Experts call on AI support for latest cybersecurity battles
AI and the latest security battles.
Deep reinforcement learning or DRL has the potential to provide better cybersecurity by detecting threats earlier. This gives the chance for taking preemptive steps to decline a cyberattack.
Experts say DRL may help artificial intelligence with protection of computer networks early-on, especially if government-sponsored hackers cooperate with more traditional hackers.
The scholars managed to develop a duplication environment, where it is possible to try out multistage attacks with different types of adversaries. It helped to scrutinize the efficacy of different AI defensive models in controlled test settings. DLR was successful at stopping rivals in 95% of cases, particularly sophisticated cyberattacks. Unlike traditional forms of AI, DLR is about defending with orchestration of subsequent decision-making. DLR provides smarter security through earlier changes detection and taking preemptive strikes. It also helps to develop and compare a number of AI-based defensive strategies under controlled conditions.
DLR is a great tool for cybersecurity professionals, because it helps defenders with adaptation, independent decision making under ever-changing conditions,preventing any damage. It offers a proactive way to keep information systems safe. It is a great tool for the environment, requiring a chain of decisions.
The DLR training process is similar to that of human’s: if a child behaves well, they will get a treat, if not - they will get punished. So, the machine learns how to make good decisions all the time and reacts properly to changing situations.
The ultimate purpose is to produce a defense system, that can foresee the next step of a rival, prepare for it and respond in the most efficient way possible. Even though it’s a long way for the system to replace humans in terms of defensive strategies understanding (human feedback is still very much needed), the system is off to a promising start.
AI Catalog's chief editor
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