Deep Reinforcement Learning in the Advanced Cybersecurity Threat Detection and Protection

Mohit Sewak (), Sanjay K. Sahay () and Hemant Rathore ()
Additional contact information
Mohit Sewak: Security & Compliance Research, Microsoft R &D India Pvt. Ltd.
Sanjay K. Sahay: BITS Pilani, Goa Campus
Hemant Rathore: BITS Pilani, Goa Campus

Information Systems Frontiers, 2023, vol. 25, issue 2, No 10, 589-611

Abstract: 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.

Keywords: Deep reinforcement learning; Network IDS; Endpoint detection; Advanced threat protection; IoT defense; 5G jamming (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10796-022-10333-x

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