Dr Nabeel Khan

About

I am currently a Lecturer of Cyber Security and Computer Forensics with in the Department of Networks and Digital Media. During my doctoral and postdoctoral research at Kingston University, I have contributed on various EU and UK projects, e.g., EU FP7 Concerto, EPSRC IoSIRE project, and an industrial funded project with DoCoMo Eurolabs, in areas such as Radio Resource Allocation, IoT, Network Security, Video optimisation over wireless networks, Neuromorphic Vision Sensors and Artificial Intelligence.

My general research interests include Cyber Security challenges in IoT, Radio Access Technologies, Multimedia Communications, Computer Vision, Internet-of-Things protocols, Machine Learning in Cyber Security, Cryptography and wireless networks.

Academic responsibilities

Lecturer of Cyber Security and Computer Forensics

Qualifications

  • PhD in Computer Science (Kingston University, London, UK)
  • MSc in Networking and Data Communications (Kingston University, London, UK)
  • BS in Electronics Engineering (Sir Syed University of Engineering and Technology, Karachi, Pakistan)

Teaching and learning

Teaching responsibilities:-

Ethical Hacking (CI5235).

Threat Hunting Analysis and Mitigation (CI6280).

Module Leadership responsibilities:-

Threat Hunting Analysis and Mitigation (CI6280).

Professional Development activities:

Currently enrolled in Introduction to Learning and Teaching course. 

Undergraduate courses taught

Postgraduate courses taught

Research

My research activities fall within the area of Visual Internet of Things titled as Event-based Vision meets Artificial Intelligence under the constraints of IoT devices. Internet of Things (IoT) framework has continued to evolve; initially IoT systems comprised of lots of small, closed networks, but this concept has evolved to incorporate larger more connected networks, for instance smart cities with smart transport infrastructures. There have been continued developments of the IoT framework, but these rarely include visual data – mainly because of the high bandwidth and high-power consumption of the visual capturing devices. Since sight is our most powerful sense, combination of visual sensors with other IoT data streams and adding machine analytics would make it immensely valuable.

 Dynamic Vision Sensors (DVS) are based on the principle of biological sensing, i.e., they report only the on/off triggering of brightness in the observed scene. Differently from frame-based cameras, where frames are acquired at regular time intervals, DVS asynchronously acquire pixel level light intensity changes, with a time resolution up to a microsecond. Events are triggered whenever there is either motion of the neuromorphic vision sensor or motion / change of light conditions in the scene or both. In other words, no data is transmitted for stationary vision sensors and static scenes. These unique properties enable neuromorphic vision sensors to achieve low bandwidth, wide dynamic range, low-latency, and low power requirements. These unique characteristics of the DVS offer advantages over conventional vision sensors in real-time interaction systems such as robotics, drones, and autonomous driving. Furthermore, this sensing technology has the potential to meet the low bandwidth and low power requirements of the IoT framework. 

Apart from Visual Internet of Things, my research work also focuses on cybersecurity issues in IoT.

Google scholar link for list of publications

https://scholar.google.co.uk/citations?user=-RfFfxYAAAAJ&hl=en

 

Qualifications and expertise

  • Cyber Threat Hunting
  • Multimedia Communications
  • Data modelling, Data compression
  • Ethical Hacking
  • Network security
  • IoT protocols

Areas of specialism

  • Cybersecurity
  • Machine Learning
  • Internet of Things
  • Dynamic Vision Sensors

Scholarly affiliations

  • IEEE Member