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Deep Q-Network enhances cloud cyber defense with 99.72% accuracy

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel cybersecurity framework utilizing a Deep Q-Network (DQN) to enhance cloud infrastructure defense against sophisticated cyberattacks. This reinforcement learning-based approach trains adaptive strategies for real-time threat detection and automated mitigation. The DQN model demonstrated superior performance compared to traditional machine learning models like decision trees and random forests, achieving a 99.72% accuracy and a 99.54% attack mitigation rate. AI

IMPACT This research demonstrates the potential of reinforcement learning for autonomous cybersecurity, offering improved threat detection and mitigation capabilities in cloud environments.

RANK_REASON The cluster describes a research paper detailing a new machine learning architecture for cybersecurity.

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AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Deep Q-Network enhances cloud cyber defense with 99.72% accuracy

COVERAGE [2]

  1. 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 ·

    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…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

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

    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 dynamic cyber defense framework. We deploy a Deep…

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