28 September, 2020 – Paul Patras will be speaking about our Mobile Traffic Super-resolution work at the AI UK | Smart Cities event organised by The Alan Turing Institute.
19 August, 2020 – Our work "Tiki-Taka: Attacking and Defending Deep Learning-based Intrusion Detection Systems" was accepted at CCSW 2020.
10 August, 2020 – Our work "Microscope: Mobile Service Traffic Decomposition for Network Slicing as a Service" was accepted at MobiCom 2020.
7 April 2020 – Work with colleagues at the IMDEA Networks Institute on building a Machine Learning-based Framework for Optimizing the Operation of Future Networks was accepted for publication in the Data Science and Artificial Intelligence for Communications Series of the IEEE Communications Magazine.
27 March 2020 – Chaoyun Zhang defended his PhD viva successfully!
25 March 2020 – Paul Patras gave a webinar about our work on mobile traffic analysis using deep learning at Samsung AI Cambridge.
18 December, 2019 – Our survey on deep learning in mobile networking is on the 4th position in the IEEE Communications Surveys & Tutorials popularity chart!
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.
Chaoyun Zhang is a recent Ph.D. graduate from the School of Informatics at the University of Edinburgh. He is interested in applying deep learning techniques to problems in the computer networking domain, including mobile big data analysis, spatio-temporal and geospatial mobile data forecasting, and network control. In the past, he has worked on computer vision and energy disaggregation problems.
Haoyu Liu is a first-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 first-year PhD 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 first-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.
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.