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Machine learning based dynamic trust estimation framework for Securing wireless sensor networks.

Prafull Goswami1, Tayyab Khan2, Vinay Pathak1

  • 1Department of Computer Science and Engineering, Indian Institute of Information Technology Sonepat, Haryana, India.

Scientific Reports
|October 14, 2025
PubMed
Summary

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This summary is machine-generated.

The Secure Machine-learning-based Adaptive Reliable Trust (SMART) model enhances Wireless Sensor Network (WSN) security by accurately detecting malicious nodes using novel trust features and machine learning algorithms, improving overall network integrity and reliability.

Area of Science:

  • Computer Science
  • Network Security
  • Machine Learning

Background:

  • Wireless Sensor Networks (WSNs) are vulnerable to security threats and attacks that compromise data integrity and network reliability.
  • Existing security frameworks require enhancement to effectively detect malicious activities and ensure trustworthy data transmission.

Purpose of the Study:

  • To propose and evaluate a Secure Machine-learning-based Adaptive Reliable Trust (SMART) model for enhancing security and trustworthiness in unattended autonomous WSN environments.
  • To improve malicious node detection accuracy and strengthen overall network security and integrity.

Main Methods:

  • Developed the SMART model utilizing a novel machine learning algorithm to derive trust features: Co-Location Relationship (CLR), Co-Work Relationship (CWR), and Cooperativeness-Frequency-Duration (CFD).
Keywords:
DependabilityMachine learningMalicious nodesReliabilitySecurityTrust evaluationTrust features

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  • Integrated immediate (direct) and derived (indirect) trust mechanisms with logical time windows for monitoring interactions.
  • Employed K-means clustering for data point assignment, Principal Component Analysis (PCA) for variance identification, and Support Vector Machine (SVM) for dimension reduction and decision-making.

    • Main Results:

    • The SMART model achieved a malicious node detection rate of 96% with a False Negative Rate (FNR) of 0.7% and an F1-Score of 0.75.
    • Demonstrated high accuracy (96%) in identifying malicious nodes, even in the presence of 50 compromised devices.
    • The model effectively enhances trustworthiness and security in WSNs, showing significant improvements in performance measures.

    Conclusions:

    • The proposed SMART model offers a robust and accurate solution for securing Wireless Sensor Networks against malicious attacks.
    • The novel trust features and integrated machine learning techniques significantly improve the detection of compromised nodes and enhance network reliability.
    • SMART is a valuable framework for ensuring the integrity and trustworthiness of data in autonomous WSNs.