Visible to the public Biblio

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2020-12-07
Labib, N. S., Brust, M. R., Danoy, G., Bouvry, P..  2019.  Trustworthiness in IoT – A Standards Gap Analysis on Security, Data Protection and Privacy. 2019 IEEE Conference on Standards for Communications and Networking (CSCN). :1–7.
With the emergence of new digital trends like Internet of Things (IoT), more industry actors and technical committees pursue research in utilising such technologies as they promise a better and optimised management, improved energy efficiency and a better quality living through a wide array of value-added services. However, as sensing, actuation, communication and control become increasingly more sophisticated, such promising data-driven systems generate, process, and exchange larger amounts of security-critical and privacy-sensitive data, which makes them attractive targets of attacks. In turn this affirms the importance of trustworthiness in IoT and emphasises the need of a solid technical and regulatory foundation. The goal of this paper is to first introduce the concept of trustworthiness in IoT, its main pillars namely, security, privacy and data protection, and then analyse the state-of-the-art in research and standardisation for each of these subareas. Throughout the paper, we develop and refer to Unmanned Aerial Vehicles (UAVs) as a promising value-added service example of mobile IoT devices. The paper then presents a thorough gap analysis and concludes with recommendations for future work.
2020-08-03
Al-Emadi, Sara, Al-Ali, Abdulla, Mohammad, Amr, Al-Ali, Abdulaziz.  2019.  Audio Based Drone Detection and Identification using Deep Learning. 2019 15th International Wireless Communications Mobile Computing Conference (IWCMC). :459–464.
In recent years, unmanned aerial vehicles (UAVs) have become increasingly accessible to the public due to their high availability with affordable prices while being equipped with better technology. However, this raises a great concern from both the cyber and physical security perspectives since UAVs can be utilized for malicious activities in order to exploit vulnerabilities by spying on private properties, critical areas or to carry dangerous objects such as explosives which makes them a great threat to the society. Drone identification is considered the first step in a multi-procedural process in securing physical infrastructure against this threat. In this paper, we present drone detection and identification methods using deep learning techniques such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Convolutional Recurrent Neural Network (CRNN). These algorithms will be utilized to exploit the unique acoustic fingerprints of the flying drones in order to detect and identify them. We propose a comparison between the performance of different neural networks based on our dataset which features audio recorded samples of drone activities. The major contribution of our work is to validate the usage of these methodologies of drone detection and identification in real life scenarios and to provide a robust comparison of the performance between different deep neural network algorithms for this application. In addition, we are releasing the dataset of drone audio clips for the research community for further analysis.
2017-09-05
Baker, Richard, Martinovic, Ivan.  2016.  Secure Location Verification with a Mobile Receiver. Proceedings of the 2Nd ACM Workshop on Cyber-Physical Systems Security and Privacy. :35–46.

We present a technique for performing secure location verification of position claims by measuring the time-difference of arrival (TDoA) between a fixed receiver node and a mobile one. The mobile node moves randomly in order to substantially increase the difficulty for an attacker to make false messages appear genuine. We explore the performance and requirements of such a system in the context of verifying aircraft position claims made over the Automatic Dependent Surveillance - Broadcast (ADS-B) system through the use of simulation and find that it correctly detects false claims with a peak accuracy of over 97\textbackslash% for the most complex attack modelled; requiring only 75m of deviation between the reported position and the actual position in order for a false claim to be detected. We then report on our design for a mobile receiver and our construction of a prototype using low-cost COTS equipment. We discuss some additional benefits of incorporating a mobile node, examine the difficulties to be overcome and explore the applicability of the approach in other location verification use-cases.