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Enhanced IoT threat detection using Graph-Regularized neural networks optimized by Sea-Lion algorithm.

D Teja Santhosh1, Koganti Krishna Jyothi2, Koganti Srilakshmi3

  • 1Department of Computer Science and Engineering, CVR College of Engineering, Hyderabad, India.

Scientific Reports
|September 29, 2025
PubMed
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This study introduces a new method for detecting cyber security threats in the Internet of Things (IoT). The proposed approach significantly enhances threat detection accuracy, improving IoT security and protecting sensitive data.

Area of Science:

  • Cyber Security
  • Internet of Things (IoT)
  • Machine Learning

Background:

  • The interconnected nature of IoT systems introduces significant cyber security risks, including malware and software piracy.
  • These threats can compromise sensitive data and damage organizational reputation.
  • Existing methods for IoT threat detection require improvement to effectively mitigate these risks.

Purpose of the Study:

  • To propose an advanced method for Internet of Things (IoT) threat detection.
  • To enhance the accuracy and effectiveness of identifying both benign and malicious cyber security threats in IoT environments.
  • To provide a robust solution for safeguarding sensitive data and protecting organizational reputation.

Main Methods:

  • Utilized the Google Code Jam Dataset for analysis.
Keywords:
Alternating Graph-regularized neural networkEdge-Aware smoothing sharpening filtering

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  • Pre-processed data using Edge-Aware Smoothing Sharpening Filtering (EASSF) and feature extraction via General Synchro extracting Chirplet Transform (GSCT).
  • Employed an AGRNN classifier optimized with the Sea-lion Optimization Algorithm for threat classification.

    • Main Results:

    • Achieved significant accuracy improvements of up to 29.60% over existing methods for detecting benign threats.
    • Demonstrated higher accuracy for malicious threat detection, with improvements up to 27.6%.
    • Showcased superior F-measure and ROC values, confirming the method's effectiveness in cyber security threat detection.

    Conclusions:

    • The proposed IoT Threat Detection using Graph-Regularized Neural Networks Method offers a robust solution for enhancing IoT security.
    • The approach effectively safeguards sensitive data and protects organizational reputation against cyber threats.
    • This work provides a promising strategy for organizations aiming to strengthen their cyber security defenses.