• A wireless sensor network border monitoring system: Deployment issues and routing protocols

      Hammoudeh, Mohammad; Al-Fayez, Fayez; Lloyd, Huw; Newman, Robert; Adebisi, Bamidele; Bounceur, Ahcène; Abuarqoub, Abdelrahman; University of Wolverhampton, Manchester Metropolitan University (IEEE, 2017-02-20)
      External border surveillance is critical to the security of every state and the challenges it poses are changing and likely to intensify. Wireless sensor networks (WSN) are a low cost technology that provide an intelligence-led solution to effective continuous monitoring of large, busy, and complex landscapes. The linear network topology resulting from the structure of the monitored area raises challenges that have not been adequately addressed in the literature to date. In this paper, we identify an appropriate metric to measure the quality of WSN border crossing detection. Furthermore, we propose a method to calculate the required number of sensor nodes to deploy in order to achieve a specified level of coverage according to the chosen metric in a given belt region, while maintaining radio connectivity within the network. Then, we contribute a novel cross layer routing protocol, called levels division graph (LDG), designed specifically to address the communication needs and link reliability for topologically linear WSN applications. The performance of the proposed protocol is extensively evaluated in simulations using realistic conditions and parameters. LDG simulation results show significant performance gains when compared with its best rival in the literature, dynamic source routing (DSR). Compared with DSR, LDG improves the average end-to-end delays by up to 95%, packet delivery ratio by up to 20%, and throughput by up to 60%, while maintaining comparable performance in terms of normalized routing load and energy consumption.
    • Proactive threat detection for connected cars using recursive Bayesian estimation

      al-Khateeb, Haider; Epiphaniou, Gregory; Reviczky, Adam; Karadimas, Petros; Heidari, Hadi (IEEE, 2017-12-12)
      Upcoming disruptive technologies around autonomous driving of connected cars have not yet been matched with appropriate security by design principles and lack approaches to incorporate proactive preventative measures in the wake of increased cyber-threats against such systems. In this paper, we introduce proactive anomaly detection to a use-case of hijacked connected cars to improve cyber-resilience. Firstly, we manifest the opportunity of behavioural profiling for connected cars from recent literature covering related underpinning technologies. Then, we design and utilise a new dataset file for connected cars influenced by the Automatic Dependent Surveillance – Broadcast (ADS–B) surveillance technology used in the aerospace industry to facilitate data collection and sharing. Finally, we simulate the analysis of travel routes in real-time to predict anomalies using predictive modelling. Simulations show the applicability of a Bayesian estimation technique, namely Kalman Filter. With the analysis of future state predictions based on the previous behaviour, cyber-threats can be addressed with a vastly increased time-window for a reaction when encountering anomalies. We discuss that detecting real-time deviations for malicious intent with predictive profiling and behavioural algorithms can be superior in effectiveness than the retrospective comparison of known-good/known-bad behaviour. When quicker action can be taken while connected cars encounter cyber-attacks, more effective engagement or interception of command and control will be achieved.