J
Jerome Francois
Researcher at French Institute for Research in Computer Science and Automation
Publications - 87
Citations - 1585
Jerome Francois is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: The Internet & Network security. The author has an hindex of 19, co-authored 79 publications receiving 1340 citations. Previous affiliations of Jerome Francois include University of Lorraine & University of Waterloo.
Papers
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Journal ArticleDOI
FireCol: a collaborative protection network for the detection of flooding DDoS attacks
TL;DR: The evaluation of FireCol using extensive simulations and a real dataset is presented, showing FireCol effectiveness and low overhead, as well as its support for incremental deployment in real networks.
Journal ArticleDOI
PhishStorm: Detecting Phishing With Streaming Analytics
TL;DR: PhishStorm, an automated phishing detection system that can analyze in real time any URL in order to identify potential phishing sites, is introduced and the new concept of intra-URL relatedness is defined and evaluated.
Book ChapterDOI
BotTrack: tracking botnets using NetFlow and PageRank
TL;DR: A novel approach is proposed for detecting stealthy botnets using peer-to-peer communication infrastructures and not exhibiting large volumes of traffic, where NetFlow related data is correlated and a host dependency model is leveraged for advanced data mining purposes.
Book ChapterDOI
Machine learning approach for IP-flow record anomaly detection
TL;DR: This paper presents an approach that leverages support vector machines in order to analyze large volumes of Netflow records using a special kernel function, that takes into account both the contextual and the quantitative information of Net flow records.
Proceedings ArticleDOI
BotCloud: Detecting botnets using MapReduce
TL;DR: This paper proposes a distributed computing framework that leverages a host dependency model and an adapted PageRank algorithm and reports experimental results from an open-source based Hadoop cluster and highlights the performance benefits when using real network traces from an Internet operator.