# Intrusion Detection Systems for Networked Unmanned Aerial Vehicles: A Survey

arXiv: https://arxiv.org/abs/1807.00435 · HTML: https://mlsanity.com/paper/1807.00435/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2018.

## Built on by (most cited first, 4 of 4)

- [Enhanced Intrusion Detection System for Multiclass Classification in UAV Networks](https://mlsanity.com/paper/2406.10417/) (2024)
- [Quantum Machine Learning for UAV Swarm Intrusion Detection](https://mlsanity.com/paper/2509.01812/) (2025)
- [Risk-Aware Human-in-the-Loop Framework with Adaptive Intrusion Response for Autonomous Vehicles](https://mlsanity.com/paper/2601.11781/) (2026)
- [Quantum Machine Learning for Cyber-Physical Anomaly Detection in Unmanned Aerial Vehicles: A Leakage-Free Evaluation with Proxy-Audited Feature Sets](https://mlsanity.com/paper/2605.19233/) (2026)

## Abstract (arXiv)

Unmanned Aerial Vehicles (UAV)-based civilian or military applications become more critical to serving civilian and/or military missions. The significantly increased attention on UAV applications also has led to security concerns particularly in the context of networked UAVs. Networked UAVs are vulnerable to malicious attacks over open-air radio space and accordingly, intrusion detection systems (IDSs) have been naturally derived to deal with the vulnerabilities and/or attacks. In this paper, we briefly survey the state-of-the-art IDS mechanisms that deal with vulnerabilities and attacks under networked UAV environments. In particular, we classify the existing IDS mechanisms according to information gathering sources, deployment strategies, detection methods, detection states, IDS acknowledgment, and intrusion types. We conclude this paper with research challenges, insights, and future research directions to propose a networked UAV-IDS system which meets required standards of effectiveness and efficiency in terms of the goals of both security and performance.