(A) study on machine learning based jamming classification and detection for wireless communications

Abstract

In this paper, we propose a novel machine learning based jamming detection algorithm that can classify known attacks used for training and detect unknown attacks not used for training. The proposed algorithm has a hybrid structure of simple classification and anomaly detection models, which are decision tree (DT) and isolation forest (IF), respectively. After a test data passes through a DT that only classifies the data as normal or one of known attacks, it enters an IF algorithm that determines if the DT’s decision is indeed correct. Furthermore, an ensemble method is applied to reduce deviation. The proposed algorithm is evaluated on real dataset obtained from wireless modems operating in C‑band under static and mobile environments with a total of four types of jamming attacks. For the simultaneous classification and detection task, the proposed algorithm is shown to achieve superior performance over a baseline algorithm for all the cases of jamming distances, number of known jamming attacks, and mobility scenarios. For the detection‑only task, our algorithm is shown to achieve F1‑scores larger than 0.99.