Applying the rainbow architecture to intrusion detection systems
Document typeMaster thesis
Rights accessOpen Access
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There is a lot of expectation on how Artificial Intelligence (AI) is going to have an impact on Cybersecurity. From new sophisticated attacks to new ways of defending a system from cybercriminals. A lot of techniques are being studied by universities and security organizations in order to implement intelligent systems to defend systems, as well as researching how can AI give solution to many problems not yet solved. However this sector has some limitations for AI to have a real impact. AI is a discipline which, in its machine learning part, needs data in order to give good results and big companies are not willing to admit there attacks or do not even know they are under attack. In particular network intrusion is one of the most common ways attackers enter a system and exploit its threats. With the adoption of IoT systems in our society more and more, companies, cities, and citizens, are going to fill their surroundings with devices that communicate to the exterior. Under these emerging threats every system and network they are connected to have to be secure and should be able to detect intrusion. This thesis will try to present a conjunction of different techniques, presented by their authors as the Rainbow architecture, used in reinforcement learning in order to tackle this problem. It will give solution to systems such as intrusion detection systems (IDS) and can be extended to intrusion prevention systems (IPS) or web application firewalls (WAF).
SubjectsComputer security, Deep learning, Reinforcement learning, Seguretat informàtica, Aprenentatge profund, Aprenentatge per reforç
DegreeMÀSTER UNIVERSITARI EN INNOVACIÓ I RECERCA EN INFORMÀTICA (Pla 2012)