proposes potential solutions to stand in future research in Fed-AIDS modeling, aiming to advance the field of IoT device security.

Deploying small macrocell base stations, hence called femtocells, enhances the level of service provided to consumers inside as well as outside. However, the effective management of user mobility poses a significant challenge in their implementation. This paper focuses extensively on this challenge, specifically emphasizing a critical aspect of mobility management: handovers. Regarding macrocell femtocell 5G A-LTE two-tier networks, the decision-making process during handovers holds paramount importance. This research classifies and thoroughly examines decision algorithms concerning handovers, considering factors such as the speed of user equipment, financial considerations, interference, and received signal strength. Nevertheless, a significant number of these described decision algorithms overlook crucial aspects. Firstly, they often fail to account for cell selection when employing a scenario with just one macrocell and numerous femtocells; a hybrid access policy is used. Secondly, a significant number of those methods do not take the retention parameter for the user equipment into account, resulting in an increased occurrence of unnecessary handovers. To address these shortcomings, we propose a sophisticated handover decision algorithm. This novel approach considers various factors, including the individual's speed, received signal strength, time spent there, and the femtocell base station's access policy. Comparing our suggested algorithm to traditional decision algorithms that are only based on simulation, data shows that our proposed approach significantly lowers the frequency of unwanted handovers on received signal strength in the 5G network to improve the Energy Efficiency of the network by over 85%.

In the era of 5G, seamless mobility handovers are critical in densely populated regions like Malaysia to mitigate disruptions and inefficiencies. 5G networks offer unprecedented data speeds and reliability, essential for advancing mobile communication and Internet of Things applications. However, ensuring continuous connectivity and service during mobility remains challenging, especially in urban settings. Digital twin technology presents a promising solution to enhance 5G handover mechanisms. A digital twin network (DTN) mirroring Malaysia's 5G infrastructure is proposed, utilising real‐time data and user behaviour insights to optimise energy consumption during handovers. The focus is on energy‐efficient protocols and algorithms, reviewed through a systematic literature review. The DTN aims to enhance mobility handover efficiency through predictive handovers and adaptive resource allocation, bolstered by sustainable practices such as edge computing. The potential of DTNs to optimise 5G handover processes is explored, starting with the foundational concepts of 5G mobility and digital twins, highlighting the need for improved strategies in high‐mobility scenarios. Methodologies leveraging digital twins to predict network conditions, simulate handover scenarios, and proactively manage decisions are examined, reducing latency and packet loss. Case studies demonstrate how digital twins adapt dynamically to network changes and user mobility, thereby improving quality of service and user experience. Malaysia's specific 5G mobility challenges are addressed with a tailored DTN emphasising energy efficiency, evaluated through practical applications. Evaluation criteria assess effectiveness with in‐depth analysis of methods, performance metrics, limitations, and recommendations for future research. Challenges and future directions including scalability, security, and real‐time data processing, are discussed, aiming to integrate digital twin technology with 5G networks for enhanced connectivity. This abstract provides a roadmap for leveraging digital twins to optimise 5G network performance sustainably, guiding future research and implementation strategies.

In the pursuit of software quality, testing plays a pivotal role in identifying and rectifying errors and defects before software is delivered to users. However, the increasing complexity of modern software, driven by diverse user needs, has resulted in numerous input functions and options. While exhaustive testing remains ideal, practical limitations in terms of time and cost impede exhaustive efforts. To address these challenges, researchers have turned to metaheuristic algorithms to formulate effective t-way testing strategies, wherein ’t’ denotes parameter interaction strength. These strategies encompass a range of exploitation and exploration techniques for generating test data. Despite these advancements, challenges persist in test case generation, such as enhancing software quality through parameter seeding and managing combinatorial explosion by understanding parameter constraints among system elements. Though existing metaheuristic-based t-way strategies offer valuable insights, none reign supreme over their counterparts. To bridge this gap, this article introduces a pioneering approach called SCHOP, integrating seeding and constraint supports within a harmony search algorithm by adopting a one-parameter-at-a-time approach. Benchmarking results illustrate the competitive performance of SCHOP across well-known benchmarking configurations against other strategies. SCHOP’s benchmarking results showed success in 61.07% of cases, with 80 out of 131 entries performing well. However, there were 51 instances of suboptimal test suite sizes (38.93%). Further analysis revealed statistical significance in 12 out of 61 cases, with 48 out of 61 cases showing no significant difference, all at a 95% confidence level (α=0.05). Finally, SCHOP concurrently integrates seeding and constraint support within the harmony search algorithm to boost software quality while reducing the test suite’s size.