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A Machine-Learning based Wireless Intrusion Detection System



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Journal of Information Systems Security
Volume 14, Number 1 (2018)
Pages 2130
ISSN 1551-0123 (Print)
ISSN 1551-0808 (Online)
Jeffrey L. Duffany — 1Universidad del Turabo, Puerto Rico
Carlos Y. Velez — Politechnic University, Puerto Rico
Information Institute Publishing, Washington DC, USA




One method of designing an Intrusion Detection System (IDS) is to have it first learn what is normal behaviour, and then program it to send an alert whenever a significant deviation occurs. A Software Defined Radio (SDR) system can scan a desired range of the radio frequency spectrum and obtain periodic snapshots of communication activity, for example WiFi spectra. Machine learning techniques can then be applied to detect anomalous activity over the airwaves. Cluster analysis demonstrated the possibility of developing an IDS using a wavelet transform to convert WiFi spectra into a feature vector comprised of the energy in each wavelet component. Training sets of 100 WiFi spectra were used, some with intrusions and others not. The R Language was used to implement the prototype IDS using both the Naive Bayes and Neural Networks classifiers.




Intrusion Detection System, Wavelet, Machine Learning, Cluster Analysis, Software Defined Radio WiFi




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