AI-Based Cybersecurity for Threat Detection in Real-Time Networks

Sai Teja Boppiniti

Abstract


The growing complexity of cyberattacks demands advanced solutions for real-time threat detection. This paper introduces an AI-based cybersecurity framework that utilizes machine learning models for anomaly detection and intrusion prevention in network systems. By analyzing network traffic patterns and identifying deviations, the system provides proactive defense mechanisms against emerging threats. Experiments on benchmark cybersecurity datasets demonstrate the system's high detection accuracy and low false-positive rates. This study underscores the importance of AI in strengthening cybersecurity defenses and mitigating risks in critical infrastructure.

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