arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2003.07308cs.ITeess.SPmath.IT

A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication

Youness Arjoune, Fatima Salahdine, Md. Shoriful Islam, Elias Ghribi, Naima Kaabouch

英文摘要:

Jamming attacks target a wireless network creating an unwanted denial of service. 5G is vulnerable to these attacks despite its resilience prompted by the use of millimeter wave bands. Over the last decade, several types of jamming detection techniques have been proposed, including fuzzy logic, game theory, channel surfing, and time series. Most of these techniques are inefficient in detecting smart jammers. Thus, there is a great need for efficient and fast jamming detection techniques with high accuracy. In this paper, we compare the efficiency of several machine learning models in detecting jamming signals. We investigated the types of signal features that identify jamming signals, and generated a large dataset using these parameters. Using this dataset, the machine learning algorithms were trained, evaluated, and tested. These algorithms are random forest, support vector machine, and neural network. The performance of these algorithms was evaluated and compared using the probability of detection, probability of false alarm, probability of miss detection, and accuracy. The simulation results show that jamming detection based random forest algorithm can detect jammers with a high accuracy, high detection probability and low probability of false alarm.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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