Enhancing ransomware detection using Siamese network

Abstract

Organizations in the current digital era are exposed to a variety of cybersecurity threats that can often result in financial losses and harm to their reputation. Among these threats, ransomware attacks can cause significant damage. Attackers are constantly improving their techniques to bypass security channels, which makes it challenging to monitor and detect the patterns of attacks. Consequently, there is a growing inclination towards employing state-of-the-art techniques to identify and defend during ransomware attacks. Deep learning is a proven technique that can be employed to learn from large complex patterns. However, large datasets are required in the training of deep learning models which is a challenging task. Few-shot learning (FSL) overcomes this limitation by using less data. In this research work, a Siamese network design is developed by incorporating the architectural principles of AlexNet and features of the VGG configuration. The employed methodology enables us to evaluate the inherent resemblances and disparities in the data. This novel methodology demonstrated exceptional performance, with an average accuracy of 97% when compared to various effects and learning rates. The results of the presented study demonstrate the capacity to greatly enhance cybersecurity by providing a scalable and effective approach for detecting ransomware.