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Artificial Intelligence-Driven Intrusion Detection in Software-Defined Wireless Sensor Networks: Towards Secure

Shimbi Masengo Wa Umba¹, Adnan M Abu-Mahfouz^1,2, Daniel Ramotsoela¹

¹Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0002, South Africa.

International Journal of Environmental Research and Public Health

|May 14, 2022

Summary

This summary is machine-generated.

This study compares AI techniques for intrusion detection in Software-Defined Wireless Sensor Networks. Decision Trees offer superior performance for anomaly detection, achieving state-of-the-art accuracy in securing Internet of Things systems.

Keywords:

Artificial Intelligence Internet of Things Software-Defined Wireless Sensor Network Wireless Sensor Network deep learning healthcare intrusion detection security

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Area of Science:

Computer Science
Network Security
Artificial Intelligence

Background:

Wireless Sensor Networks (WSNs) are crucial for Internet of Things (IoT) applications, handling vast amounts of critical data.
Efficient management and security are paramount for WSNs, leading to the development of Software-Defined Wireless Sensor Networks (SDWSNs).
Intrusion Detection Systems (IDS) are vital for safeguarding SDWSN-based IoT environments.

Purpose of the Study:

To evaluate the effectiveness of three Artificial Intelligence (AI) techniques—Decision Tree, Naïve Bayes, and Deep Artificial Neural Network—as anomaly detectors in IDSs for SDWSNs.
To compare the performance metrics of these AI models for both binary and multinomial intrusion classification.
To introduce an advanced feature engineering scheme to enhance detection accuracy.

Main Methods:

Trained Decision Tree, Naïve Bayes, and Deep Artificial Neural Network models on the NSL-KDD dataset.
Implemented an end-to-end feature engineering process to expand the dataset from 41 to 118 features.
Evaluated models based on accuracy (binary classification) and F-scores (multinomial classification).

Main Results:

The Decision Tree-based IDS demonstrated superior performance, achieving state-of-the-art accuracy of 0.999777 in binary classification and high F-scores in multinomial classification.
The Naïve Bayes model proved suitable for binary classification in resource-constrained SDWSN-IoT devices.
The Deep Artificial Neural Network shows significant promise as a future default anomaly detector due to its current performance and the growing availability of training data.

Conclusions:

Decision Tree-based anomaly detection is highly effective for securing SDWSN-based IoT systems.
Naïve Bayes is a viable option for low-memory IoT devices requiring binary intrusion detection.
Deep Artificial Neural Networks are poised to become the standard for anomaly detection in WSNs as data availability increases.