Deep Q-Network stops attacks
TECHNOLOGY
Deep Q-Network stops attacks
The team trained defensive agents based on four deep reinforcement learning algorithms : DQN ( Deep Q-Network ) and three variations of what ’ s known as the actor-critic approach .
The agents were trained with simulated data about cyberattacks and then tested against attacks that they had not observed in training .
In the least sophisticated attacks ( based on varying levels of adversary skill and persistence ), DQN stopped 79 % of attacks midway through the attack stages and 93 % by the final stage .
For moderately sophisticated attacks , DQN stopped 82 % of attacks midway and 95 % by the final stage .
In the most sophisticated attacks , DQN stopped 57 % of attacks midway and 84 % by the final stage — far higher than the other three algorithms .
adapt , and make autonomous decisions in the face of rapidly changing circumstances . By orchestrating sequential decisionmaking plans , defenders can quickly respond to cyberattacks and prevent them from doing any damage .
One of the key benefits of DRL is its ability to detect changes in the cyber landscape early , allowing defenders to take preemptive steps to stop cyberattacks before they happen . With the threat of cyberattacks only set to increase , DRL offers a smarter and more proactive way to keep our computer networks safe .
The research findings were documented in a research paper and presented at a workshop on AI for Cybersecurity during the annual meeting of the Association for the Advancement of Artificial Intelligence in Washington , D . C . The development of DRL for cybersecurity defence represents an exciting step forward in the battle against cyber threats . As technology advances , researchers will undoubtedly discover new and innovative ways to harness AI for cybersecurity , ensuring that our systems remain safe and secure .
104 April 2023