AI-Powered Cybersecurity Framework for Threat Detection and Prevention
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Abstract
The rise in sophisticated cyber threats necessitates advanced security mechanisms. This paper proposes an AI-driven cybersecurity framework that leverages machine learning and deep learning techniques to detect and prevent cyberattacks in real time. The system analyzes network traffic patterns and identifies anomalies using unsupervised learning models. Experimental results show high detection accuracy and reduced response time. The framework provides a scalable and adaptive solution for modern cybersecurity challenges.
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References
Asopa, P., Purohit, P., Nadikattu, R. R., & Whig, P. (2021). Reducing carbon footprint for sustainable development of smart cities using IoT. In Proceedings of the Third International Conference on Intelligent Communication Technologies.
Whig, P., Velu, A., & Nadikattu, R. R. (2022). The economic impact of AI-enabled blockchain in 6G-based industry. In AI and blockchain technology in 6G wireless network (pp. 205–224).
Whig, P., Velu, A., & Nadikattu, R. R. (2022). Blockchain platform to resolve security issues in IoT and smart networks. In AI-enabled agile internet of things for sustainable FinTech ecosystems (pp. 46–65).
Vaddadi, S. A., Vallabhaneni, R., & Whig, P. (2023). Utilizing AI and machine learning in cybersecurity for sustainable development through enhanced threat detection and mitigation. International Journal of Sustainable Development Through AI, ML and IoT, 2(2).