SOURCE-LINKED INTELLIGENCE

Machine Learning for Offensive Computer Security

CORDIS · observation · Publication date unknown

Machine Learning for Offensive Computer Security Despite a long series of research, computer attacks still pose a major threat to the security of digital systems. Different malicious actors, such as cybercriminals and intelligence agencies, continuously develop new offensive techniques to evade and outsmart existing defenses. As a result, security research is in a constant arms race and needs to anticipate novel developments as early as possible. However, one of the key technologies of the last years, machine learning, has received very little attention in offensive security so far. The simple question — ''how would a hacker use machine learning?'' — is largely unexplored and there is a striking gap in current research that hinders the anticipation of forthcoming threats. The project Malfoy closes this gap and systematically explores how machine learning can be applied for offensive c

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recordType

award

status

SIGNED

region

EU

value

1962000

unit

EUR