BSpace

The British University in Dubai (BUiD) Digital Repository

Welcome to BSpace, the online institutional repository of the British University in Dubai. BSpace provides access to the Dissertations, Thesis, Research projects, Faculty publications and archives of BUiD.

Submit your dissertation/thesis by completing the registration using your BUiD email.

Review the submission guidelines before you submit the final version

Communities in DSpace

Select a community to browse its collections.

Now showing 1 - 7 of 7

Recent Submissions

  • Thumbnail Image
    Item type:Item,
    A Novel Versatile Framework for Enabling Early Detection of Evolving Network-based Cyberattacks
    (The British University in Dubai (BUiD), 2025-02) THOMAS, RAJESH; Dr Yerima, Suleiman
    Network-based cyber-attacks have been increasing in scale, frequency and sophistication, posing significant threats to nation states and organizations worldwide. Researchers have proposed various anomaly-based solutions to detect such attacks and address the shortcomings of traditional signature-based methods. However, these solutions either require complex preprocessing to extract network flow statistics or depend on hand-crafted features from domain expertise, thus adding computational overhead that limits the ability for early attack detection. To address these limitations, this thesis proposes a novel framework called FPAC (Flexible Parser Anonymizer Converter) which is designed to enable early detection of different types of attacks by processing only the first few packets of network flows. The study departs from established methods that rely on flow statistics and hand-crafted features by introducing innovative techniques for processing and learning from raw network traffic bytes. In the thesis, two attack detection scenarios i.e. Botnet and Low-rate Denial of Service (LDoS), and four different low overhead techniques i.e. Histogram of Oriented Gradients (HOG), entropy byte histogram, byte-based feature learning, and representation learning from bytes, were used to demonstrate the applicability of the FPAC framework for early attack detection. Experiments were performed to validate the FPAC approach using the CTU botnet and the UTSA 2021 LDoS datasets. For botnet attack detection, the byte-based feature learning techniques with Decision Trees (DT) and Extreme Gradient Boosting (XGB) performed optimally, achieving 99.9% accuracy with fast detection times ranging from 0.006 to 0.026 seconds. Image-based approaches using HOG and entropy byte histogram also achieved 99.4% and 100% accuracy, respectively, while incurring reduced overheads compared to related works. The 1D CNN model matched the best byte-based results with 99.9% accuracy, validating the role of deep learning within the FPAC framework. For LDoS attack detection, which is inherently more challenging due to its subtle nature, all four lightweight techniques employed in this thesis performed favourably compared to existing approaches. The byte-based method again delivered the best results, achieving 95.8% accuracy. Image-based techniques attained accuracies of 88.9% for HOG and 92.1% for entropy byte histogram with XGB, while the representation learning from bytes approach using 1D CNN achieved 95.6% accuracy. These results outperform computationally expensive methods reported in related works, showcasing that the FPAC framework achieves high detection performance with very low overheads while also generalizing effectively across different network attack types. Keywords: network-based attacks, early attack detection, machine learning, representation learning, botnet, LDoS, HOG, entropy byte histogram.
  • Thumbnail Image
    Item type:Item,
    Ventilation System Modelling and Turbulence Minimisation
    (Word Scientific Connect, 2014) WHALLEY, R.; ABDUL-AMEER, A.
    In this feasibility study, a large scale ventilation system comprising spatially dispersed enclosed volumes, fans, ducting and airways is considered. Analytical procedures enabling the construction of simple, compact models including the relatively pointwise and significantly distributed system elements are proposed. Modeling accuracy, with the incorporation of the entrance and exit impedances and the airway, continuous energy storage and dissipation effects are emphasized. Output flow maximization, under quiescent operating conditions is investigated and the optimum relationships between the airway characteristic impedance, entrance and exit resistances are established. The minimization of the vibration and turbulence arising from the continuous compression/expansion effects arising from the input–output volume airflow difference is achieved, whilst simultaneously maximizing the output volume airflow. Variations in the parameter values are employed to confirm the effectiveness of operating under optimum conditions, for ventilation system airways with various dimensions and characteristics.
  • Thumbnail Image
    Item type:Item,
    Drill String Modeling and Stress Analysis
    (2012) Abdul-Ameer, A.
    The unsupported deflection of drill strings which are subjected to increasing gravitational loading and distortion, with borehole depth and twisting, is considered. Eccentric loading configurations which include the lateral reaction to mud flow pressure, compression, bending forces and twisting moments are incorporated in the derivations. The bending- buckling deflections and twist angle dynamics following drill string drive motor voltage changes are provided. The principal stress level is identified when operating at particular borehole depths and with specified cutting velocities.
  • Thumbnail Image
    Item type:Item,
    Hybrid Modelling of Machine Tool Drives
    (Elsevier, 2005) Whalley, R.; Ebrahimi, M.; A. Abdul-Ameer, A.
    The x-axis dynamics of a milling machine where the workpiece and saddle are mounted on supporting slides is considered. A permanent magnet motor, lead screw, ball nut and bearings are employed as the machine, traverse actuator mechanism. Hybrid, distributed–lumped parameter methods are used to model the machine tool x-axis drive system. Inclusion of the spatial configuration of the drive generates the incident, travelling and reflected vibration signature of the system. Lead screw interactive torsion and tension loading, which is excited by cutting and input disturbance conditions, is incorporated in the modelling process. Measured and results from simulation exercises are presented in comparative studies enabling the dynamic characteristics of the machine to be identified under, no load and with the application of cyclic, cutting force disturbances. The effect of the lead screw length, cutting speed and hence the load disturbance frequency are examined and the resulting performance accuracy is commented upon.
  • Thumbnail Image
    Item type:Item,
    The dynamic modelling and simulation of cutting processes in turning
    (Researchgate, 2004) Moughith, W.S.E.; Abdul-Ameer, A.A.; Khanipour, Ahmad
    A dynamic model of a lathe machine and cutting process is presented for condition monitoring purpose. The model presented permits a better understanding of the cutting process and its interaction with the machine tool, and this can be used as a part of a model based fault diagnose system.