News

26 November, 2020 – Paul Patras presented our work on mobile traffic super-resolution and decomposition at Huawei's DataCom "Global Connect 2020".

16 November, 2020 – Alexis Duque spoke about our work on mobile traffic decomposition using deep learning, in a webinar part of the ITU AI for Good series.

7 November, 2020 – Eiko Yoneki and Paul Patras are organising the first "Workshop on Machine Learning and Systems (EuroMLSys)" co-located with EuroSys '21.

3 November, 2020 – Paul Patras spoke at the ITU Webinar on "Towards a truly Autonomous Network".

12 October, 2020 – Work with colleagues at ETRI on driver behaviour recognition using CNNs was accepted for publication in IEEE Access.

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.

21 September, 2020 – Video presentations of our Microscope work presented at ACM MobiCom 2020 are available on YouTube in short (5-min) and long (20-min) formats.

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.

10 July 2020 – Rui Li and Paul Patras are part of the organising committee of Distributed ML 2020, the 1st Workshop on Distributed Machine Learning, co-located with ACM CoNEXT 2020.

Members

Paul Patras

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.

Alexis Duque

Alexis Duque is a Research Associate deeply involved in developing Microscope, a patent-pending technology launched by the Net AI spinout. Since 2015, he drives the research and innovation strategy in an IoT design house where he collaborates with academia to bridge the gap between IoT, cybersecurity, and machine learning. He received a Ph.D. in Computer Science from University of Lyon and a Master of Engineering in Telecommunication from INSA Lyon. During his Ph.D., he developed a patented technology allowing bidirectional visible light communication for IoT devices, now commercialized under the brand Kiwink. His research interests are at the crossroad of wireless communication, Internet of Things, machine learning, and cybersecurity.

Haoyu Liu

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

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

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

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.

Visitors

Luyang Xu, Computer Network Information Center, Chinese Academy of Sciences, Nov 2020 –Oct 2021.

Past members

Chaoyun Zhang, PhD, 2016–2020 (now at Tencent)

Media Coverage

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.

Research

Deep Learning Powered Mobile Network Analytics

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.

Automatic Threat Detection and Anomaly Counteraction in Home IoT

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.

Recent Publications

Sponsors

The Mobile Intelligence Lab gratefully acknowledges the support of the following organizations.