Defending Against Intrusion of Malicious UAVs with Networked UAV Defense Swarms

Brust, Matthias R.; Danoy, Grégoire; Bouvry, Pascal; Gashi, Dren; Pathak, Himadri; Gonçalves, Mike P.

doi:10.1109/lcn.workshops.2017.71

Cited by 58 publications

(36 citation statements)

References 12 publications

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“…We noticed that there is a small subset of papers where the cruise part could be split into several parts. For example, in [20], defending against malicious UAVs with UAV defense swarms is discussed. The cruise part of the flight consists of the following parts: (1) deployment, (2) clustering, (3) formation, (4) chase, (5) escort.…”

Section: Nomenclaturementioning

confidence: 99%

Multiple UAV Systems: A Survey

2020

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Nowadays, Unmanned Aerial Vehicles (UAVs) are used in many different applications. Using systems of multiple UAVs is the next obvious step in the process of applying this technology for variety of tasks. There are few research works that cover the applications of these systems and they are all highly specialized. The goal of this survey is to fill this gap by providing a generic review on different applications of multiple UAV systems that have been developed in recent years. We also present a nomenclature and architecture taxonomy for these systems. In the end, a discussion on current trends and challenges is provided.

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Section: Nomenclaturementioning

confidence: 99%

Multiple UAV Systems: A Survey

2020

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“…However, they did not consider the situation of air-to-air interference, so their review only focused on interference to ground stations. Brust et al [16] proposed a method for escorting detected malicious UAVs by transforming the formation of the UAV swarm. However, if the malicious UAV suddenly launches a jamming attack, the large number of UAVs near this malicious UAV will suffer disastrous consequences.…”

Section: Cyber Threats Against Uav Networkmentioning

confidence: 99%

A Dyna-Q-Based Solution for UAV Networks Against Smart Jamming Attacks

Lü

Shi

et al. 2019

Symmetry

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Unmanned aerial vehicle (UAV) networks have a wide range of applications, such as in the Internet of Things (IoT), 5G communications, and so forth. However, the communications between UAVs and UAVs to ground control stations mainly use radio channels, and therefore these communications are vulnerable to cyberattacks. With the advent of software-defined radio (SDR), smart attacks that can flexibly select attack strategies according to the defender’s state information are gradually attracting the attention of researchers and potential attackers of UAV networks. The smart attack can even induce the defender to take a specific defense strategy, causing even greater damage. Inspired by symmetrical thinking, a solution using a software-defined network (SDN) to combat software-defined radio was proposed. We propose a network architecture which uses dual controllers, including a UAV flight controller and SDN controller, to achieve collaborative decision-making. Built on the top of the SDN, the state information of the whole network converges quickly and is fitted to an environment model used to develop an improved Dyna-Q-based reinforcement learning algorithm. The improved algorithm integrates the power allocation and track planning of UAVs into a unified action space. The simulation data showed that the proposed communication solution can effectively avoid smart jamming attacks and has faster learning efficiency and higher convergence performance than the compared algorithms.

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“…(1) When the node does not perform intrusion detection but the attacker initiates a malicious behavior, the profits ( ) ij P FPDR (8) In the following we describe the best balance between IDS high detection and low overhead using Bayesian Nash equilibrium game theory.…”

Section: The Design Of Intrusion Detection Systemmentioning

confidence: 99%

“…(1) Classifier-based IDS: Brust et al [8] proposed a UAV defense system based on the self-balancing clustering method. When an intrusion is detected, the rogue UAVs or maliciously-targeted aircraft can be tracked by the UAV group used for defense, which effectively reduce the communication losses.…”

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confidence: 99%

An Intrusion Detection Based on Bayesian Game Theory for UAV Network

Sun¹,

Wang²,

Da³

et al. 2018

Proceedings of the 11th EAI International Conference on Mobile Multimedia Communications

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Unmanned aerial vehicles (UAVs) have enormous potential in public, civil and military fields, so the security of UAV networks is attracting increasing people's attention. The existing schemes to network intrusion detection have two main limitations: a high false alarm rate and computational overhead. In this paper, aiming at the problem of UAV network security, an intrusion detection scheme based on Bayesian game theory is proposed. Bayesian game means that game participants do not have complete information on the profit function of the opponent. In this paper, incomplete information means that the IDS agent is not sure of the type of attacker and the attacker is not sure whether its neighbor node is an IDS agent or not. The simulation and experimental comparison show that the proposed scheme achieves a high detection rate of intrusion. In addition, the communication cost of the network is the lowest.

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Defending Against Intrusion of Malicious UAVs with Networked UAV Defense Swarms

Cited by 58 publications

References 12 publications

Multiple UAV Systems: A Survey

Multiple UAV Systems: A Survey

A Dyna-Q-Based Solution for UAV Networks Against Smart Jamming Attacks

An Intrusion Detection Based on Bayesian Game Theory for UAV Network

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