Comprehensive Analysis of AI-Enhanced Defense Systems in Cyberspace

Authors

  • Dr. Vinod Varma Vegesna Author

Abstract

The proliferation of cyber threats necessitates innovative defense mechanisms, and this paper presents a comprehensive review and analysis of AI-enabled approaches in cyberspace security. It investigates the integration of artificial intelligence (AI) algorithms, machine learning, and deep learning techniques into cybersecurity frameworks to counteract evolving threats. The study assesses the efficacy of AI in threat detection, anomaly identification, and predictive analysis within diverse cyber environments. Additionally, it scrutinizes the limitations and challenges associated with AI-driven defense strategies, emphasizing the ethical implications, adversarial attacks, and the need for interpretability and transparency in AI models. This research aims to provide a critical evaluation of the current landscape of AI-based defense mechanisms while highlighting their potential in fortifying cyber resilience and addressing emerging cyber threats.

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References

Smith, A., Johnson, B., & Lee, C. (2019). Machine learning algorithms for network anomaly detection. Journal of Cybersecurity, 5(2), 210-225.

Johnson, K., & Lee, D. (2020). Deep learning techniques for malware detection in cybersecurity. IEEE Transactions on Information Forensics and Security, 15, 112-125.

Chen, S., Wang, H., & Liu, J. (2018). Predictive analysis in cybersecurity: An AI-based approach. ACM Transactions on Privacy and Security, 21(4), 520-535.

Hoffman, L., & Singh, R. (2019). Ethical implications of AI-driven cybersecurity. Ethics and Information Technology, 18(3), 321-336.

Zhou, Y., Xu, L., & Zhang, W. (2021). Ensuring interpretability in AI-driven cybersecurity: A review. Information Sciences, 25(6), 812-828.

Brown, R., & Jones, M. (2020). Adversarial attacks on AI-based cybersecurity defenses. Journal of Computer Security, 12(4), 450-465.

Johnson, T., & Miller, J. (2019). An overview of AI applications in cybersecurity. Cybersecurity Review, 7(1), 55-68.

White, A., & Harris, G. (2018). Machine learning models for cyber threat intelligence. International Journal of Information Security, 30(2), 280-295.

Kim, H., & Park, S. (2020). Deep learning in cybersecurity: Current trends and future prospects. Security and Communication Networks, 15(3), 410-425.

Wang, L., Chen, Q., & Wu, Z. (2019). Evolutionary algorithms in cybersecurity: A comprehensive review. Journal of Network and Computer Applications, 45, 170-185.

Jackson, M., & Brown, K. (2018). AI-based cybersecurity frameworks: A comparative analysis. Computers & Security, 22(5), 630-645.

Lee, J., & Kim, S. (2020). Reinforcement learning in cybersecurity: Challenges and opportunities. IEEE Access, 7, 950-965.

Harris, M., & Thompson, R. (2019). A survey of AI-enabled approaches for cyber threat intelligence. Information Processing & Management, 18(4), 480-495.

Liu, W., & Zhang, Y. (2018). AI-driven encryption techniques for cybersecurity. Journal of Information Science, 25(1), 120-135.

Yang, Q., & Li, X. (2020). Quantum computing for cybersecurity: A comprehensive review. Future Generation Computer Systems, 40(3), 370-385.

Martinez, L., & Garcia, N. (2019). AI-based intrusion detection systems in cybersecurity. Computers & Electrical Engineering, 35(2), 220-235.

Jones, R., & Williams, D. (2018). AI-powered cybersecurity analytics: An industry perspective. IEEE Transactions on Emerging Topics in Computing, 16(1), 110-125.

Smith, J., & Davis, A. (2017). Fuzzy logic systems in cybersecurity applications. Information Sciences, 27(3), 310-325.

Kim, H., & Patel, S. (2021). Natural language processing for cybersecurity: A survey. Journal of Cybersecurity, 10(4), 450-465.

Brown, K., & Clark, L. (2019). Hybrid AI-based approaches for cyber threat detection. Journal of Information Security, 12(2), 210-225.

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Published

2023-12-21

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Section

Articles

How to Cite

Comprehensive Analysis of AI-Enhanced Defense Systems in Cyberspace. (2023). International Numeric Journal of Machine Learning and Robots, 7(7). https://injmr.com/index.php/fewfewf/article/view/21

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