Enhanced Intrusion Detection System for Multiclass Classification in UAV Networks

TL;DR

This paper introduces a deep learning-based intrusion detection system for UAV networks that improves multiclass classification accuracy by capturing complex patterns and discarding misleading features, achieving 95% accuracy on a UAV dataset.

Contribution

The paper proposes a novel IDS using binary-tuple encoding and deep learning, with feature correlation analysis to enhance multiclass UAV network security detection.

Findings

Achieved 95% classification accuracy on UAV-IDS-2020 dataset.

Effectively captured complex class relationships and temporal patterns.

Reduced misleading features through cross-correlation analysis.

Abstract

Unmanned Aerial Vehicles (UAVs) have become increasingly popular in various applications, especially with the emergence of 6G systems and networks. However, their widespread adoption has also led to concerns regarding security vulnerabilities, making the development of reliable intrusion detection systems (IDS) essential for ensuring UAVs safety and mission success. This paper presents a new IDS for UAV networks. A binary-tuple representation was used for encoding class labels, along with a deep learning-based approach employed for classification. The proposed system enhances the intrusion detection by capturing complex class relationships and temporal network patterns. Moreover, a cross-correlation study between common features of different UAVs was conducted to discard correlated features that might mislead the classification of the proposed IDS. The full study was carried out using the UAV-IDS-2020 dataset, and we assessed the performance of the proposed IDS using different evaluation metrics. The experimental results highlighted the effectiveness of the proposed multiclass classifier model with an accuracy of 95%.