Authors - SunilKumar Ketineni, Preethi Kandukuri, Hruthik Sreeramaneni, Vivek Bojjagani Abstract - Phishing continues to pose a serious threat to digital security by ex ploiting human vulnerabilities to steal confidential data through deceptive online interactions. Traditional detection methods often fall short in identifying advanced phishing strategies. This survey presents a comprehensive overview of phishing detection techniques, with a strong focus on modern, multi-layered machine learning and deep learning-based solutions. The proposed layered framework includes four key stages: data collection and preparation, model training, detection and prediction, and explainability. In the first layer, email, URL, and metadata are collected and preprocessed for feature extraction. The second layer involves model training using both machine learning classifiers such as Random Forest, SVM, Naïve Bayes, and KNN and deep learning archi tectures like CNN, RNN, and LSTM. These models feed into the third layer where phishing is detected and classified. Finally, the fourth layer integrates Explainable AI (XAI) methods like LIME, SHAP, and Anchors to enhance model transparency and interpretability. This survey evaluates the effectiveness and limitations of each layer and highlights the need for explainable, scalable, and adaptive phishing detection systems.
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