Artificial Intelligence and Malicious Code Detection

Abstract

Malicious software has become a critical cybersecurity issue due to the increasing number of threats it poses to devices and internet environments. Traditional static scanning techniques and behavior-based malware detection methods have limitations in meeting the new requirements in information security due to high false positives and false negatives. In this work, we propose a CNN convolutional neural network-based method for detecting malicious code. Practical operations were conducted using the Cuckoo sandbox system, and Python programs were utilized to preprocess analytical reports. This article presents the construction of a deep learning training model for CNN designed to identify malicious code. The model is compared with machine learning and general antivirus tools for comprehensive evaluation. Experimental verification shows that our proposed method exhibits greater advantages in comparison and achieved excellent detection results with higher feasibility. The research significance of this work is highlighted as malicious software has become a core issue in cybersecurity. The method proposed in this article provides powerful support for addressing new needs in information security. This paper emphasizes the importance of utilizing CNN-based methods in detecting malware, which can better address the limitations of traditional detection techniques. Overall, this work provides an effective solution to detect malicious code and addresses a critical cybersecurity issue.