Malware Classification Based on Deep Learning using Convloutional Neural Network

The increasing sophistication and volume of malware have made it a growing concern in the field of cybersecurity. Traditional detection techniques are becoming less effective as malware continues to evolve, making it harder to identify and mitigate. To address these challenges, deep learning offers a promising solution, particularly due to its advanced capabilities in pattern recognition. This project seeks to harness the power of deep learning to improve malware classification and detection. By applying several deep learning algorithms to a large dataset of malware samples, the research aims to outperform traditional detection methods in both speed and accuracy (99<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup>). Key objectives include developing a highly efficient and adaptable model that can swiftly respond to emerging malware threats, thereby strengthening cyber security defenses. The project will explore the use of modern deep learning techniques such as transfer learning, along with architectures like InceptionNet and MobileNet, to classify malware based on image representations of their signatures. The proposed approach is expected to not only provide better detection rates but also be scalable, enabling real-time detection and adaptation to newly discovered malware strains. Ultimately, the research aims to deliver a more robust, scalable solution for malware detection that can significantly improve cybersecurity systems.