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

arXiv: https://arxiv.org/abs/2003.07308 · HTML: https://mlsanity.com/paper/2003.07308/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2020.

## Built on by (most cited first, 6 of 6)

- [Phishing Attacks Detection -- A Machine Learning-Based Approach](https://mlsanity.com/paper/2201.10752/) (2022)
- [Deep Attention Recognition for Attack Identification in 5G UAV scenarios: Novel Architecture and End-to-End Evaluation](https://mlsanity.com/paper/2303.12947/) (2023)
- [A Comprehensive Survey on the Cyber-Security of Smart Grids: Cyber-Attacks, Detection, Countermeasure Techniques, and Future Directions](https://mlsanity.com/paper/2207.07738/) (2022)
- [Two methods for Jamming Identification in UAVs Networks using New Synthetic Dataset](https://mlsanity.com/paper/2203.11373/) (2022)
- [Jammer classification with Federated Learning](https://mlsanity.com/paper/2306.02587/) (2023)
- [PCA-Featured Transformer for Jamming Detection in 5G UAV Networks](https://mlsanity.com/paper/2412.15312/) (2024)

## Abstract (arXiv)

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.