SENTINEY: Securing ENcrypted mulTI-party computatIoN for Enhanced data privacY and phishing detection

Phishing attacks have recently become a real danger that threatens the security of sensitive data. This research paper presents a new approach based on Secure Multi-Party Computation (SMPC) to identify phishing attacks on encrypted emails while ensuring data confidentiality and privacy. The proposed approach, SENTINEY (Securing ENcrypted mulTIparty computatIoN for Enhanced data privacY and phishing detection), combines unsupervised machine learning and string matching techniques to detect phishing links over encrypted data, making it adaptive. Subsequently, an adaptive system dynamically selects the appropriate phishing detection technique taking into account various factors (e.g, the volume of new attacks, the accuracy of the machine learning model, attack specificity and available system resources) is suggested. For efficiency reasons, the learning model uses network virtualization features to improve computational resources. This new approach has shown good performance in taking advantage of network virtualization to create a secure and collaborative environment for the SMPC. The dataset used to train and test the proposal is generated on the basis of real phishing emails, including real phishing URLs and keywords. An in-depth performance analysis evaluated the performance of the proposed approach in terms of efficiency (processing time) and robustness (accuracy, precision, recall and F1 score). Simulation results and comparison with relevant solutions show that the proposed approach achieves superior robustness at lower costs. Using a string-matching approach, the multilayer perceptron achieved the highest accuracy of 98% with a detection time of 0.89 s. On the other hand, Isolation Forest showed high efficiency in combating zero-day phishing attacks. The MLP model combined with other tools achieved an accuracy of 99.4%.