Reinforcement Learning for Adaptive Cyber Defense: A Dynamic Approach to Threat Mitigation

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Prof. Daniel Shah

Abstract

In the rapidly evolving landscape of cyber threats, static defense mechanisms are often insufficient. This paper introduces a reinforcement learning-based framework for adaptive cyber defense that dynamically adjusts security measures based on the evolving threat landscape. By modeling cybersecurity as a decision-making process, the framework learns from past incidents and continuously optimizes responses to emerging threats. Experimental results demonstrate that this adaptive approach significantly improves the efficacy of threat mitigation strategies, reducing the impact of cyberattacks and enhancing overall security posture.


 

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How to Cite
Reinforcement Learning for Adaptive Cyber Defense: A Dynamic Approach to Threat Mitigation. (2024). International Meridian Journal, 6(6). https://meridianjournal.in/index.php/IMJ/article/view/82
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Articles

How to Cite

Reinforcement Learning for Adaptive Cyber Defense: A Dynamic Approach to Threat Mitigation. (2024). International Meridian Journal, 6(6). https://meridianjournal.in/index.php/IMJ/article/view/82

References

Balantrapu, S. S. (2024). Current Trends and Future Directions Exploring Machine Learning Techniques for Cyber Threat Detection. International Journal of Sustainable Development Through AI, ML and IoT, 3(2), 1-15.

Balantrapu, S. S. (2023). Evaluating the Effectiveness of Machine Learning in Phishing Detection. International Scientific Journal for Research, 5(5).

Balantrapu, S. S. (2023). Future Trends in AI and Machine Learning for Cybersecurity. International Journal of Creative Research In Computer Technology and Design, 5(5).

Balantrapu, S. S. (2024). A Comprehensive Review of AI Applications in Cybersecurity. International Machine learning journal and Computer Engineering, 7(7).

Balantrapu, S. (2023). Cybersecurity Frameworks Enhanced by Machine Learning Techniques. International Journal of Sustainable Development in Computing Science, 5(4), 1-19. Retrieved from https://www.ijsdcs.com/index.php/ijsdcs/article/view/584

Balantrapu, S. S. (2021). A Systematic Review Comparative Analysis of Machine Learning Algorithms for Malware Classification. International Scientific Journal for Research, 3(3), 1-29.

Balantrapu, S. S. (2020). AI-Driven Cybersecurity Solutions: Case Studies and Applications. International Journal of Creative Research In Computer Technology and Design, 2(2).

Balantrapu, S. S. (2024). AI for Predictive Cyber Threat Intelligence. International Journal of Management Education for Sustainable Development, 7(7), 1-28.

Balantrapu, S. S. (2022). Evaluating AI-Enhanced Cybersecurity Solutions Versus Traditional Methods: A Comparative Study. International Journal of Sustainable Development Through AI, ML and IoT, 1(1), 1-15.

Balantrapu, S. S. (2022). Ethical Considerations in AI-Powered Cybersecurity. International Machine learning journal and Computer Engineering, 5(5).

Balantrapu, S. S. (2021). The Impact of Machine Learning on Incident Response Strategies. International Journal of Management Education for Sustainable Development, 4(4), 1-17.

Balantrapu, S. S. (2019). Adversarial Machine Learning: Security Threats and Mitigations. International Journal of Sustainable Development in Computing Science, 1(3), 1-18.

Pillai, S. E. V. S., Polimetla, K., Avacharmal, R., Perumal, A. P., & Gopal, S. K. (2023). Beyond the Bin: Machine Learning-Driven Waste Management for a Sustainable Future. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING (JRTCSE), 11(1), 16-27.

Deekshith, A. (2022). Cross-Disciplinary Approaches: The Role of Data Science in Developing AI-Driven Solutions for Business Intelligence. International Machine learning journal and Computer Engineering, 5(5).

Deekshith, A. (2021). Data Engineering for AI: Optimizing Data Quality and Accessibility for Machine Learning Models. International Journal of Management Education for Sustainable Development, 4(4), 1-33.

Deekshith, A. (2023). Scalable Machine Learning: Techniques for Managing Data Volume and Velocity in AI Applications. International Scientific Journal for Research, 5(5).

Deekshith, A. (2020). AI-Enhanced Data Science: Techniques for Improved Data Visualization and Interpretation. International Journal of Creative Research In Computer Technology and Design, 2(2).

Deekshith, A. (2019). Integrating AI and Data Engineering: Building Robust Pipelines for Real-Time Data Analytics. International Journal of Sustainable Development in Computing Science, 1(3), 1-35.

Boppiniti, S. T. (2022). Exploring the Synergy of AI, ML, and Data Analytics in Enhancing Customer Experience and Personalization. International Machine learning journal and Computer Engineering, 5(5).

Boppiniti, S. T. (2023). Data Ethics in AI: Addressing Challenges in Machine Learning and Data Governance for Responsible Data Science. International Scientific Journal for Research, 5(5).

Boppiniti, S. T. (2020). Big Data Meets Machine Learning: Strategies for Efficient Data Processing and Analysis in Large Datasets. International Journal of Creative Research In Computer Technology and Design, 2(2).

Boppiniti, S. T. (2021). Real-Time Data Analytics with AI: Leveraging Stream Processing for Dynamic Decision Support. International Journal of Management Education for Sustainable Development, 4(4).

Boppiniti, S. T. (2019). Machine Learning for Predictive Analytics: Enhancing Data-Driven Decision-Making Across Industries. International Journal of Sustainable Development in Computing Science, 1(3).

Pillai, S. E. V. S., Polimetla, K., Avacharmal, R., & Perumal, A. P. (2022). Mental health in the tech industry: Insights from surveys and NLP analysis. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING (JRTCSE), 10(2), 22-33.

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