Reinforcement Learning-Based Framework for Cybersecurity in Online Examination Systems

Abstract

As cyber security threats are becoming more and more complex, sophisticated and persistent, the methods of tradi-tional defence mechanism are becoming obsolete and are struggling hard in keeping pace cost-effectively to preventing the black swan [1] threats of the todays cyberworld, that are poised with the advent of Artificial Intelligence, hence need is being felt for the adaptive capabilities of the cybersecurity tools along with the dynamic decision-making abilities to mitigate the ever evolving cybersecurity risks associated not only in the cyberworld but also with the online examination systems being used today in every step of the learning process as well as the shortlisting process of any institute/organisation. Machine Learning (ML) must be used to counter the attacks on the system today, as the same are increasing exponentially day by day. For this, Reinforcement Learning (RL), which is a powerful subset of ML, must be effectively used to create strategies in dy-namic environments with varied hardware and software in different scenarios to effectively detect and control the ever-evolving cybersecurity risks and threats while effectively addressing the fatigue in manual processes and providing real-time decision-making for the stakeholders. In this paper, we are investigating extensively the application of RL techniques to pro-vide adaptive decision-making processes by enabling systems with proactive decision-making in real-time changing threat scenarios. This paper evaluates the present state-of-the-art RL-based methods, while highlighting their strengths and weak-nesses, and it also identifies the areas that require further improvements to propose a novel RL-based cybersecurity frame-work tailored to the specific demands of online examination platforms.