AIAS: AI-ASsisted cybersecurity platform to defend against adversarial AI attacks-Reference-Cited by-同舟云学术

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AIAS: AI-ASsisted cybersecurity platform to defend against adversarial AI attacks

Published:2024-07-30 Issue: Volume: Page:1-7
ISSN:
Container-title:Proceedings of the 19th International Conference on Availability, Reliability and Security
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Author:

Petihakis Georgios¹^ORCID,Farao Aristeidis¹^ORCID,Bountakas Panagiotis²^ORCID,Sabazioti Athanasia³^ORCID,Polley John⁴^ORCID,Xenakis Christos¹^ORCID

Affiliation:

1. Department of Digital Systems, University of Piraeus, Greece

2. Department of Digital Systems, University of Piraeus, Greece and SPHYNX Technology Solutions AG, Switzerland

3. Department of Tourism Studies, University of Piraeus, Greece

4. School of Communication, University of Southern California, USA

Funder

European Commission

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3664476.3669920

Reference32 articles.

1. EKnad: Exploit Kits’ network activity detection

2. HELPHED: Hybrid Ensemble Learning PHishing Email Detection

3. Defense strategies for Adversarial Machine Learning: A survey

4. De-Pois: An Attack-Agnostic Defense against Data Poisoning Attacks

5. Lingwei Chen, Yanfang Ye, and Thirimachos Bourlai. 2017. Adversarial machine learning in malware detection: Arms race between evasion attack and defense. In 2017 European intelligence and security informatics conference (EISIC). IEEE, 99–106.

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