Joined us a s a new PhD student in October 2018
Joined us a s a new PhD student in October 2018
This post offers a unique opportunity to conduct research on the Safety and Security challenges in the Internet of Things, with access to a wide pool of academic, industrial, and governmental stakeholders and research and development “in the wild”. The successful researcher will be responsible for reviewing research outcomes from PETRAS projects, generalising lessons learned across projects and industry sectors and contributing to the Hub’s research agenda and delivery programme. You will be expected to collaborate with partners on projects across the Hub and to contribute to research activities with particular focus in security of cyber-physical systems and embedded devices.
Deadline: 27th May 2018
Further Details: Full Advert
His general interests fall within the intersection of cybersecurity and mathematics. His current research is on the security of machine learning algorithms, primarily adversarial machine learning. He also dabbles a bit in cryptocurrencies and distributed ledgers.
Find him on LinkedIn.
Javier joined the group as a PhD student in April 2018. He received his MEng in Telecommunications Engineering and his MRes in Multimedia and Communications from University Carlos III of Madrid (Spain). He is currently interested in the investigation and evaluation of Machine Learning algorithms in adversarial settings.
An Infographic based on our work has been published by IoT UK, which describes the fusion of the digital, physical and human aspects in IoT systems the vulnerabilities this introduces and the way to leverage these aspects to defend systems against malicious threats.
A post/blog entry on the trustworthiness of cyber-physical systems including consideration of Malicious Data Injections, Adversarial Machine Learning and Bayesian Risk Assessment. Follow this link to the post.
Malicious data injections pose a severe threat to the systems based on Wireless Sensor Networks (WSNs) since they give the attacker control over the measurements, and on the system’s status and response in turn. Malicious measurements are particularly threatening when used to spoof or mask events of interest, thus eliciting or preventing desirable responses. Spoofing and masking attacks are particularly difficult to detect since they depict plausible behaviours, especially if multiple sensors have been compromised and collude to inject a coherent set of malicious measurements. Previous work has tackled the problem through measurements inspection, which analyses the inter-measurements correlations induced by the physical phenomena. However, these techniques consider simplistic attacks and are not robust to collusion. Moreover, they assume highly predictable patterns in the measurements distribution, which are invalidated by the unpredictability of events. We design a set of techniques that effectively detect malicious data injections in the presence of sophisticated collusion strategies, when one or more events manifest. Moreover, we build a methodology to characterise the likely compromised sensors. We also design diagnosis criteria that allow us to distinguish anomalies arising from malicious interference and faults. In contrast with previous work, we test the robustness of our methodology with automated and sophisticated attacks, where the attacker aims to evade detection. We conclude that our approach outperforms state-of-the-a
rt approaches. Moreover, we estimate quantitatively the WSN degree of resilience and provide a methodology to give a WSN owner an assured degree of resilience by automatically designing the WSN deployment. To deal also with the extreme scenario where the attacker has compromised most of the WSN, we propose a combination with software attestation techniques, which are more reliable when malicious data is originated by a compromised software, but also more expensive, and achieve an excellent trade-off between cost and resilience.
Vittorio P. Illiano, Andrea Paudice, Luis Muñoz-González, and Emil C. Lupu. 2018. Determining Resilience Gains From Anomaly Detection for Event Integrity in Wireless Sensor Networks. ACM Trans. Sen. Netw. 14, 1, Article 5 (February 2018), 35 pages. DOI: https://doi.org/10.1145/3176621
Abstract: Measurements collected in a wireless sensor network (WSN) can be maliciously compromised through several attacks, but anomaly detection algorithms may provide resilience by detecting inconsistencies in the data. Anomaly detection can identify severe threats to WSN applications, provided that there is a sufficient amount of genuine information. This article presents a novel method to calculate an assurance measure for the network by estimating the maximum number of malicious measurements that can be tolerated. In previous work, the resilience of anomaly detection to malicious measurements has been tested only against arbitrary attacks, which are not necessarily sophisticated. The novel method presented here is based on an optimization algorithm, which maximizes the attack’s chance of staying undetected while causing damage to the application, thus seeking the worst-case scenario for the anomaly detection algorithm. The algorithm is tested on a wildfire monitoring WSN to estimate the benefits of anomaly detection on the system’s resilience. The algorithm also returns the measurements that the attacker needs to synthesize, which are studied to highlight the weak spots of anomaly detection. Finally, this article presents a novel methodology that takes in input the degree of resilience required and automatically designs the deployment that satisfies such a requirement.
Video of the presentation given in the Cyber security session of the Codex ‘World’s Top 50 Innovators’. The session also included presentations from Mikko Hypponen, Chief Research Officer, F-Secure, Andrew Rubin, CEO and Founder, Illumio, and Dave Palmer, Director of Technology, Darktrace as well as a panel discussion.
Jukka joined the group as a PhD student on the HiPEDS CDT.
His current research interests are improving the resilience of partially compromised networks, and network forensics.
Jukka holds an MSc in Computing Science from Imperial College London, and degrees in economics from the University of Oxford and the University of St Andrews.