About

I am a Lecturer in Cybersecurity with a strong academic and practical background in developing secure solutions for modern digital environments. My research focusses on securing Industrial IoT systems through lightweight cryptographic protocols and designing federated, AI-driven intrusion detection systems for resource-constrained networks.

During my doctoral studies in Cybersecurity at Cranfield University, I contributed to both research and teaching, supporting MSc-level lab instruction in areas such as virtual private networks (VPNs), cryptography, and secure systems design. I also held a lecturing position at Northumbria University London, leading postgraduate modules focussed on cybersecurity principles and practice.

My academic work has been published in high-impact journals and international conferences, with over ten peer-reviewed papers covering a breadth of cybersecurity challenges. More recent research has explored emerging topics such as machine learning for dark web detection, AI-driven phishing attacks, and defensive strategies in cyber warfare.

At the core of my research is a commitment to developing applied, forward-thinking security solutions, particularly for IoT ecosystems. I actively investigate cryptographic algorithms suited to low-power hardware and the use of explainable AI in real-time threat detection. My work also explores Zero Trust models for cloud and IoT environments and the integration of blockchain technologies to strengthen data integrity across distributed networks.

Teaching responsibilities

Current Modules Taught and Led:

• Ethical Hacking

• Security Attacks and Countermeasures

• Advanced Security Protocols

Previous Modules Taught and Led:

• Computer Networks and Security

• Network Security

• Wireless Networks and Security

• Ethical Hacking

• Information Governance and Cybersecurity

• Foundations of Computing

• Leadership in the Digital Age

Research interests

My research interests are primarily centred on advancing cybersecurity, with a strong emphasis on practical applications and emerging technologies. A core area of my work involves the detection and mitigation of diverse cyber threats, as evidenced by my publications on dark web threat detection, AI-powered phishing attacks, and cyber warfare defence strategies. I am particularly focused on leveraging machine learning and artificial intelligence to develop robust security solutions, including customized intrusion detection systems for complex networks and real-time threat analysis.

Another significant aspect of my research is the security of Internet of Things (IoT) and Industrial IoT (IIoT) ecosystems. This includes developing lightweight cryptographic protocols and authentication schemes suitable for resource-constrained devices, as well as exploring secure communication in various network environments, such as Bluetooth and WiMAX/UMTS. My work in this domain aims to ensure system integrity and data privacy in increasingly interconnected environments.

Furthermore, I am keenly interested in the application of AI for detection in Electric and Hybrid Vehicles (EVH). This new direction builds upon my expertise in AI-driven threat detection and extends it to the critical domain of automotive cybersecurity, focusing on identifying and mitigating potential vulnerabilities and attacks in smart vehicle systems. This includes exploring areas such as anomaly detection in vehicle networks, predictive maintenance based on security data, and safeguarding autonomous driving systems.

My research also encompasses broader themes such as post-quantum cryptography for hardware-constrained environments, Zero Trust security models for cloud and IoT, and the integration of blockchain technologies to enhance data integrity across distributed networks. I am committed to developing forward-thinking, explainable AI solutions that provide transparency and effectiveness in addressing complex cybersecurity challenges.

Membership of professional bodies

• Higher Education Academy Fellowship (FHEA)
• Cisco Certified 

Qualifications

• PhD in Cybersecurity - Cranfield University, UK
• MSc in Networking (Pentesting) with Distinction - Sheffield Hallam University, UK

Recent publications

• Medipelly, S. and Abosata, N, 2024. Detection of Dark Web Threats Using Machine Learning and Image Processing. arXiv preprint arXiv:2407.00704.
• Arun, A. and Abosata, N., 2024. Next Generation of Phishing Attacks using AI Powered Browsers. arXiv preprint arXiv:2406.12547.
• Fernandes, Y. and Abosata, N., 2024. Analysing India’s Cyber Warfare Readiness and Developing a Defence Strategy. arXiv preprint arXiv:2406.12568.
• Abosata, N., Al-Rubaye, S. and Inalhan, G., 2023. Customised intrusion detection for an industrial IoT heterogeneous network based on machine learning algorithms called FTL-CID. Sensors, 23(1), p.321.
• Farhan, M. and Abosata, N., 2023. Eavesdropping in Bluetooth networks. International Journal of Current Engineering and Technology, 13(2), pp.123–130.
• Akinyemi, O., Sulaiman, R. and Abosata, N., 2023. Analysis of the LockBit 3.0 and its infiltration into Advanced’s infrastructure crippling NHS services. arXiv preprint arXiv:2308.05565.
• Abosata, N., Al-Rubaye, S. and Inalhan, G., 2022. Lightweight payload encryption-based authentication scheme for advanced metering infrastructure sensor networks. Sensors, 22(2), p.534.
• Abosata, N., Al-Rubaye, S., Inalhan, G. and Emmanouilidis, C., 2021. Internet of Things for system integrity: A comprehensive survey on security, attacks and countermeasures for industrial applications. Sensors, 21(11), p.3654.
• Ishgeem, O.A., Abood, A.M., Abosata, N.R. and Alnaser, M.N., 2021. Analysis and Evaluation QoS of VoIP over WiMAX and UMTS Networks. In: 2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA). IEEE, pp.787–793.
• Abosata, N.R.A., Kemp, A.H., et al., 2019. Secure smart-home application based on IoT CoAP protocol. In: 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS). IEEE, pp.1–6.