AI-based malware behavior detection focuses on identifying potentially malicious software by analyzing executable characteristics and runtime behavior within Linux environments. It involves examining binary-related features, monitoring process activities, analyzing system interactions and network communications, and extracting behavioral indicators to distinguish suspicious software activity from legitimate operations. By combining explainable machine learning classification with dynamic behavioral analysis, this approach helps identify potentially malicious binaries, detect abnormal execution patterns, and understand the features contributing to malware predictions. The resulting analysis supports timely malware investigation, improved threat visibility, and the implementation of appropriate detection and containment measures to reduce the impact of malicious software on system integrity and security.