Deep Learning for Malware Detection and Classification

Malicious software, or malware, poses a persistent and evolving threat to cybersecurity. Traditional detection methods based on signatures and heuristics are challenged by the rapid proliferation of polymorphic and zero-day attacks. This research paper explores the application of deep learning, specifically Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), in the realm of malware detection and classification. CNNs excel in capturing spatial patterns in binary data, treating it akin to images, while RNNs are adept at handling sequential information. Through rigorous experimentation and evaluation, this study provides a comparative analysis of the performance of these deep learning architectures against traditional methods. Furthermore, the paper delves into the interpretability of deep learning models, shedding light on their decision-making process in the context of malware detection. The findings of this research not only demonstrate the efficacy of deep learning in bolstering cybersecurity measures but also contribute valuable insights into the evolving landscape of malware defense strategies.