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Author

P. Paul

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Open access Jul 2026

ELEVATING PHISHING DETECTION PERFORMANCE WITH MACHINE LEARNING AND DEEP LEARNING-ENABLED FEATURE SELECTION

An intelligent phishing detection framework that integrates machine learning, deep learning-enabled feature selection, multi-source phishing feature extraction, hybrid classification, and real-time risk assessment is proposed that can achieve higher detection accuracy, precision, recall, F1-score, and lower response latency than blacklist-based, conventional machine learning, and standalone deep learning approaches.

P. Paul, Bharath Bhushan, Bandameedi Sai Charan et al. · 0 citations
Open access Jun 2026

CYBER THREAT FORECASTING: THE TRANSITION FROM TRADITIONAL ML TO GENERATIVE AI APPROACHES

This paper presents a comprehensive framework for cyber threat forecasting by integrating traditional machine learning techniques with advanced Generative AI models and demonstrates that Generative AI significantly improves cyber threat prediction accuracy, reduces false-positive rates, enhances adaptive learning, and supports real-time security decision-making.

P. Paul, Bharath Bhushan, Bathini Revanth et al. · 0 citations