MAOMLB: Advancing Malware Analysis with AI-Based Open-Source Architecture Integrating Machine Learning and Behavioral Techniques

Abstract

The sophistication in cyberattacks calls for new solutions so that malware can be properly dissected. This work presents the architecture of the AI open-source system that infuses novel machine learning models to increase the effectiveness of malware identification and analysis. Superior pattern recognition CNNs are exploited to analyze the patterns, along with LSTMs, while behavioral insights are inspected from the time-series data samples. Reduction in dimensions helps streamline data of large dimensionality for visualization, where PCA and t-SNE are often used. Markov chains and isolation forests are further applied for modeling behaviors and anomaly detection, respectively. Experimental evaluation on various benchmark datasets delivers outstanding results compared with the best available methods of an order of magnitude while improving precision by 8.3%, accuracy by 8.5%, recall by 9.4%, AUC by 10.5%, specificity improved by 5.9%, and further reducing detection delay by 2.9%. These results highlight robust detection and mitigation of variant malware attacks by the system. This manuscript describes an advanced AI-based open-source architecture, MAOMLB, which can enhance malware detection through techniques involving machine learning and behavioral analysis. Its performance appears to be better than that of existing methodologies, which suffer from major drawbacks, on metrics such as precision, recall, and AUC. It is open source and encourages community-driven enhancement for robust cybersecurity applications.