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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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Volume 9 | Issue 5
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| Paper Title: | Optimizing Honeypot Deployment in Ultra-Dense Beyond 5G Networks Using Deep Q-Networks: A Novel Reinforcement Learning Strategy |
| Authors Name: | Vijaya S Rao , Kumaraswamy S |
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| Author Reg. ID: |
IJNRD_214526
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| Published Paper Id: | IJNRD2404060 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| 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. |
| Keywords: | Honeypot, Intrusion Detection, Deep Q-Network, Reinforcement Learning, Beyond 5G Networks, Cybersecurity. |
| Cite Article: | "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 (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.a463-a472, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404060.pdf |
| Downloads: | 00038 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2404060 Registration ID: 214526 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: a463-a472 Country: Bangalore, Karnataka, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404060 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404060 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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