From Binaries to Grayscale Pixels: A Deep Learning Approach to Unmask Ransomware Threats

Recently, we live in an interconnected, increasingly sophisticated, and ever more prevalent digital landscape, making the challenge of malicious code increasingly sophisticated and growing in prevalence. One type of malware called ransomware (which encrypts the victim’s data and demands a ransom payment for its decryption) requires advanced detection methods such as machine learning to combat it. Nevertheless, machine learning algorithms frequently encounter difficulty extracting meaningful patterns from high-dimensional data spaces, where each added feature exponentially increases the complexity of the search. This leads to sparse data distributions and increasing computational demands, making reliable feature selection and representation challenging. Consequently, models become prone to overfitting on noise and redundant variables, degrading their ability to generalize to new data. Therefore, this project adopts deep learning (Convolutional Neural Networks (CNNs)) to detect ransomware by transforming ransomware binaries into image data. CNN excels at handling high-dimensional data and capturing intricate patterns, making it suitable for tasks such as image and speech recognition. Our proposed CNN-based system achieves high accuracy, precision, recall, and F1 score, confirming its success in ransomware identification. We train our CNN over 20 epochs, which shows consistent improvements in loss and accuracy metrics, implying successful learning and generalization. The results of validation essentially match those of training; therefore, overfitting is small. For the classification of ransomware executable in binary image form, the suggested CNN achieves an accuracy of 99%, precision of 98.96%, recall of 98.96%, and F score of 98.9.