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

Authors: Georgios Petihakis; Aristeidis Farao; Panagiotis Bountakas; Athanasia Sabazioti; John Polley; Christos Xenakis;

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

Abstract

The increasing integration of Artificial Intelligence (AI) in critical sectors such as healthcare, finance, and cybersecurity has simultaneously exposed these systems to unique vulnerabilities and cyber threats. This paper discusses the escalating risks associated with adversarial AI and outlines the development of AIAS. AIAS is a comprehensive, AI-driven security platform designed to enhance the resilience of AI systems against such threats. In addition, AIAS features advanced modules for threat simulation, detection, mitigation, and deception, using adversarial defense techniques, attack detection mechanisms, and sophisticated honeypots. The platform leverages explainable AI (XAI) to improve the transparency and effectiveness of threat countermeasures. Through meticulous analysis and innovative methodologies, AIAS aims to revolutionize cybersecurity defenses, enhancing the robustness of AI systems against adversarial attacks while fostering a safer deployment of AI technologies in critical applications. The paper details the components of the AIAS platform, explores its operational framework, and discusses future research directions for advancing AI security measures.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Top 10%
Average
Top 10%
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