Deep Q-Network Architecture Enhances Cloud Cybersecurity

By PulseAugur Editorial · [1 sources] · 2026-08-13 04:00

Researchers have developed a novel adaptive Deep Q-Network (DQN) architecture for enhanced cybersecurity in cloud environments. This reinforcement learning-based framework aims to provide real-time intrusion detection and automated threat mitigation. The DQN model demonstrated superior performance compared to traditional machine learning algorithms like decision trees and support vector machines, achieving a 99.72% accuracy and a 99.54% attack mitigation rate. AI

IMPACT This research could lead to more robust and automated cybersecurity solutions for cloud infrastructure.

RANK_REASON The cluster contains a research paper detailing a new machine learning architecture for cybersecurity. [lever_c_demoted from research: ic=1 ai=1.0]

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Deep Q-Network Architecture Enhances Cloud Cybersecurity

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arXiv cs.AI TIER_1 English(EN) · Md Yassir Mottalib, Md Yousuf, Eklachur Rahman Bhuiyan, S M Ahsan Habib, Sonjoy Kumar Dey, Md. Salahuddin Gazi, Molay Kumar Roy, Asaduzzaman Anik · 2026-08-13 04:00

Machine Learning-Based Cyber Defense for Cloud Infrastructure: An Adaptive Deep Q-Network Architecture for Intelligent Intrusion Detection and Automated Threat Mitigation

arXiv:2608.12190v1 Announce Type: cross Abstract: With the increasing complexity of cyber assaults in cloud environments, adaptable security solutions are needed that can support real-time detection and autonomous response. In this paper, we propose a reinforcement learning-based…

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Machine Learning-Based Cyber Defense for Cloud Infrastructure: An Adaptive Deep Q-Network Architecture for Intelligent Intrusion Detection and Automated Threat Mitigation

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