Adversarial actors have shown their ability to infiltrate enterprise networks deployed around Cyber Physical Systems (CPSs) through social engineering, credential stealing and file-less infections. When inside, they can gain enough privileges to maliciously call legitimate APIs and apply unsafe control actions to degrade the system performance and undermine its safety. Our work lies at the intersection of security and safety, and aims to understand dependencies among security, reliability and safety in CPS/IoT. We present a methodology to perform hazard driven threat modelling and impact assessment in the context of CPSs. The process starts from the analysis of behavioural, functional and […]
Zaid joined the group as a Research Associate in May 2020. His activities focus on federated learning and adversarial machine learning.
Research Assistant salary in the range: £35,477 to £38,566 per annum* Research Associate salary in the range: £40,215 to £47,579 per annum Full Time, Fixed Term appointment for to start ASAP until the 31/11/2021 The Resilient Information Systems Security Group (RISS) in the Department of Computing at Imperial College London is seeking a Research Assistant/Associate to work on EU funded Musketeer project. Musketeer aims to create a federated and privacy preserving machine learning data platform, that is interoperable, efficient and robust against internal and external threats. Led by IBM the project involves 11 academic and industrial partners from 7 countries […]
Our paper on procedural noise adversarial examples has been accepted to the 26th ACM Conference on Computer and Communications Security (ACM CCS ’19). official: https://dl.acm.org/citation.cfm?id=3345660 code: https://github.com/kenny-co/procedural-advml Abstract: Deep Convolutional Networks (DCNs) have been shown to be vulnerable to adversarial examples—perturbed inputs specifically designed to produce intentional errors in the learning algorithms at test time. Existing input-agnostic adversarial perturbations exhibit interesting visual patterns that are currently unexplained. In this paper, we introduce a structured approach for generating Universal Adversarial Perturbations (UAPs) with procedural noise functions. Our approach unveils the systemic vulnerability of popular DCN models like Inception v3 and YOLO […]
We have released the code with a demo or our poisoning attack described in the paper “Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization.” You can access the code in this link.
Fulvio joined the group as a Visiting Researcher. His activities focused on analysing and modelling hybrid threats.
Aaron will be presenting a paper based on his MSc thesis work “Exploiting Correlations to Detect False Data Injections in Low-Density Wireless Sensor Networks” at 5th ACM CPSS 2019, co-located workshop with ACM ASIACCS.
Aaron joined RISS in April 2019. To find out more, visit his website.
The massive increase in data collected and stored worldwide calls for new ways to preserve privacy while still allowing data sharing among multiple data owners. Today, the lack of trusted and secure environments for data sharing inhibits data economy while legality, privacy, trustworthiness, data value and confidentiality hamper the free flow of data. By the end of the project, MUSKETEER aims to create a validated, federated, privacy-preserving machine learning platform tested on industrial data that is inter-operable, scalable and efficient enough to be deployed in real use cases. MUSKETEER aims to alleviate data sharing barriers by providing secure, scalable and […]