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Intrusion Detection Systems for Networked Unmanned Aerial Vehicles: A Survey

Gaurav Choudhary, Vishal Sharma, Ilsun You, Kangbin Yim, Ing-Ray Chen, Jin-Hee Cho

arXiv:1807.00435v1cs.CR

TL;DR

Networked UAVs face security vulnerabilities and malicious attacks, while intrusion detection must balance effectiveness with efficiency. This paper surveys UAV-IDS approaches and classifies their components, then identifies challenges and research directions toward an affordable, secure cyber-physical system.

  • Problem

    Networked UAVs face vulnerabilities that can compromise confidentiality, integrity, availability, and authorization, while effective IDS detection must be balanced against efficiency and overhead.

    Source: §V · p. 5

  • Method

    The paper surveys state-of-the-art UAV-IDS approaches and classifies them by information sources, deployment, detection methods and states, acknowledgment, and intrusion types.

    Source: §I · p. 1, §III · p. 2

  • Results

    The survey identifies challenges involving resource constraints, sophisticated threats, and distributed security vulnerabilities in UAV-IDS environments.

    Source: §VI · p. 5

  • Takeaways & Limitations

    Future research should account for these challenges in aiming to build an affordable, secure cyber-physical UAV-IDS.

    Source: §VI · p. 5

Abstract

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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.

I. INTRODUCTION

UAV mobility and reconfigurability support diverse civilian and military missions, but networked deployments also face architectural, spectrum, energy, overhead, and security challenges. This paper surveys UAV-IDS approaches, develops taxonomies, and discusses research challenges and future directions.

I. INTRODUCTION

  • Networked UAVs face challenges in architectural standardization, spectrum sharing, energy-aware deployment and path planning, and system overhead; this paper focuses on security.The authors also identify controllability and link distortion as concerns associated with UAV mobility.Source: §I · p. 1
  • The paper surveys state-of-the-art UAV-IDS approaches and taxonomies to explain system components and key security concerns.The survey is framed as an overview of what UAV-IDS systems are and what their components must address.Source: §I · p. 1
  • It discusses research challenges for building a cyber-physical hardened UAV-IDS in resource-constrained, hostile, dynamic, and distributed environments.These environments reflect characteristics of military tactical settings.Source: §I · p. 1
  • The paper proposes future research directions based on the challenges and insights discussed.The remainder covers background, taxonomies, evaluation techniques, research challenges, and future work.Source: §I · p. 1

II. OVERALL DESCRIPTION OF UAV-IDS ENVIRONMENTS

A UAV-IDS analyzes UAV or network activity under security rules to detect attacks and misuse. The paper describes its operational monitoring and placement, then introduces taxonomies of system components.

II. OVERALL DESCRIPTION OF UAV-IDS ENVIRONMENTS

  • A UAV-IDS analyzes behavior or activity against network security rules or policies to detect anomalous behavior and illegal activity.It monitors system configuration, data files, and network transmission to check for attacks, and aims to mitigate their effects and detect UAV misuse.Source: §II · p. 2
  • Operational monitoring can cover signals, command traffic, control instructions, working behavior, energy consumption, and UAV-component operations.The IDS also analyzes data flow and gathers information from UAV components operating as network nodes.Source: §II · p. 2
  • An IDS may be placed on a UAV or ground control system, with placement based on required security levels; most existing UAV IDSs use behavior-based detection.Policies provide low-complexity rules for detecting anomalies or potential threats.Source: §II · p. 2

III. TAXONOMIES OF UAV-IDS SYSTEM COMPONENTS

  • The taxonomy covers information-gathering sources, deployment strategies, detection methods and states, acknowledgment, and intrusion types.Figure 1 is identified as an operational overview of UAV-IDS.Source: §III · p. 2, Figure 1 · p. 2

A. Information Gathering Sources

The paper describes information sources across UAV sensors, communications, ground control, and onboard components, then distinguishes ground-coordinated from autonomous IDS deployment. Deployment must balance IDS activity against UAV transmissions and performance.

A. Information Gathering Sources

  • UAV-IDS information sources include sensors, communication links, the ground control system, and UAV components.These sources span onboard sensing and control, inter-UAV and ground-system traffic, and information exchanged among UAV components.Source: A. Information Gathering Sources · pp. 2–3
  • Sensor information can include signals and behavior from inertial, location, or threat sensors, while malicious retrieval from onboard sensors can affect UAV performance.Sensors may be integrated into the UAV or tied to a specific task object, such as a weather-capturing sensor.Source: A. Information Gathering Sources · pp. 2–3
  • Communication links transmit to UAVs and share information among UAVs and ground systems, while also monitoring traffic between source and destination.Ground control conducts surveillance and reconnaissance based on data generated by aircraft payloads.Source: A. Information Gathering Sources · p. 3
  • Deployment must balance IDS operations against UAV transmissions, and can be ground-coordinated or autonomous.Ground-coordinated IDSs analyze gathered information at the ground station; autonomous IDSs analyze data and control or coordinate other UAVs from host UAVs.Source: A. Information Gathering Sources · p. 3

B. Intrusion Detection Systems (IDSs)

The paper distinguishes specification-, signature-, anomaly-, and hybrid-based IDS mechanisms. Their rules, known-attack signatures, learning or filtering, and combinations address different detection targets.

B. Intrusion Detection Systems (IDSs)

C. Detection States

The survey distinguishes on-site (dynamic) and off-site (static) detection states according to where information is collected and analyzed.

C. Detection States

  • UAV-IDS detection states are classified as on-site (dynamic) or off-site (static).The distinction is based on the source of information.Source: C. Detection States · p. 3
  • On-site detection uses data from real-time operations, with analysis and decisions made at the UAVs.Source: C. Detection States · p. 3
  • Off-site detection analyzes all data collected by the IDS from information sources to make its detection decision.Source: C. Detection States · p. 3

D. IDS Acknowledgment

The survey distinguishes instant from periodic IDS acknowledgment based on when detection decisions or alarms are generated.

D. IDS Acknowledgment

E. Intrusion Types

The survey identifies malware, signal and information modification, routing and path attacks, and message or GPS spoofing among the intrusion types a UAV-IDS should detect.

E. Intrusion Types

IV. SURVEY OF EXISTING UAV-IDS APPROACHES

The survey reviews UAV-IDS approaches addressing attacks and security and performance goals in sensitive UAV networks. It organizes the approaches using its proposed taxonomies.

IV. SURVEY OF EXISTING UAV-IDS APPROACHES

  • Table II surveys existing UAV-IDS approaches aimed at improving security and performance and protecting networked UAVs against inside and outside attackers.Source: §IV · p. 4
  • Blasch et al. use ROC plots to visualize classification effectiveness, while Lauf et al. use maxima and cross-correlation detection; the latter work does not capture false-positive or false-negative errors.The Maxima Detection System identifies zero or one suspicious node, whereas cross-correlation methods can detect multiple intrusions.Source: §IV · p. 4
  • Shen et al. model object, situation, and threat states, while Shen et al. use an adaptive Markov game for cooperative surveillance and jamming.The latter strategy considers IDS deployment, email filtering, firewall settings, and server shutdown or reset policies.Source: §IV · p. 4
  • Muniraj and Farhood detect sensor-based cyber-physical attacks using physical-system knowledge and statistical analysis, but not combinations of smaller piece-wise constant attacks.Sedjelmaci et al. propose hierarchical intrusion detection and response through threat classification and UAV behavior monitoring.Source: §IV · p. 4
  • Mitchell and Chen use attack-model behavior rules to minimize false positives and false negatives while balancing UAV security and performance.Kwon et al. use reachability analysis in a real-time safety assessment algorithm for cyber attacks.Source: §IV · p. 4
  • Lauf and Robinson propose distributed sensing for fault-tolerant resource management, though service-discovery flooding can cause communication bursts and congestion.Source: §IV · p. 4
  • Sedjelmaci et al. estimate threats from beliefs and use policies to maintain data integrity and network availability; their Security Game Framework models attacker and defense interactions with Bayesian games.Source: §IV · pp. 4–5
  • Trafton and Pizzi propose the Joint Airborne Network Services Suite to integrate a military airborne network, with IDS as part of its assurance strategy.Source: §IV · p. 5

V. RESEARCH CHALLENGES

The challenges center on balancing effective threat detection and response with the efficiency needed in resource-constrained UAV networks. The paper highlights tradeoffs in detection latency, cost, overhead, behavior modeling, and threat assessment.

V. RESEARCH CHALLENGES

  • UAV-IDS design must minimize detection errors and service interruptions while reducing computational and communication overhead.The paper identifies achieving both effectiveness and efficiency as a nontrivial design goal.Source: §V · p. 5
  • Detection latency measures IDS agility, but more frequent triggering raises communication and computation overhead; response speed also affects attack response.The paper frames balancing latency and overhead as necessary for practical UAV-IDS systems.Source: §V · p. 5
  • IDS computational cost trades overhead against accuracy and the level of security vulnerability a system allows.Greater cost can add overhead while also bringing security benefits.Source: §V · p. 5
  • High IDS implementation overhead can consume power and degrade UAV performance, potentially resulting in network shutdown.Source: §V · p. 5
  • Accurate observation and modeling of threat and attack behaviors is difficult, while effective threat-assessment policies are identified as key to security and performance.Source: §V · p. 5
  • Maximum network throughput and minimum cost conflict, though accurate dynamic monitoring can support achieving both dynamically set goals.Source: §V · p. 5
  • Lightweight IDS mechanisms are necessary because UAVs are battery-operated and resource-constrained.Source: §V · p. 5

VI. CONCLUSION & FUTURE RESEARCH DIRECTIONS

The paper concludes with an overview and taxonomy of UAV-IDS mechanisms and identifies challenges to developing effective, efficient systems. It proposes future work on attack modeling, behavior-rule IDS design, and evaluation using agility or resilience metrics.

VI. CONCLUSION & FUTURE RESEARCH DIRECTIONS

  • The paper synthesizes state-of-the-art UAV-IDS mechanisms, design challenges, taxonomies, and research challenges for an affordable, secure cyber-physical system.Source: §VI · p. 5
  • Future work will define UAV-IDS attack behaviors, derive an attack graph, and build corresponding countermeasures.Source: §VI · p. 6
  • The authors plan a behavior-rule UAV-IDS that minimizes memory while maximizing detection accuracy, with formal verification and Bayesian ground-truth checks proposed for validation.Source: §VI · p. 6
  • The planned lightweight UAV-IDS will be evaluated for effectiveness and efficiency using agility or resilience metrics.Source: §VI · p. 6
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