23 January, 2024 – Our work Stable-Sketch: A Versatile Sketch for Accurate, Fast, Web-Scale Data Stream Processing was accepted for publication at The Web Conference 2024.

3 December, 2023 – Our work Cluster and Conquer: Malicious Traffic Classification at the Edge will appear in IEEE Transactions on Network and Service Management.

19 September, 2023 – Work documenting Amoeba, our adversarial reinforcement learning approach to circumventing ML-supported network censorship was accepted at ACM CoNEXT.

14 August, 2023 – Our paper Sabre: Cutting through Adversarial Noise with Adaptive Spectral Filtering and Input Reconstruction will appear at IEEE S&P Spring 2024.

5 August, 2023 – Work documenting our Tight-Sketch: A High-Performance Sketch for Heavy Item-Oriented Data Stream Mining with Limited Memory Size accepted at ACM CIKM.

4 August, 2023 – Work on a Fast and Accurate Sketch for Persistent Item Lookup accepted for publication in ACM/IEEE Transactions on Networking.

3 August, 2023 – Work on Deciphering Clusters With a Deterministic Measure of Clustering Tendencyaccepted for publication in IEEE Transactions on Knowledge and Data Engineering.

20 July, 2023 – Paul spoke on a panel on ``What are the key challenges (research, market, infrastructures) of future telecommunications?'' at the launch of RESTART Foundation: RESearch and innovation on future Telecommunications systems and networks, to make Italy more smART, Bari, Italy.

8 June, 2023 – AI for Good webinar about how to improve operational efficiency in mobile networks using deep traffic analytics.

29 May, 2023 – Haoyu Liu presenting our Android OS privacy work at ACM WiSec. Further documentation about our study is available at

19 April, 2023 – Paul Patras spoke about the work with Net AI on revolutionising mobile network management via AI-driven analytics at World Summit AI Americas in Montreal.

24 February, 2023 – NGN Webinar about how to unlock the 5G RoI with AI-driven traffic analytics.

16 February, 2023 – Interview with Voice of America about the Android OS privacy research.

21 December, 2022 – Work on the privacy of Android OS firmware distributions on the Chinese market accepted for publication at ACM WiSec.

19 December, 2022 – Work on Android OS privacy accepted for publication in PLOS ONE.


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.

Alec Diallo

Alec Diallo is a postdoctoral research associated. His research crosses the boundaries between artificial intelligence, neural network explainability, computer networking, security and privacy.

Weihe Li

Weihe Li is a first-year PhD student in the School of Informatics at the University of Edinburgh. He received a M.E. in Computer Science and Technology from the Central South University China. His research focuses on developing new techniques for accurate detection of specific types of traffic flows in high-speed networks.

Haoyu Liu

Haoyu Liu is currently a machine learning researcher at Net AI and an affiliated member of the Mobile Intelligence Lab. He graduated with a PhD in Informatics from the University of Edinburgh in 2023 and is interested in developing machine learning tools for mobile traffic analytics, IoT and network security, and user privacy.

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.

Past members

Rupen Mitra, MPhil, 2021–2023 (now at Net Reply)

Yini Fang, MPhil, 2019–2022 (now at Chinese University of Hong Kong)

Luyang Xu, Research Visitor, 2020 – 2021 (now with Computer Network Information Center, Chinese Academy of Sciences)

Alexis Duque, Post-doctoral Research Associate, 2020–2021 (now at Net AI)

Chaoyun Zhang, PhD, 2016–2020 (now at Microsoft Research Asia)

Media Coverage

Our research on the privacy of different Android distributions was featured in EuroNews, Hacker News, The Register, Yahoo News, Fox News, Voice of America, Help Net Security, The Irish Times, Belfast Telegraph, Irish Examiner, IT World Canada, Asia Financial, DIGIT, Science X, Techdirt, RT, Newsmax, Texplore, India Education Daily, Hypertext, 91mobiles, BGR, Techstory, Heise (DE), (DE), Clubic (FR), ICTjournal (FR), OK Diario (ES), Gigazine (JP), CityPortal (GR), Fastweb (IT), IlCitadinoOnline (IT), TuttoAndroid (IT), Tiroler Tageszeitung (AT), Evenimentul Zilei (RO), GRYOnline (PL), Kallxo (XK), Diario Libre (RD), Droidsans (Th).

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

Our security and privacy investigation of the Belkin WeMo ecosystem featured in The National, The Herald,,, ECN Mag,,, The Edinburgh Reporter, Edinburgh Live, Scottish Construction Now, Scottish Housing News. Forth Radio also ran a short interview with me about this work.


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


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