Jamming Attack Detection and Classification Using Exponentially Weighted Moving Average and Random Forest in Wireless Sensor Networks
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Date
2024-10
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Addis Ababa University
Abstract
Wireless sensor networks are widely used in environmental monitoring, industrial automation, healthcare, and smart cities for data collection, real-time monitoring, and automated decision-making. These networks, consisting of randomly distributed autonomous nodes, are vulnerable to jamming attacks where malicious entities disrupt network transmissions by emitting interfering signals. Existing detection methods typically rely on either statistical or machine learning-based approaches, each with significant limitations: statistical-based methods are prone to high false alarm rates, while machine learning-based methods impose computational overhead on resource-constrained nodes. To address these limitations, this thesis presents a two-level jamming attack detection and classification method that combines the strengths of both approaches. The method integrates an Exponentially Weighted Moving Average (EWMA) for lightweight detection with a Random Forest classifier for accurate jamming attack classification. The approach begins with feature selection, utilizing key features such as the Received Signal Strength Indicator (RSSI) and Packet Error Rate (PER), which can be easily obtained without adding significant overhead on sensor nodes. The method consists of a training phase and a testing phase. In the training phase, the dataset is processed through the EWMA computation to smooth the time-series data, followed by threshold calculation. The EWMA-smoothed data is then used to train the Random Forest classifier. In the testing phase, the testing dataset also passes through the EWMA computation, and the EWMA-based jamming detection determines if a jamming attack is occurring by comparing against a predefined threshold. Once potential jamming is detected, the system transitions into the classification of the three jamming types: constant, periodic, or reactive jamming. Experimental evaluation demonstrates that our method achieves a 99.91% detection rate and 99.26% accuracy in jamming classification. These results show significant improvements over existing methods, particularly in reducing false positives while maintaining high detection accuracy.
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Keywords
Exponentially Weighted Moving Average, Jamming Detection, Radio Jamming, Random Forest, Wireless Sensor Network