1 July, 2021 – Alec Diallo won the Brendan Murphy Prize at the 33rd Multi-Service Networks workshop.
24 June, 2021 – Paul Patras gave a keynote entitled &nquot;Can we trust AI to secure our edge?&nquot; at the 1st Workshop on Security and Privacy for Mobile AI (MAISP) co-located with ACM MobiSys 2021.
4 June, 2021 – Work with colleagues at IDCOM on Deep Reinforcement Learning-Based Beam Training for Spatially Consistent Millimeter Wave Channels accepted at PIMRC '21.
12 May, 2021 – Alec Diallo presented our paper "Adaptive Clustering-based Malicious Traffic Classification at the Network Edge" at IEEE INFOCOM. The source code of the implementation is available on GitHub.
17 December, 2020 – Our security analysis of the m-tickets App presented at ISC 2020. The recording of Jorge's talk is available here. The Data-Driven Innovation programme wrote this piece about our work.
2 December, 2020 – Our work "CloudLSTM: A Recurrent Neural Model for Spatiotemporal Point-cloud Stream Forecasting" was accepted at AAAI-21.
Paul Patras leads the Mobile Intelligence Lab. He is a Reader (Associate Professor) and Chancellor's Fellow in the School of Informatics at the University of Edinburgh and a member of the Institute for Computing Systems Architecture (ICSA). He holds a Ph.D. in Telematics Engineering from University Carlos III of Madrid and is the first alumnus of the IMDEA Networks Institute. He held visiting research positions at the University of Brescia, Northeastern University, Technical University Darmstadt, and Rice University. His research bridges the gap between fundamental mathematical models and real-world applications of networked systems, focusing on problems related to artificial intelligence in mobile networks, performance optimisation, security and privacy, prototyping and test beds.
Haoyu Liu is a second-year PhD student in the School of Informatics at the University of Edinburgh. He received B.Sc. degrees from the University of Edinburgh and the South China University of Technology in May 2019. His research focuses on developing machine learning tools for IoT and Network Security.
Yini Fang is a second-year MPhil student in the School of Informatics at the University of Edinburgh. She is investigating mobile traffic analysis problems by applying a range of machine learning techniques, including reinforcement learning.
Alec Diallo is a second-year PhD student at the University of Edinburgh. Previously, he worked in Paris as a machine learning research engineer. His current research seeks to bridge the gap between the ever-evolving nature of cyber threats and the security and privacy of users' data on networked systems, by using Artificial Intelligence to build automatic network threat detection and counteraction mechanisms.
Rui Li is currently a researcher at Samsung AI Cambridge, UK and an affiliated member of the Mobile Intelligence Lab. She graduated with a PhD in Informatics from the University of Edinburgh in 2019, and her thesis was advised by Dr Paul Patras. Rui is interested in domain customisations of machine learning in mobile networks and wireless communications.
Luyang Xu, Computer Network Information Center, Chinese Academy of Sciences, Nov 2020 – Oct 2021.
Alexis Duque, Research Associate, 2020–2021 (now at Net AI)
Chaoyun Zhang, PhD, 2016–2020 (now at Tencent)
Our survey of deep learning in mobile and wireless networks has been picked up by Chinese media and was covered in The Heart of the Machine, Trencent, NetEase, SINA, and Sohu.com.
Our security and privacy investigation of the Belkin WeMo ecosystem featured in The National, The Herald, Phys.org, Technology.org, ECN Mag, tekk.tv, qianjia.com, The Edinburgh Reporter, Edinburgh Live, Scottish Construction Now, Scottish Housing News. Forth Radio also ran a short interview with me about this work.
In-depth city-scale mobile network traffic analysis is essential for a range of network engineering, such as network slicing and anticipatory resources provisioning. Unraveling in real time the geographic distribution of data consumption and predicting future demands in real-time remains challenging, due to substantial measurements, storage and post-processing overheads. This project tackles these challenges by developing dedicated deep learning techniques that extract abstract features characteristic to mobile traffic, in order to serve intelligent resource management tools in future networks.
The number of Internet-connected devices is expected to reach 1 trillion by 2035 and a large fraction of these devices will become an integral part of households. This can improve the productivity and quality of life of their users, but also exposes them to new cyber security and privacy risks. This project focuses on designing intelligent algorithms that can detect and counteract cyber threats originating from or targeting Internet of Things (IoT) devices in user homes, without requiring manual intervention. We are also investigating novel mechanisms that can allow users to operate safely with potentially compromised gadgets, while ensuring such devices will not damage the networking infrastructure or the operation of other equipment.