June 7, 2016

Publications

2020

  1. “Two Decades of AI4NETS – Challenges & Research Directions”
    Pedro Casas
    @AnNet, 5th IEEE/IFIP International Workshop on Analytics for Network and Service Management.
  1. “WhatsThat? On the Usage of Hierarchical Clustering for Unsupervised Detection & Interpretation of Network Attacks”
    Pavol Mulinka, Kensuke Fukuda, Pedro Casas, Lukas Kencl
    @IEEE Euro S&P, 5th Workshop on Traffic Measurements for Cybersecurity (WTMC).
  1. “HUMAN – Hierarchical Clustering for Unsupervised Anomaly Detection & Interpretation”
    Pavol Mulinka, Pedro Casas, Kensuke Fukuda, Lukas Kencl
    @NoF, 11th IEEE International Conference on Networks of the Future.
  1. “On the Usage of Generative Models for Network Anomaly Detection in Multivariate Time-Series”
    Gastón García González, Pedro Casas, Alicia Fenández, Gabriel Gómez
    @IFIP Performance, Workshop on AI in Networks (WAIN).
  1. “ViCrypt to the Rescue: Real-time, Machine Learning-driven Video QoE Monitoring for Encrypted Streaming Traffic”
    Sarah Wassermann, Michael Seufert, Pedro Casas, Gang Li, Li Kuang
    @TNSM, IEEE Transactions on Network and Service Management, vol. 17, no. 4, pp. 2007-2023.
  1. “Adaptive and Reinforcement Learning Approaches for Online Network Monitoring and Analysis”
    Sarah Wassermann, T‪hibaut Cuvelier, Pavol Mulinka, Pedro Casas
    @TNSM, IEEE Transactions on Network and Service Management.

2019

  1. “A Survey on Big Data for Network Traffic Monitoring and Analysis”
    Alessandro D’Alconzo, Idilio Drago, Andrea Morichetta, Marco Mellia, Pedro Casas
    @TNSM, IEEE Transactions on Network and Service Management, vol. 16 (3), pp. 800-813.
  1. “Stream-based Machine Learning for Real-time QoE Analysis of Encrypted Video Streaming Traffic”
    M. Seufert, P. Casas, N. Wehner, G. Li, L. Kuang
    3rd International Workshop on Quality of Experience Management.
  1. “Deep in the Dark – Deep Learning-based Malware Traffic Detection Without Expert Knowledge”
    G. Marín, P. Casas, G. Capdehourat
    IEEE S&P 2019 Workshops, 2nd Workshop on Deep Learning and Security (DLS).
  1. “MLSEC – Benchmarking Shallow and Deep Machine Learning Models for Network Security”
    P. Casas, G. Marín, G. Capdehourat, M. Korczynski
    @IEEE S&P Workshops, 4th Workshop on Traffic Measurements for Cybersecurity (WTMC).
  1. “RAL – Improving Stream-Based Active Learning by Reinforcement Learning”
    S. Wassermann, T. Cuvelier, P. Casas
    @ECML-PKDD, Workshop on Interactive Adaptive Learning (IAL).
  1. “ADAM & RAL: Adaptive Memory Learning and Reinforcement Active Learning for Network Monitoring”
    S. Wassermann, T. Cuvelier, P. Mulinka, P. Casas
    @CNSM, International Conference on Network and Service Management.
  1. “Should I (re)Learn or Should I Go(on)? Stream Machine Learning for Adaptive Defense against Network Attacks”
    P. Casas, P. Mulinka, J. Vanerio
    @ACM CCS, 6th ACM Workshop on Moving Target Defense (MTD).
  1. “Continuous and Adaptive Learning over Big Streaming Data for Network Security”
    P. Mulinka, P. Casas, J. Vanerio
    @IEEE CloudNET, 8th IEEE International Conference on Cloud Networking.
  1. “EXPLAIN-IT: Towards Explainable AI for Unsupervised Network Traffic Analysis”
    A. Morichetta, P. Casas, M. Mellia
    @ACM CoNEXT Workshops, 3rd Workshop on Big DAta, Machine Learning and Artificial Intelligence for Data Communication Networks (Big-DAMA).
  1. “4 Years of EU Cookie Law: Results and Lessons Learned”
    Martino Trevisan, Stefano Traverso, Eleonora Bassi, Marco Mellia
    @PoPET, Proceedings on Privacy Enhancing Technologies.
  1. “Absolute Cluster Validity”
    F. Iglesias, T. Zseby, A. Zimek
    @TPAMI, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42 (9), pp. 2096-2112.
  1. “MDCGen: Multidimensional Dataset Generator for Clustering”
    F. Iglesias Vazquez, T. Zseby, D. Ferreira, A. Zimek
    Journal of Classification, vol 36, pp. 599-618.
  1. “Extreme Dimensionality Reduction for Network Attack Visualization with Autoencoders”
    D. Ferreira, F. Iglesias Vazquez, T. Zseby
    @IJCNN, International Joint Conference on Neural Networks.
  1. “Are Network Attacks Outliers? A Study of Space Representations and Unsupervised Algorithms”
    Felix Iglesias, Alexander Hartl, Tanja Zseby, Arthur Zimek
    @ECML-PKDD, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases.
  1. “NTARC: A Data Model for the Systematic Review of Network Traffic Analysis Research”
    F. Iglesias, D. Ferreira, G. Vormayr, M. Bachl, T. Zseby
    @MDPI, Applied Sciences, vol. 10 (12).
  1. “PAIN: A Passive Web performance indicator for ISPs”
    Martino Trevisan, Idilio Drago, Marco Mellia
    @COMNET, Computer Networks, vol. 149, pp. 115-126.
  1. “Stream-based Machine Learning for Real-time QoE Analysis of Encrypted Video Streaming Traffic”
    Michael Seufert, Pedro Casas, Nikolas Wehner, Gang Li, Li Kuang
    @ICIN 2019 Workshops, 3rd Workshop on QoE-Managament.

2018

  1. “Outlier Detection Based on Low Density Models”
    Félix Iglesias Vazquez, Tanja Zseby, Arthur Zimek
    @ICDM 2018, IEEE International Conference on Data Mining, ICDM Workshop on Data Science and Big Data Analytics.
  1. “Analysis of Lightweight Feature Vectors for Attack Detection in Network Traffic”
    Fares Meghdouri, Tanja Zseby, Félix Iglesias
    @MDPI Applied Sciences, vol. 8 (11), 2018.
  1. “Fuzzy Classification Boundaries Against Adversarial Network Attacks”
    Félix Iglesias Vazquez, Jelena Milosevic, Tanja Zseby
    @Fuzzy Sets and Systems, vol. 368 (1), pp. 20-35, 2018.
  1. “Achieving Horizontal Scalability in Density-based Clustering for URLs”
    Azadeh Faroughi, Reza Javidan, Marco Mellia, Andrea Morichetta, Francesca Soro, Martino Trevisan
    @IEEE Big Data, IEEE International Conference on Big Data.
  1. “You, the Web and Your Device: Longitudinal Characterization of Browsing Habits”
    Luca Vassio, Idilio Drago, Marco Mellia, Zied Ben-Houidi, Mohamed Lamine Lamali
    @TWEB, ACM Transactions on the Web, vol. 12 (4), pp. 24:1-24:30.
  1. “Remember the Good, Forget the Bad, do it Fast: Continuous Learning over Streaming Data”
    Pavol Mulinka, Sarah Wassermann, Gonzalo Marín, Pedro Casas
    @NeurIPS 2018 Workshops, Workshop on Continual Learning.
  1. “DeepSec meets RawPower – Deep Learning for Detection of Network Attacks Using Raw Representations”
    Gonzalo Marín, Pedro Casas, Germán Capdehourat
    @IFIP Performance 2018 Workshops, Workshop on AI in Networks.
  1. “Machine Learning Models for YouTube QoE and User Engagement Prediction in Smartphones”
    Sarah Wassermann, Nikolas Wehner, Pedro Casas
    @IFIP Performance 2018 Workshops, Workshop on AI in Networks.
  1. “Robust URL Classification With Generative Adversarial Networks “
    Martino Trevisan, Idilio Drago
    @IFIP Performance 2018 Workshops, Workshop on AI in Networks.
  1. “Five Years at the Edge: Watching Internet from the ISP Network”
    Martino Trevisan, Danilo Giordano, Idilio Drago, Marco Mellia, Maurizio Munafò
    @CoNEXT 2018, ACM 14th International Conference on emerging Networking EXperiments and Technologies.
  1. “Vivisecting Blockchain P2P Networks – Unveiling the Bitcoin IP Network”
    Sami Ben Mariem, Pedro Casas, Benoit Donnet
    @CoNEXT 2018, ACM CoNEXT 2018 Student Workshop.
  1. “Beauty is in the Eye of the Smartphone Holder – A Data Driven Analysis of YouTube Mobile QoE”
    Nikolas Wehner, Sarah Wassermann, Pedro Casas, Michael Seufert, Florian Wamser
    @IEEE CNSM 2018, 14th IEEE International Conference on Network and Service Management.
  1. “Hi-Clust: Unsupervised Analysis of Cloud Latency Measurements through Hierarchical Clustering”
    Pavol Mulinka, Pedro Casas, Lukas Kencl
    @IEEE CloudNet 2018, 7th IEEE International Conference on Cloud Networking.
  1. “RawPower: Deep-Learning-based Anomaly Detection from Raw Network Traffic Measurements”
    Gonzalo Marín, Pedro Casas, Germán Capdehourat
    @SIGCOMM 2018, ACM SIGCOMM 2018 Posters, Demos, and Student Research Competition.
  2. 2nd Place SRC Undergraduate Competition.

  1. “Adaptive Network Security through Stream Machine Learning”
    Pavol Mulinka, Pedro Casas
    @SIGCOMM 2018, ACM SIGCOMM 2018 Posters, Demos, and Student Research Competition.
  1. “Enhancing Machine Learning based QoE Prediction by Ensemble Models”
    Pedro Casas, Michael Seufert, Nikolas Wehner, Anika Schwind, Florian Wamser
    @Internet-QoE 2018, IEEE ICDCS 3rd Workshop on QoE-based Analysis and Management of Data Communication Networks.
  1. “BIGMOMAL – Big Data Analytics for Mobile Malware Detection”
    Sarah Wassermann, Pedro Casas
    @WTMC 2018, ACM SIGCOMM 2018 Workshop on Traffic Measurements for Cybersecurity.
  1. “Stream-based Machine Learning for Network Security and Anomaly Detection”
    Pavol Mulinka, Pedro Casas
    @Big-DAMA 2018, ACM SIGCOMM Workshop on Big Data Analytics and Machine Learning for Data Communication Networks.
  1. “Distributed Internet Paths Performance Analysis through Machine Learning”
    Sarah Wassermann, Pedro Casas
    @TMA Conference 2018 Demos, 2nd Network Traffic Measurement and Analysis Conference.
  1. “LENTA: Longitudinal Exploration for Network Traffic Analysis”
    Andrea Morichetta, Marco Mellia
    @ITC 30, 30th International Teletraffic Congress.
  2. Best Student Paper Award.

  1. “Machine Learning Models for Wireless Network Monitoring and Analysis”
    Pedro Casas
    @WCNC 2018, IEEE International Workshop on Big Data with Computational Intelligence for Wireless Networking.
  1. “Outlier Detection Based on Low Density Models”
    Felix Iglesias Vazquez, Tanja Zseby, Arthur Zimek
    @IEEE ICDM Worshops, 6th International Workshop on Data Science and Big Data Analytics, IEEE International Conference on Data Mining.
  1. “On the Analysis of Network Measurements through Machine Learning: the Power of the Crowd”
    Pedro Casas
    @TMA 2018, Conference on Network Traffic Measurement and Analysis.
  1. “Anycast on the Move: A Look at Mobile Anycast Performance”
    Sarah Wassermann, John P. Rula, Fabián E. Bustamante, Pedro Casas
    @TMA 2018, Conference on Network Traffic Measurement and Analysis.
  1. “MLNET – Machine Learning Models for Network Analytics”
    Pedro Casas
    @DARLI-AP 2018, 2nd International Workshop on Data Analytics Solutions for Real-LIfe APplications.
  1. “Big Data in Computer Network Monitoring”
    Idilio Drago, Marco Mellia, Alessandro D’Alconzo
    @Springer Nature, Encyclopedia of Big Data Technologies, S. Sakr and A. Y. Zomaya editors.

2017

  1. “Analytic Study of Features for the Detection of Covert Timing Channels in Network Traffic”
    Felix Iglesias Vazquez, R. Annessi, Tanja Zseby
    @Journal of Cyber Security and Mobility, vol. 6(3), pp. 225-270, 2017.
  1. “GML Learning, A Generic Machine Learning Model for Network Measurements Analysis”
    Pedro Casas, Juan Vanerio, Kensuke Fukuda
    @CNSM 2017, 13th International Conference on Network and Service Management.
  1. “Network Security and Anomaly Detection with Big-DAMA, a Big Data Analytics Framework”
    Pedro Casas, Francesca Soro, Juan Vanerio, Giuseppe Settanni, Alessandro D’Alconzo
    @CloudNet 2017, 7th IEEE International Conference on Cloud Networking.
  1. “Super Learning for Anomaly Detection in Cellular Networks”
    Pedro Casas, Juan Vanerio
    @WiMob 2017, 13th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications.
  1. “Big-DAMA – a Big Data Analytics Framework for Large-Scale Network Traffic Monitoring and Analysis”
    Francesca Soro, Pedro Casas, Alessandro D’Alconzo
    @N2Women 2017, 6th ACM SIGCOMM Networking Networking Women Workshop.
  1. “Predicting QoE in Cellular Networks using Machine Learning and in-Smartphone Measurements”
    Pedro Casas, Alessandro D’Alconzo, Florian Wamser, Michael Seufert, Bruno Gardlo, Anika Schwind, Phuoc Tran-Gia, Raimund Schatz
    @QoMEX 2017, 9th International Conference on Quality of Multimedia Experience.
  1. “Improving QoE Prediction in Mobile Video through Machine Learning”
    Pedro Casas, Sarah Wassermann
    @NoF 2017, 8th International Conference on Network of the Future.
  1. “BIGMOMAL – Big Data Analytics for Mobile Malware Detection”
    Sarah Wassermann, Pedro Casas
    @IMC 2017, ACM Internet Measurement Conference (poster).
  1. “GML Learning, A Generic Machine Learning Model for Network Measurements Analysis”
    Pedro Casas, Juan Vanerio, Kensuke Fukuda
    @IMC 2017, ACM Internet Measurement Conference (poster).
  1. “Big-DAMA – Big Data Analytics for Large-Scale Network Traffic Monitoring and Analysis”
    Pedro Casas, Francesca Soro, Juan Vanerio, Alessandro D’Alconzo
    @IMC 2017, ACM Internet Measurement Conference (poster).
  1. “Ensemble-learning Approaches for Network Security and Anomaly Detection”
    Juan Vanerio,  Pedro Casas,
    @Big-DAMA 2017, ACM SIGCOMM Workshop on Big Data Analytics and Machine Learning for Data Communication Networks.
  1. “Pattern Discovery in Internet Background Radiation”
    Félix Iglesias Vázquez,  Tanja Zseby,
    @IEEE Transactions on Big Data, 2017, vol. 99, pp. 1-14.
  1. “A Meta-Analysis Approach for Feature Selection in Network Traffic Research”
    Daniel C. Ferreira,  Félix Iglesias Vázquez, Gernot Vormayr, Maximilian Bachl, Tanja Zseby,
    @Reproducibility 2017, ACM SIGCOMM Reproducibility Workshop.
  1. “Decision Tree Rule Induction for Detecting Covert Timing Channels in TCP/IP Traffic”
    Félix Iglesias Vázquez, Robert Annessi, Valentin Bernhard,  Tanja Zseby,
    @CD-MAKE 2017, IFIP Cross Domain Conference for Machine Learning & Knowledge Extraction.
  1. “Are Network Covert Timing Channels Statistical Anomalies?”
    Félix Iglesias Vázquez,  Tanja Zseby,
    @CUING 2017, ARES Workshop on Criminal Use of Information Hiding.
  1. “Users’ Fingerprinting Techniques from TCP Traffic”
    Luca Vassio,  Danilo Giordano,  Martino Trevisan,  Marco Mellia, Ana Paula Couto da Silva,
    @Big-DAMA 2017, ACM SIGCOMM Workshop on Big Data Analytics and Machine Learning for Data Communication Networks.
  1. “NETPerfTrace – Predicting Internet Path Dynamics and Performance with Machine Learning”
    Sarah Wassermann,  Pedro Casas, Thibaut Cuvelier, Benoit Donnet,
    @Big-DAMA 2017, ACM SIGCOMM Workshop on Big Data Analytics and Machine Learning for Data Communication Networks.
  1. “Predicting Internet Path Dynamics and Performance with Machine Learning” [pdf]
    Sarah Wassermann,  Pedro Casas, Thibaut Cuvelier, Benoit Donnet,
    AIT-Big-DAMA Tech. Rep. A3215, 2017.
  1. “Traffic Analysis with Off-the-Shelf Hardware: Challenges and Lessons Learned” [pdf]
    Martino Trevisan, Alessandro Finamore, Marco Mellia, Maurizio Munafò, Dario Rossi,
    @IEEE COMMAG 2017, IEEE Communications Magazine, Network Testing and Analytics Series, vol. 55, no. 3, pp. 163-169, 2017.

2016

  1. “Grasping Popular Applications in Cellular Networks with Big Data Analytics Platforms” [pdf]
    Pierdomenico Fiadino, Pedro Casas, Alessandro D’Alconzo, Mirko Schiavone, Arian Baer
    @TNSM 2016, IEEE Trans. on Network and Service Management, Special Issue on Big Data Analytics for Management, vol. 13, no. 3, pp. 681-695, 2016.
  1. “DBStream: a Holistic Approach to Large-scale Network Traffic Monitoring and Analysis” [pdf]
    Arian Baer, Pedro Casas, Alessandro D’Alconzo, Pierdomenico Fiadino, Lukasz Golab, Marco Mellia, Erich Schikuta
    @Computer Networks 2016, The International Journal of Computer and Telecommunications Networking (Elsevier), Special Issue on Machine Learning, Data Mining and Big Data Frameworks for Network Monitoring and Troubleshooting, vol. 107, part 1, pp. 5-19, 2016.
  1. “Big-DAMA: Big Data Analytics for Network Traffic Monitoring and Analysis” [pdf]
    Pedro Casas, Alessandro D’Alconzo, Tanja Zseby, Marco Mellia
    @LANCOMM 2016, ACM SIGCOMM Workshop on Fostering Latin-American Research in Data Communication Networks
  1. “BGPStream: A Software Framework for Live and Historical BGP Data Analysis” [pdf]
    Chiara Orsini, Alistair King, Danilo Giordano, Vasileios Giotsas, Alberto Dainotti
    @IMC 2016, ACM Internet Measurement Conference
  1. “An Educated Guess on QoE in Operational Networks through Large-Scale Measurements” [pdf]
    Pedro Casas, Bruno Gardlo, Raimund Schatz, Marco Mellia
    @Internet-QoE 2016, ACM SIGCOMM Workshop on QoE-based Analysis and Management of Data Communication Networks
  1. “Detecting and Diagnosing Anomalies in Cellular Networks using Random Neural Networks” [pdf]
    Pedro Casas, Alessandro D’Alconzo, Pierdomenico Fiadino, Christian Callegari
    @TRAC 2016, 7th International Workshop on TRaffic Analysis and Characterization
  1. “Detecting User Actions from HTTP Traces: Toward an Automatic Approach” [pdf]
    Luca Vassio, Idilio Drago, Marco Mellia
    @TRAC 2016, 7th International Workshop on TRaffic Analysis and Characterization
  1. “Towards Web Service Classification using Addresses and DNS” [pdf]
    Martino Trevisan, Idilio Drago, Marco Mellia, Maurizio Munafò
    @TRAC 2016, 7th International Workshop on TRaffic Analysis and Characterization
  1. “Machine-Learning Based Approaches for Anomaly Detection and Classification in Cellular Networks” [pdf]
    Pedro Casas, Pierdomenico Fiadino, Alessandro D’Alconzo
    @TMA 2016, 8th Traffic Monitoring and Analysis Workshop
  1. “CLUE: Clustering for Mining Web URLs” [pdf]
    Andrea Morichetta, Enrico Bocchi, Hassan Metwalley, Marco Mellia
    @ITC 2016, 28th International Teletraffic Congress
  1. “When Smartphones become the Enemy: Unveiling Mobile Apps Anomalies through Clustering Techniques” [pdf]
    Pedro Casas, Pierdomenico Fiadino, Alessandro D’Alconzo
    @ATC 2016, ACM MOBICOM Workshop on All Things Cellular: Operations, Applications and Challenges
  1. “(Semi)-Supervised Machine Learning Approaches for Network Security in High-Dimensional Network Data” [pdf]
    Pedro Casas, Alessandro D’Alconzo, Giuseppe Settanni, Pierdomenico Fiadino, Florian Skopik
    @CCS 2016, ACM Conference on Computer and Communications Security
  1. “Machine Learning based Prediction of Internet Path Dynamics” [pdf]
    Sarah Wassermann, Pedro Casas, Benoit Donnet
    @CoNEXT 2016 Student Workshop, ACM 12th International Conference on emerging Networking EXperiments and Technologies
  1. “WHAT: A Big Data Approach for Accounting of Modern Web Services” [pdf]
    Martino Trevisan, Idilio Drago, Marco Mellia, Han Hee Song, Mario Baldi
    @IEEE BigData 2016, Workshop on Big Data and Machine Learning in Telecom.