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Information Systems Frontiers

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30.08.2022

Deep Reinforcement Learning in the Advanced Cybersecurity Threat Detection and Protection

verfasst von: Mohit Sewak, Sanjay K. Sahay, Hemant Rathore

Erschienen in: Information Systems Frontiers | Ausgabe 2/2023

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Abstract

The cybersecurity threat landscape has lately become overly complex. Threat actors leverage weaknesses in the network and endpoint security in a very coordinated manner to perpetuate sophisticated attacks that could bring down the entire network and many critical hosts in the network. To defend against such attacks, cybersecurity solutions are upgrading from the traditional to advanced deep and machine learning defense mechanisms for threat detection and protection. The application of these techniques has been reviewed well in the scientific literature. Deep Reinforcement Learning has shown great promise in developing AI solutions for areas that had earlier required advanced human cognizance. Different techniques and algorithms under deep reinforcement learning have shown great promise in applications ranging from games to industrial processes, where it is claimed to augment systems with general AI capabilities. These algorithms have recently also been used in cybersecurity, especially in threat detection and protection, where these are showing state-of-the-art results. Unlike supervised machine learning and deep learning, deep reinforcement learning is used in more diverse ways and is empowering many innovative applications in the threat defense landscape. However, there does not exist any comprehensive review of deep reinforcement learning applications in advanced cybersecurity threat detection and protection. Therefore, in this paper, we intend to fill this gap and provide a comprehensive review of the different applications of deep reinforcement learning in this field.

Vorheriger Artikel Towards Adversarially Superior Malware Detection Models: An Adversary Aware Proactive Approach using Adversarial Attacks and Defenses

Nächster Artikel Visualizing Convolutional Neural Network Models’ Sensitivity to Nonnatural Data Order

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Titel: Deep Reinforcement Learning in the Advanced Cybersecurity Threat Detection and Protection
verfasst von: Mohit Sewak
Sanjay K. Sahay
Hemant Rathore
Publikationsdatum: 30.08.2022
Verlag: Springer US
Erschienen in: Information Systems Frontiers / Ausgabe 2/2023
Print ISSN: 1387-3326
Elektronische ISSN: 1572-9419
DOI: https://doi.org/10.1007/s10796-022-10333-x

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