Deep Learning Models to Identify and Classify Malware Variants

Abstract

Witthe ever-growing threat of cyber-attacks, proactive malware detection and remediation are of utmost importance in securing the digital world. Our research investigates using advanced deep learning techniques to detect and classify malware strains such as worms, viruses, and ransomware based on a multitude of characteristics including code signatures, behavior patterns, and structural elements. Deep neural network architectures including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer models, among others, are used to create a powerful and reliable malware detection machine learning framework. By studying tens of thousands of malwares, specimens and the characteristics that make them up, our models can differentiate between good and bad programs and learn how to quickly identify new malware threats. The suggested approach continuously learns new malware samples and adapts to attack vectors in real time to ensure a proactive security posture. The suggested model experiments significantly improve the performance of malware detection models and certify that they have a high detection rate in the new environment. This work shows how deep learning techniques could largely improve the detection of different malware viruses. Also, the quality of known detection improved steadily by analyzing the results of our models on a test set