Abstract: Malware, or malicious software, is defined as any software that is purposely meant to harm computers, networks, or users. Malware is a broad term that refers to numerous forms of malicious programs used by cybercriminals to steal data, disrupt operations, or gain illegal access to networks.In order to analyse and represent the data, many different types of charts and diagrams are used. It further elaborates on issues such as adversarial threats, computational cost, and data quality in a complete view of the area. The paper tests both conventional machine learning algorithms and state-of-the-art deep learning models, which, in the author's opinion, prove that convolutional neural networks are the superior choice for malware detection. The authors also point to the necessity of balanced datasets and hybrid analysis methods, which apply both static and dynamic techniques for dealing with malware complexity. Through key findings and actionable insights, this paper helps to advance work on the further development of automatic malware analysis systems and the hardening of digital infrastructures.
Keywords: Artificial Intelligence, Malware Analysis, Cybersecurity, Automated Classification, Data Visualization, Static Analysis, Dynamic Analysis
Copyright Form