Advancements in AI Frameworks for Cyber Threat Detection

• •XGBoost and LightGBM are top-performing models for cyber threat detection.
• •Both frameworks utilize SHAP analysis for improved model interpretability.
• •Addressing class imbalance is crucial for effective cybersecurity risk assessment.

Recent studies have introduced advanced machine learning frameworks aimed at enhancing cybersecurity risk assessment. Article 1 discusses an explainable AI framework utilizing SHAP analysis for improved transparency in threat detection, achieving notable performance with XGBoost on the CIC-IDS2017 dataset. Article 2 presents an optimized machine learning framework that addresses class imbalance and interpretability issues, reporting LightGBM as the top performer on a synthetic dataset mimicking CIC-IDS2017 characteristics. Both frameworks emphasize the importance of feature selection and risk scoring mechanisms to enhance decision-making in cybersecurity. The studies highlight the growing need for effective tools to combat increasingly sophisticated cyber threats. These frameworks aim to provide actionable insights for network security professionals, potentially improving response times and threat mitigation strategies.