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A secure and efficient encryption system based on adaptive and machine learning for securing data in fog computing.

Priyanka Rajan Kumar1, Sonia Goel2

  • 1Department of Computer Science, Punjabi University, Patiala, 147001, India. srpriyankass@gmail.com.

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Summary
This summary is machine-generated.

This study presents an adaptive encryption framework for fog computing, using machine learning to dynamically adjust security levels for enhanced data protection and efficient processing in edge environments.

Keywords:
AES algorithmAdaptive encryptionECC algorithmFog computingMachine learning

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Edge Computing

Background:

  • Fog computing, integral to edge computing, faces unique security challenges due to its distributed and heterogeneous nature.
  • Existing security measures may not adequately balance performance and protection in dynamic fog environments.

Purpose of the Study:

  • To develop a novel adaptive encryption framework for fog computing environments.
  • To enhance data security and processing efficiency by dynamically adjusting encryption strategies based on data sensitivity and context.

Main Methods:

  • Implemented a machine learning-based system using K-Nearest Neighbors (KNN) for data classification (sensitive vs. normal).
  • Employed a hybrid encryption approach (ECC + AES) for sensitive data and standard AES for normal data.
  • Conducted comprehensive experiments evaluating encryption/decryption time, throughput, histogram analysis, NPCR, UACI, and scalability with varying dataset sizes.

Main Results:

  • The adaptive framework demonstrated effective security enhancements with high NPCR (99.349%) and UACI (33.079%).
  • Evaluations confirmed consistent performance and adaptability across different data types (text, image) and sizes (1kB to 1000kB).
  • The system achieved a balance between robust security and efficient data processing.

Conclusions:

  • The proposed adaptive encryption framework significantly improves security in fog computing.
  • The system's dynamic adjustment capabilities ensure efficient and secure data transmission in large-scale edge environments.