A Novel Hybrid Expert System for Encryption Detection in Smart Grid and Mobile Storage Devices

Ivan Opirskyy, Oleh Harasymchyk, Leila Rzayeva, Bayandy Sarsembayev, Murat Zhakenov, Kassimova Botakoz, Korhan Kayisli

Abstract


Ensuring reliable detection of encrypted data is increasingly important for protecting smart grid infrastructures and supporting mobile forensic investigations. This study investigates statistical and non-statistical approaches for encrypted data detection, including the Kolmogorov–Smirnov test, autocorrelation analysis, compression testing, file signature search, entropy estimation, and file system recognition. The main contribution of this work is a novel hybrid expert system that combines multiple detection methods with a knowledge-based inference engine to provide intelligent and explainable decision-making. Experimental results demonstrate that the proposed system achieves over 90% accuracy in identifying encrypted data under diverse conditions, including scenarios involving empty blocks that affect statistical distributions. Compared with conventional entropy-based and single-test approaches, the proposed framework provides improved accuracy, scalability, transparency, and adaptability.


Keywords


Data encryption detection, distributed control systems, intelligent decision support, Kolmogorov-Smirnov test, smart grid security

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References


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DOI (PDF): https://doi.org/10.20508/ijsmartgrid.v10i3.762.g430

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