ForenSecure-AILSO: A Blockchain-Fuzzy Intelligence Framework for Resilient Cloud Forensics and Secure Log Management in Distributed Environments
Ragu Gnanaprakasam, Ramamoorthy Sriramulu, Poorvadevi Ramamoorthy · International Journal of Electronics and Communication Engineering · 2025
Cloud computing continues to redefine the way data is stored, processed, and accessed globally. However, as organizations use more cloud services, digital forensic investigations get more complex in these networks. Typical forensic methods still struggle to handle secure logging, key exchange, and current threat sharing in systems that change their operation with growth and shrinkage. This paper introduces ForenSecure-AILSO, a unique cloud forensics framework that uses Fuzzy Logic, CALSO, and mechanisms based on smart contracts to secure the collection of evidence. It tackles important problems in multi-cloud computing, such as log storage verification, mystery identity generation, real-time monitoring to spot risks, and matching forensic evidence. In order to keep the information secure and traceable, the log entries are cleaned, enriched, and hashed with SHA-3 on the private consortium blockchain. With a fuzzy identity engine, automatic tokens are created that are difficult to trace, while CALSO keeps session keys strong by adjusting them regularly based on how difficult they are to break. CALSO-TPR directs an anomaly detection engine to quickly spot and alert about any suspicious activity. An evaluation of six key areas, using 10 benchmark models, reveals that ForenSecure-AILSO performs better than other options, scoring an F1-score for log cleaning of 94.0%, accuracy in identifying threats of 96.5%, and a key entropy of 289 bits. The framework managed to detect 99.2% of attempts to change digital tokens and was precise with 93.5% of its subgraph matches, proving its strength and adherence to laws.