Enhanced Phishing Website Detection Using Bi-LSTM with Attention Mechanism for Robust Cybersecurity

Phishing attacks are among the most rampant cyber threats, luring victims onto fraudulent websites. Existing rule-based or white-box statistical models and other traditional phishing detection methods are far from ideal. These methods are highly susceptible to manual feature engineering; they fail to capture sequential dependencies in web content and are known to show very high false positive rates. Most importantly, they are not scalable and cannot adapt to the new changes that keep emerging in phishing techniques, making them ineffective. In fact, to combat these challenges, the present research work proposes an advanced model for phishing website detection using a Bidirectional Long Short-Term Memory (Bi-LSTM) network engineered with an attention mechanism. The Bi-LSTM infrastructure effectively captures contextual dependencies across the website data, while the attention mechanism refines this by easily selecting significant features based on the patterns for phishing detection. Additionally, Particle Swarm Optimization (PSO) optimizes model parameters to enhance detection accuracy while minimizing computation complexity. With the experimental results available, the proposed model has achieved a practically superior performance improvement over conventional approaches of 12-15% accuracy and standard machine learning methods. The model becomes more dynamic for real-time phishing detection which augments the safety mechanisms against evolving cyber threats by deep learning and optimization techniques.