Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Other ORP type . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Other ORP type . 2026
License: CC BY
Data sources: Datacite
ZENODO
Other ORP type . 2026
License: CC BY
Data sources: Datacite
versions View all 2 versions
addClaim

COcyber Success Stories - DeepHack

Authors: COcyber;

COcyber Success Stories - DeepHack

Abstract

With phishing attacks becoming more sophisticated, leveraging AI for cybersecurity defence has become essential. Traditional rule-based detection methods are no longer sufficient. Instead, AI-driven solutions that understand phishing tactics, identify deception patterns, and adapt to new threats are necessary to protect individuals and organisations. From April 24 to 26, 2025, COcyber’s AI Cybersecurity Deephack brought together cyber enthusiasts from academia, research and the start-up scene for an intense 48-hour challenge to answer one critical question: can artificial intelligence stop the next big phishing attack? During the DeepHack, participants used natural language processing (NLP), anomaly detection, and existing phishing threat intelligence sources to develop AI-driven systems able to analyse linguistic anomalies, behavioural patterns, and suspicious URLs in emails and websites to detect, identify and prevent phishing attacks in real-time. The challenge required participants to focus on dual-use cybersecurity technologies to ensure that the final solutions could target both civilian and defence cybersecurity applications, while addressing practical deployment issues such as multilingual attacks, AI bias, infrastructure constraints, and adversarial threats.

Keywords

Cybersecurity, Phishing threat, dual-use technologies, aritificial intelligence, NLP

  • BIP!
    Impact byBIP!
    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).
    0
    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.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
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!
0
Average
Average
Average