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DATAMUt: Deterministic Algorithms for Time-Delay Attack Detection in Multi-Hop UAV Networks

Keiwan Soltani, Federico Corò, Punyasha Chatterjee, Sajal K. Das

TL;DR

The paper tackles Time Delay Attacks in multi-hop UAV networks by introducing DATAMUt, a pair of deterministic polynomial-time algorithms that convert network temporal dynamics into a Time-Window Graph ($TWiG$). One algorithm operates with global knowledge, the other with local (2-hop) knowledge, enabling efficient and scalable detection of malicious nodes. By leveraging shortest-path comparisons on $TWiG$ and targeted local checks, the approach achieves high accuracy with substantially lower message overhead and execution time than existing ML-based methods like HOTD or ETD, including dramatic speedups (approximately $860\times$ global and $1050\times$ local) and reduced overhead (factor of 5 and 12 reductions in global/local settings). The results indicate strong practical impact for real-time, secure UAV networks, with potential extensions to variable transmission times and adaptive detection strategies.

Abstract

Unmanned Aerial Vehicles (UAVs), also known as drones, have gained popularity in various fields such as agriculture, emergency response, and search and rescue operations. UAV networks are susceptible to several security threats, such as wormhole, jamming, spoofing, and false data injection. Time Delay Attack (TDA) is a unique attack in which malicious UAVs intentionally delay packet forwarding, posing significant threats, especially in time-sensitive applications. It is challenging to distinguish malicious delay from benign network delay due to the dynamic nature of UAV networks, intermittent wireless connectivity, or the Store-Carry-Forward (SCF) mechanism during multi-hop communication. Some existing works propose machine learning-based centralized approaches to detect TDA, which are computationally intensive and have large message overheads. This paper proposes a novel approach DATAMUt, where the temporal dynamics of the network are represented by a weighted time-window graph (TWiG), and then two deterministic polynomial-time algorithms are presented to detect TDA when UAVs have global and local network knowledge. Simulation studies show that the proposed algorithms have reduced message overhead by a factor of five and twelve in global and local knowledge, respectively, compared to existing approaches. Additionally, our approaches achieve approximately 860 and 1050 times less execution time in global and local knowledge, respectively, outperforming the existing methods.

DATAMUt: Deterministic Algorithms for Time-Delay Attack Detection in Multi-Hop UAV Networks

TL;DR

The paper tackles Time Delay Attacks in multi-hop UAV networks by introducing DATAMUt, a pair of deterministic polynomial-time algorithms that convert network temporal dynamics into a Time-Window Graph (). One algorithm operates with global knowledge, the other with local (2-hop) knowledge, enabling efficient and scalable detection of malicious nodes. By leveraging shortest-path comparisons on and targeted local checks, the approach achieves high accuracy with substantially lower message overhead and execution time than existing ML-based methods like HOTD or ETD, including dramatic speedups (approximately global and local) and reduced overhead (factor of 5 and 12 reductions in global/local settings). The results indicate strong practical impact for real-time, secure UAV networks, with potential extensions to variable transmission times and adaptive detection strategies.

Abstract

Unmanned Aerial Vehicles (UAVs), also known as drones, have gained popularity in various fields such as agriculture, emergency response, and search and rescue operations. UAV networks are susceptible to several security threats, such as wormhole, jamming, spoofing, and false data injection. Time Delay Attack (TDA) is a unique attack in which malicious UAVs intentionally delay packet forwarding, posing significant threats, especially in time-sensitive applications. It is challenging to distinguish malicious delay from benign network delay due to the dynamic nature of UAV networks, intermittent wireless connectivity, or the Store-Carry-Forward (SCF) mechanism during multi-hop communication. Some existing works propose machine learning-based centralized approaches to detect TDA, which are computationally intensive and have large message overheads. This paper proposes a novel approach DATAMUt, where the temporal dynamics of the network are represented by a weighted time-window graph (TWiG), and then two deterministic polynomial-time algorithms are presented to detect TDA when UAVs have global and local network knowledge. Simulation studies show that the proposed algorithms have reduced message overhead by a factor of five and twelve in global and local knowledge, respectively, compared to existing approaches. Additionally, our approaches achieve approximately 860 and 1050 times less execution time in global and local knowledge, respectively, outperforming the existing methods.
Paper Structure (17 sections, 3 theorems, 4 equations, 8 figures, 3 tables, 3 algorithms)

This paper contains 17 sections, 3 theorems, 4 equations, 8 figures, 3 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. System Model
  4. Network Model
  5. Time Delay Attack Model
  6. Problem Formulation
  7. TDA Detection using Global Knowledge
  8. Constructing Time-Window Graph
  9. Detecting Malicious Node(s)
  10. TDA Detection using Local Knowledge
  11. Performance Evaluation
  12. Simulation Environment
  13. Performance Metrics
  14. Network Overhead
  15. Accuracy
  16. ...and 2 more sections

Key Result

Theorem 1

Given a message $m$ transmitted from the node $s_m$ to node $d_m$ along a path $p_m$, there exists at least one malicious node in $p_m$ if and only if $p_m \neq SP_m$.

Figures (8)

  • Figure 1: Normal and delay forwarding behaviors in the UAV network.
  • Figure 2: Effect of TDA in UAV network for emergency response
  • Figure 3: Example of a UAV network
  • Figure 4: Message and attached information.
  • Figure 5: Constructed TWiG corresponding to the scenario in Fig. \ref{['fig:UAV-net']}
  • ...and 3 more figures

Theorems & Definitions (10)

  • Definition 1
  • Definition 2
  • Definition 3
  • Definition 4
  • Theorem 1
  • proof
  • Theorem 2
  • proof
  • Theorem 3
  • proof