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Identifying Tampered Radio-frequency Transmissions In Lora Networks Using Machine Learning.

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Identifying Tampered Radio-frequency Transmissions In Lora Networks Using Machine Learning.

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Identifying Tampered Radio-Frequency Transmissions in LoRa Networks Using Machine Learning.

Nurettin Selcuk Senol¹, Amar Rasheed¹, Mohamed Baza²

¹Department of Computer Science, Sam Houston State University, Huntsville, TX 77340, USA.

Sensors (Basel, Switzerland)

|October 26, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces an image-based method using anomaly detection algorithms to identify tampered radio frequency signals in LoRa networks. Local Outlier Factor achieved the highest accuracy, enhancing LoRa security.

Keywords:

IoT LoRa anomaly detection cybersecurity frequency analysis machine learning

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Area of Science:

* Internet of Things (IoT) security
* Wireless communication systems
* Cybersecurity and data integrity

Background:

* Long-range (LoRa) networks are crucial for IoT, offering low-power, long-range communication.
* LoRa devices are susceptible to radio-frequency interference and signal manipulation, compromising data integrity and security.
* Detecting tampered frequency signals in LoRa networks is a significant challenge.

Purpose of the Study:

* To present an innovative method for detecting tampered radio frequency transmissions in LoRa networks.
* To evaluate the efficacy of five anomaly detection algorithms for identifying signal manipulation.
* To enhance the security and reliability of LoRa-based IoT systems.

Main Methods:

* Utilized five anomaly detection algorithms: Local Outlier Factor, Isolation Forest, Variational Autoencoder, traditional Autoencoder, and Principal Component Analysis.

* Employed image-based tampered frequency techniques by converting LoRa transmission signals into images.

* Generated a dataset of over 26,000 images from real-world experiments with normal and manipulated signals.

Main Results:

* Local Outlier Factor (LOF) demonstrated the highest detection accuracy at 97.78%.
* Variational Autoencoder (VAE), traditional Autoencoder (AE), and Principal Component Analysis (PCA) achieved 97.27% accuracy.
* Isolation Forest (IF) achieved 84.49% accuracy in detecting tampered signals.

Conclusions:

* The proposed image-based anomaly detection methods are effective in identifying tampered radio frequency signals in LoRa networks.
* Local Outlier Factor shows superior performance in detecting signal manipulation.
* These findings offer a promising approach to bolster the security and reliability of LoRa-based IoT infrastructures.