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Automated Sensor Node Malicious Activity Detection with Explainability Analysis.

Md Zubair¹, Helge Janicke², Ahmad Mohsin²

¹Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chittagong 4349, Bangladesh.

Sensors (Basel, Switzerland)

|June 27, 2024

Summary

This summary is machine-generated.

This study introduces a hybrid data balancing technique and an ensemble machine learning model to effectively detect malicious sensor nodes in cybersecurity systems, achieving high accuracy.

Keywords:

cybersecurity data balancing ensemble learning explainability analysis malicious node detection wireless sensor node

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Area of Science:

Cybersecurity
Machine Learning
Data Science

Background:

Modern automated systems rely heavily on sensors for decision-making.
Malicious activities targeting sensors can cause system-wide failures.
Detecting malicious sensor activity is crucial for system safety and security.

Purpose of the Study:

To develop a robust method for detecting malicious sensor nodes.
To address the challenge of imbalanced datasets in detecting malicious activities.
To enhance the explainability of machine learning models in cybersecurity.

Main Methods:

Proposed a hybrid data balancing technique combining Cluster-based Under Sampling and Synthetic Minority Oversampling Technique (SMOTE).
Developed an ensemble machine learning model for improved detection accuracy.
Conducted explainability analysis to identify critical security risk features.

Main Results:

The proposed hybrid data balancing technique effectively handles imbalanced datasets.
The ensemble machine learning model achieved 99.7% accuracy in detecting malicious sensor nodes.
Identified critical features contributing to security risks in sensor nodes.

Conclusions:

The hybrid data balancing and ensemble model offer a reliable solution for detecting malicious sensor nodes.
The approach significantly enhances cybersecurity for sensor-based automated systems.
Explainability analysis provides valuable insights into sensor node vulnerabilities.