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Browsing Book Chapters by Author "Abu-Mahfouz, Adnan MI"
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Item Integrated framework for enhancing SDN security and reliability(2023-11) Isong, B; Ratanang, T; Gasela, N; Abu-Mahfouz, Adnan MIThis paper addresses the issues of fault tolerance (FT) and intrusion detection (ID) in the Software-defined networking (SDN) environment. We design an integrated model that combines the FT-Manager as an FT mechanism and an ID-Manager, as an ID technique to collaboratively detect and mitigate threats in the SDN. The ID-Manager employs a machine learning (ML) technique to identify anomalous traffic accurately and effectively. Both techniques in the integrated model leverage the controllerswitches communication for real-time network statistics collection. While the full implementation of the framework is yet to be realized, experimental evaluations have been conducted to identify the most suitable ML algorithm for ID-Manager to classify network traffic using a benchmarking dataset and various performance metrics. The principal component analysis method was utilized for feature engineering optimization, and the results indicate that the Random Forest (RF) classifier outperforms other algorithms with 99.9% accuracy, precision, and recall. Based on these findings, the paper recommended RF as the ideal choice for ID design in the integrated model. We also stress the significance and potential benefits of the integrated model to sustain SDN network security and dependability.Item Machine learning – Imaging applications in transport systems: A review(2023-11) Adams, A; Abu-Mahfouz, Adnan MI; Hancke, GPTransport systems are fundamental to supporting economic growth, and the need for smarter, safer, more efficient and climate neutral transport systems continues to grow. Maintenance and operation of transport infrastructure is expensive and has many difficulties. In recent years, the application of machine learning to solve problems has been pursued with varying success rates. Many open problems still remain at this stage. This paper provides a review of deep learning applications in transport systems. Multiple deep learning applications are discussed e.g. railway safety, self-driving cars, pedestrian crossing and traffic light detection. Reviewed papers are evaluated in terms of challenges, contribution, weakness, research gaps. Key research questions for future study are proposed: performance optimization, data set improvement and the need for research on real-time performance on edge devices.Item Pressure management strategies for water loss reduction in large-scale water piping networks: A review(Springer, 2018-02) Adedeji, KB; Hamam, Y; Abe, BT; Abu-Mahfouz, Adnan MIIn water distribution networks (WDNs), water loss through leaking pipes is inevitable, as it constitutes a major threat to the operational services of water utilities. While water utilities are keen to providing an adequate supply of water to its end users, the undermined service quality, wasted energy resources and financial loss caused by leakages are major concerns. The financial loss, among others, associated with leaky pipes is increasingly growing at an alarming rate in recent years. Therefore, monitoring pipelines health through leakage control is crucial. Nevertheless, several methods for controlling leakages in WDNs have proposed. Research efforts conducted in the past acknowledged water pressure control as an effective method for reducing losses in water piping networks. Although, adequate pressure is required in the system to meet customer’s demands, it is a general agreement that reducing pressure will reduce the leakage flow rate as well as the possibility of pipe burst or crack. Several pressure management strategies have been proposed for leakage reduction in water distribution systems. In this work, we present an overview of the pressure management approaches proposed for reducing leakages in water distribution networks. Some previous and recent research efforts are outlined. Furthermore, information about leakage control, which may be useful for water utilities and pipeline engineers are provided.Item A review of intrusion detection techniques in the SDN environment(2021-11) Sebopelo, R; Isong, B; Gasela, N; Abu-Mahfouz, Adnan MIDespite the advantages of Software-defined networking (SDN) over the traditional networks, SDN is facing several challenges such as security threats and attacks, dominated by a distributed denial of service (DDoS) attacks that target the controller. In recent years, the SDN has witnessed several research attentions leading to proposals and the development of countermeasures such as intrusion detection systems (IDS). IDS plays a critical role in detecting and preventing malicious activities on the networks. Several detection techniques have been exploited for the effectiveness of the IDS such as pattern matching, anomaly-based and specification-based. With the nature of SDN architecture, flow-based anomaly detection has been effective and commendable. Therefore, this paper conducted a review of some of the IDS schemes in the SDN environment. It was aimed to identify the solution offers, techniques, challenges and provide research directions. The findings show that IDS in the SDN is an active research area and several techniques exist and are dominated by machine learning (ML) which exploits the network traffic flow to detect abnormal behaviours. Intrusion detection on the SDN is still at large and more ML techniques needs to be explored, considering the critically of the SDN controller.