Browsing by Author "Jiggins, Julia"

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    5G aviation networks using novel AI approach for DDoS detection
    (IEEE, 2023-07-17) Whitworth, Huw; Al-Rubaye, Saba; Tsourdos, Antonios; Jiggins, Julia
    The advent of Fifth Generation (5G) technology has ushered in a new era of advancements in the aviation sector. However, the introduction of smart infrastructure has significantly altered the threat landscape at airports, leading to an increased vulnerability due to the proliferation of endpoints. Consequently, there is an urgent requirement for an automated detection system capable of promptly identifying and thwarting network intrusions. This research paper proposes a deep learning methodology that merges a Convolutional Neural Network (CNN) with a Gated Recurrent Unit (GRU) to effectively detect various types of cyber threats using tabular-based image data. To transform time series features into 2D texture images, Gramian Angular Fields (GAFs) are utilized. These images are then stacked to form an N-channel image, which is fed into the CNN-GRU architecture for sequence analysis and identification of potential threats. The provide solution GAF-CNN-GRU achieved an accuracy of 98.6% on the Cranfield Embedded Systems Attack Dataset. We further achieved Precision, Recall and F1-scores of 97.84%, 91% and 94.3%. To evaluate model robustness we further tested this approach, using a benchmark random selection of input features, on the Canadian Institute for Cyber-Security (CIC) 2019 Distributed Denial-of-service attack (DDoS) Dataset achieving an Accuracy of 89.08%. Following feature optimisation our approach was able to achieve an accuracy of 98.36% with Precision, Recall and F1 scores of 93.09%, 95.45% and 94.56% respectively.
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    Aircraft to operations communication analysis and architecture for the future aviation environment
    (IEEE, 2021-11-15) Whitworth, Huw; Al-Rubaye, Saba; Tsourdos, Antonios; Jiggins, Julia; Silverthorn, Nigel; Thomas, Karim
    Fifth Generation (5G) systems are envisaged to support a wide range of applications scenarios with varying requirements. 5G architecture includes network slicing abilities which facilitate the partitioning of a single network infrastructure on to multiple logical networks, each tailored to a given use case, providing appropriate isolation and Quality of Service (QoS) characteristics. Radio Access Network (RAN) slicing is key to ensuring appropriate QoS over multiple domains; achieved via the configuration of multiple RAN behaviours over a common pool of radio resources. This Paper proposes a novel solution for efficient resource allocation and assignment among a variety of heterogeneous services, to utilize the resources while ensuring maximum QoS for network services. First, this paper evaluates the effectiveness of different wireless data bearers. Secondly, the paper proposes a novel dynamic resource allocation algorithm for RAN slicing within 5G New Radio (NR) networks utilising cooperative game theory combined with priority-based bargaining. The impact of this work to industry is to provide a new technique for resource allocation that utilizes cooperative bargaining to ensure all network services achieve minimum QoS requirements – while using application priority to reduce data transfer time for key services to facilitate decreased turnaround time at the gate.