AI-Driven Security Operation Center
S R Namrutha · International Journal for Research in Applied Science and Engineering Technology · 2025
Cyber threats such as malware, phishing, and DDoS attacks are becoming increasingly sophisticated, necessitating advanced detection mechanisms. This paper presents an AI-driven cybersecurity system that integrates machine learning models for real-time detection of cyber threats, network intrusions, phishing URLs, and email phishing. The system employs NLP for malware analysis, anomaly detection for intrusion detection, and classification models for phishing prevention. Developed using FastAPI for real-time inference and SQLite for secure logging, the system ensures efficient threat identification and response. Security measures such as SQL injection protection, API authentication, and data encryption further enhance its robustness. Experimental results show high detection accuracy, with intrusion detection at 96% and email phishing detection at 97%.