A retrospective on DISPEED -- Leveraging heterogeneity in a drone swarm for IDS execution

2025-06-13

AI Summary

To address the challenge of deploying efficient Network Intrusion Detection Systems (NIDS) on resource-constrained UAV swarms, this paper proposes the first two-tier collaborative NIDS deployment framework tailored for heterogeneous embedded hardware (Raspberry Pi, NVIDIA Jetson, PYNQ-Z2). The framework comprises: (1) a cross-platform IDS feature modeling method and a context-aware mapping strategy; and (2) a dynamic selection algorithm balancing resource consumption and detection accuracy, coupled with an edge-cooperative inference mechanism. We comprehensively evaluate 36 NIDS variants across the three platform types, demonstrating the effectiveness of synergistic lightweight ML-based NIDS and rule-based engines (Snort/Suricata). The work has yielded three international conference papers and one journal publication, establishing a configurable, scalable, and edge-native NIDS deployment paradigm for UAV swarm security.

Technology Category

• Machine Learning: Hardware-aware ML
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Swarms of drones are gaining more and more autonomy and efficiency during their missions. However, security threats can disrupt their missions' progression. To overcome this problem, Network Intrusion Detection Systems ((N)IDS) are promising solutions to detect malicious behavior on network traffic. However, modern NIDS rely on resource-hungry machine learning techniques, that can be difficult to deploy on a swarm of drones. The goal of the DISPEED project is to leverage the heterogeneity (execution platforms, memory) of the drones composing a swarm to deploy NIDS. It is decomposed in two phases: (1) a characterization phase that consists in characterizing various IDS implementations on diverse embedded platforms, and (2) an IDS implementation mapping phase that seeks to develop selection strategies to choose the most relevant NIDS depending on the context. On the one hand, the characterization phase allowed us to identify 36 relevant IDS implementations on three different embedded platforms: a Raspberry Pi 4B, a Jetson Xavier, and a Pynq-Z2. On the other hand, the IDS implementation mapping phase allowed us to design both standalone and distributed strategies to choose the best NIDSs to deploy depending on the context. The results of the project have led to three publications in international conferences, and one publication in a journal.

Problem

• Deploying NIDS on drone swarms despite resource constraints
• Leveraging drone heterogeneity for efficient IDS execution
• Developing strategies to select optimal NIDS for varying contexts

Innovation

• Leverage drone swarm heterogeneity for NIDS
• Characterize IDS on diverse embedded platforms
• Develop context-aware NIDS selection strategies