PHISHING DETECTION

Phishing attacks, which use phoney emails and misleading websites to target people and organisations, have emerged as one of the most common cybersecurity threats. These attacks cause serious security breaches by tricking users into divulging private information, including login credentials and financial information. This project offers a machine learning-based phishing detection tool for emails and web browsers that uses logistic regression to efficiently detect and stop phishing threats. To improve the accuracy of phishing detection, the study investigates different feature extraction methods, dataset preprocessing, and model training approaches. Python was used to develop the system, and the Scikit-learn library was used to implement the model. Both authentic and phishing URLs and emails made up the dataset, which was preprocessed using techniques like feature scaling and outlier removal. Key performance metrics, such as accuracy, precision, recall, F1-score, and a confusion matrix, were used to train and assess the model. The outcomes showed that the suggested model was successful in differentiating between phishing and authentic entities, as evidenced by its high classification accuracy. Notwithstanding its encouraging results, the system has certain drawbacks, including the difficulty of balancing false positives and false negatives and vulnerability to new phishing tactics. To further enhance detection capabilities, future studies could use ensemble approaches or sophisticated deep learning models. By offering users and organisations a reliable and effective detection mechanism, this project supports the continuous efforts to strengthen cybersecurity defences against phishing attacks.