Paper Title
Optimizing Honeypot Deployment in Ultra-Dense Beyond 5G Networks Using Deep Q-Networks: A Novel Reinforcement Learning Strategy
Article Identifiers
: Authors
Keywords
Honeypot, Intrusion Detection, Deep Q-Network, Reinforcement Learning, Beyond 5G Networks, Cybersecurity.
Abstract
In the landscape of Beyond 5G networks, the fusion of Software Defined Networking (SDN) and virtualization heralds a new phase of digital connectivity, albeit with heightened security vulnerabilities. This research introduces an innovative security strategy utilizing Deep Q-Networks (DQN) for the deployment of honeypots, sophisticated decoy systems designed to entrap cyberattackers, thereby safeguarding genuine network assets. Diverging from traditional reinforcement learning techniques, our approach harnesses the advanced capabilities of DQN to navigate the complex, dynamic environment of ultra-dense networks more efficiently. We propose a DQN-based framework that not only overcomes the limitations of data dependency inherent in machine and deep learning models but also dynamically adapts to evolving cyber threats, ensuring robust network security. Through extensive simulations, we demonstrate the enhanced performance of our method in optimizing honeypot deployment, marking a significant step forward in the proactive defense mechanisms for next-generation networks.
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How To Cite (APA)
Vijaya S Rao & Kumaraswamy S (April-2024). Optimizing Honeypot Deployment in Ultra-Dense Beyond 5G Networks Using Deep Q-Networks: A Novel Reinforcement Learning Strategy. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), a463-a472. https://ijnrd.org/papers/IJNRD2404060.pdf
Issue
Volume 9 Issue 4, April-2024
Pages : a463-a472
Other Publication Details
Paper Reg. ID: IJNRD_214526
Published Paper Id: IJNRD2404060
Downloads: 000122011
Research Area: Engineering
Author Type: Indian Author
Country: Bangalore, Karnataka, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2404060.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404060
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