The development of a phishing attack protection system using software-hardware implementation of machine learning methods

Due to the constant evolution of phishing attacks, traditional protection methods, such as URL filtering and user training, have become insufficiently effective. The article examines modern methods of detecting phishing attacks using machine learning algorithms aimed at improving the accuracy and efficiency of URL classification. The developed system employs a multilayer perceptron for automatic URL analysis and classification of links as either phishing or legitimate. Creating a high-quality, representative dataset containing both phishing and legitimate links is one of the key stages in model development. The focus is on analyzing URL addresses based on 30 key features, including URL length, SSL certificate presence, and IP address usage. The model testing results demonstrated high accuracy, significantly surpassing the results of traditional filtering methods. The developed software, implemented in Python with TensorFlow and Scikit-Learn libraries, proved highly effective in real-world conditions, ensuring high accuracy, recall, and F1 score. The results confirm that machine learning enhances the efficiency and accuracy of phishing detection compared to traditional methods.

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