Robust Malware Detection Leveraging Machine Learning Algorithms

Abstract

The rampant increase in malware attacks has caused a significant impact on various industries and governments, leading to serious consequences. Malware analysis and detection has become hot topics for research. Malware could be anything that looks malicious or acts like a virus, worm, trojan, spyware, adware, etc. Any suspicious software that may cause harm to the system can be considered malware. Currently, static and dynamic strategies are used for malware analysis and detection, but it is time-consuming and ineffective for identifying malware in real-time. Advanced strategies like machine learning and deep learning are used to determine whether any executable file is malware, and these methods give better accuracy and performance traditional methods. These methods use Portable Executable (PE) file header for malware detection and classification.