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This study aims to develop an Automated Incident Response System (AIRS) for real-time detection and mitigation of cybersecurity threats. The system employs Random Forest for anomaly detection using live network traffic data. SMOTE is applied to address class imbalance, and Optuna optimizes model parameters for enhanced accuracy. A web-based dashboard provides real-time visualization of security incidents. Performance evaluation on the UNSW-NB15 dataset demonstrates high accuracy (94.7%) and reduced false positives, ensuring robust cyber defense.

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Automated Incident Response System for Cybersecurity Threat Mitigation

  • 会议论文

  • 发表日期：

    2026年

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  • Akshat Sharma,
  • Alka Chaudhary

摘要

This study aims to develop an Automated Incident Response System (AIRS) for real-time detection and mitigation of cybersecurity threats. The system employs Random Forest for anomaly detection using live network traffic data. SMOTE is applied to address class imbalance, and Optuna optimizes model parameters for enhanced accuracy. A web-based dashboard provides real-time visualization of security incidents. Performance evaluation on the UNSW-NB15 dataset demonstrates high accuracy (94.7%) and reduced false positives, ensuring robust cyber defense.