AI-AUGMENTED THREAT RESPONSE SYSTEMS WITH REAL-TIME ADAPTIVE DEFENSE
Keywords:
AI-Augmented Threat Response, Real-Time Adaptive Defense, Cybersecurity, Deep Learning, Reinforcement LearningAbstract
As cyber threats become increasingly sophisticated and pervasive, the necessity for advanced defense mechanisms has never been more critical. This research introduces a novel AI-augmented threat response system that integrates real-time adaptive defense strategies to enhance cybersecurity resilience. Leveraging deep learning algorithms and reinforcement learning techniques, the proposed system dynamically identifies, assesses, and mitigates emerging threats with minimal human intervention. Unlike traditional static defense mechanisms, this adaptive approach continuously evolves by learning from ongoing attack patterns and environmental changes, thereby significantly reducing response times and improving accuracy in threat neutralization. Through extensive simulations and real-world scenario testing, the system demonstrates superior performance in threat detection rates, adaptability, and operational efficiency compared to existing solutions. This study not only advances the theoretical framework of AI-driven cybersecurity but also provides practical insights for the deployment of AI-augmented threat response systems, real-time intelligent defense mechanisms, and proactive security strategies in complex and dynamic network environments. Key topics include adaptive defense, cybersecurity, deep learning, and reinforcement learning.
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Copyright (c) 2023 Rajashekhar Reddy Kethireddy (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
