![]() ![]() The model achieved a high accuracy of 0.9864, not only surpassing previous results but also providing valuable insights into the reasoning behind the classification. ![]() To address these limitations, this paper presents a novel model for malware detection, utilizing vision transformers to analyze the opcode sequences of more than 350,000 Windows portable executable malware samples from real-world datasets. However, these models often lack transparency, making it difficult to understand the reasoning behind the model’s decisions, otherwise known as the black-box problem. Recent developments in machine learning have led to the creation of deep models for malware detection. One of the major challenges in tackling this problem is the complexity of malware analysis, which requires expertise from human analysts. ![]() Windows malware is becoming an increasingly pressing problem as the amount of malware continues to grow and more sensitive information is stored on systems. ![]()
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