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

by   Youness Arjoune, et al.

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.


page 1

page 2

page 3

page 4


Performance Evaluation of Machine Learning Techniques for DoS Detection in Wireless Sensor Network

The nature of Wireless Sensor Networks (WSN) and the widespread of using...

Phishing Attacks Detection – A Machine Learning-Based Approach

Phishing attacks are one of the most common social engineering attacks t...

Intelligent Methods for Accurately Detecting Phishing Websites

With increasing technology developments, there is a massive number of we...

Shapelets for earthquake detection

This paper introduces EQShapelets (EarthQuake Shapelets) a time-series s...

Decision Forest Based EMG Signal Classification with Low Volume Dataset Augmented with Random Variance Gaussian Noise

Electromyography signals can be used as training data by machine learnin...

A Survey of Machine Learning Algorithms for Detecting Ransomware Encryption Activity

A survey of machine learning techniques trained to detect ransomware is ...

Machine Learning Techniques to Detecting and Preventing Jamming Attacks in Optical Networks

We study the effectiveness of various machine learning techniques, inclu...