The aforementioned findings were made by researchers from the Department of Energy’s Pacific Northwest National Laboratory based on an abstract simulation of the digital conflict between threat actors and defenders in a network and trained four different DRL neural networks in order to expand rewards based on minimizing compromises and network disruption.
The simulated attackers transitions from the initial access and reconnaissance phase to other attack stages until they arrived at their objective, i.e. the impact and exfiltration phase. Apparently, these strategies were based on the classification of the MITRE ATT&CK architecture.
Samrat Chatterjee, a data scientist who presented the team’s work at the annual meeting of the Association for the Advancement of Artificial Intelligence in Washington, DC, on February 14, claims that the successful installation and training of the AI system on the simplified attack surfaces illustrates the defensive responses to cyberattacks that, in current times, could be conducted by an AI model.
“You don’t want to move into more complex architectures if you cannot even show the promise of these techniques[…]We wanted to first demonstrate that we can actually train a DRL successfully and show some good testing outcomes before moving forward,” says Chatterjee.&
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