Abstract
In the race to simplify man-machine interactions and maintenance processes, hardware is increasingly interconnected. With more connected devices than ever, in our homes and workplaces, the attack surface is increasing tremendously. To detect this growing flow of cyber-attacks, machine learning based intrusion detection systems are being deployed at an unprecedented pace. In turn, these require a constant feed of data to learn and differentiate normal traffic from abnormal traffic. Unfortunately, there is a lack of learning datasets available. In this paper, we present a software platform generating fully labelled datasets for data analysis and anomaly detection.
Original language | English |
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Title of host publication | Foundations and practice of security |
Subtitle of host publication | 12th International Symposium, FPS 2019, Toulouse, France, November 5–7, 2019, Revised Selected Papers |
Editors | Abdelmalek Benzekri, Michel Barbeau, Guang Gong, Romain Laborde, Joaquin Garcia-Alfaro |
Place of Publication | Cham |
Publisher | Springer |
Pages | 98-113 |
Number of pages | 16 |
ISBN (Electronic) | 9783030453718 |
ISBN (Print) | 9783030453701 |
DOIs | |
Publication status | Published - 17 Apr 2020 |
Event | 12th International Symposium on Foundations and Practice of Security - Crowne Plaza, Toulouse, France Duration: 5 Nov 2019 → 7 Nov 2019 Conference number: 12th https://fps2019.sciencesconf.org/ |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 12056 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Name | LNCS Sublibrary: SL4 – Security and Cryptology |
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Publisher | Springer |
Conference
Conference | 12th International Symposium on Foundations and Practice of Security |
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Abbreviated title | FPS 2019 |
Country | France |
City | Toulouse |
Period | 5/11/19 → 7/11/19 |
Internet address |