Abstract
Malware attacks are expanding dramatically, yet traditional malware detection fails to keep up with the rapidly increasing malware threats. This paper proposes a Malware Cryptographic Security Architecture Attack Analysis framework that integrates cryptographic hashing, fuzzy hashing, and machine learning-based behavioral analysis to provide a more reliable and accurate malware detection system. The framework combines fast signature-based identification with dynamic behavioral monitoring to improve detection of both known and unknown malware. The novelty of this work lies in the design of a hybrid, multi-stage malware detection architecture that integrates cryptographic hashing, fuzzy similarity analysis, and machine learning-based behavioral classification within a single operational pipeline. Unlike conventional approaches that rely either on signatures or deep learning, the proposed system dynamically applies cryptographic techniques for known malware and their variants while utilizing computational intensive machine learning only for previously unseen threats. This multistage detection strategy achieves both high accuracy and realtime operational efficiency. Cryptographic hashes are used to rapidly detect known malware, while fuzzy hashing enables the identification of polymorphic and modified variants. Suspicious behaviors such as abnormal network activity, file access, memory behavior and other indicators are analyzed using a Random Forest classifier. The proposed system reduces false positives and improves detection of zero-day and unseen malware. Experimental results demonstrate an overall detection accuracy of 96.8 % on a dataset including various malware families, showing that the proposed architecture provides an effective, efficient, and scalable solution for malware detection in modern cybersecurity environments.