Drones' Face off: Authentication by Machine Learning in Autonomous IoT Systems

Karimibiuki, Mehdi; Aibin, Michał; Lai, Yuyu; Khan, Raziq; Norfield, Ryan; Hunter, Aaron

doi:10.1109/uemcon47517.2019.8992931

Cited by 15 publications

(5 citation statements)

References 14 publications

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“…Although encryption may only prolong the time for data loss, it can provide an extra level of defense and can get activated through the acknowledged command by the MSCs [61]. 3) High Complexity Level: When the aerial or terrestrial intruders launch sophisticated cyber attacks, such as side-channel attacks, fault injection attacks, software attacks, etc., into the legitimate UAV's link, UAVs should be equipped with advanced cryptographic mechanisms and secure key management to combat such attacks [62]. These attacks are complex to execute as they are supposed to surpass the security levels of the legitimate link to overhear the transmitted information.…”

Section: Overview Of Security Threats On Uavsmentioning

confidence: 99%

Security Threats and Mitigation Techniques in UAV Communications: A Comprehensive Survey

et al. 2022

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Unmanned aerial vehicles (UAVs) have been instrumental in enabling many new applications and services, including military and rescue operations, aerial surveillance, civilian applications, precision farming, as well as providing extensive wireless network access in remote areas. They have also been used in other areas such as transmission line and oil rig monitoring, disaster recovery, etc. With their increasing payload ability and flight duration, using UAVs is a preferred choice for a multitude of upcoming wireless communication systems. However, because of their open operational nature, UAVs are highly vulnerable to severe security breaches via cyber attacks, eavesdropping on navigational and communication links, etc. Given their widespread applications, the requirement of secured UAV communications has become more and more critical since security failures can lead to detrimental consequences. Several works have examined the extent of privacy and security issues in UAV-assisted networks and introduced various mitigation techniques to address different security challenges. In this paper, a comprehensive survey is conducted that centers on the security issues related to UAV-aided networks. A descriptive taxonomy of various security intrusions on the UAV networks and commonly-used secrecy performance metrics are thoroughly reviewed. An in-depth discussion is provided on alleviating threats with proactive security techniques blended with key wireless communication technologies such as mmWave, non-orthogonal multiple access (NOMA), massive multiple-input multiple-output (MIMO), and cognitive radio. Moreover, several emerging topics like machine learning, software-defined networks, fog and edge computing, blockchain, are discussed in the context of UAV-aided secure communications.INDEX TERMS Physical layer security, unmanned aerial vehicles (UAVs), proactive mitigation techniques, secrecy metrics, emerging wireless technologies.GAURAV KUMAR PANDEY (Graduate Student Member, IEEE) received the B.Tech. degree in electrical and electronics engineering from the

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Section: Overview Of Security Threats On Uavsmentioning

confidence: 99%

Security Threats and Mitigation Techniques in UAV Communications: A Comprehensive Survey

et al. 2022

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“…For instance, in [4], the authors proposed an Elliptic Curve Cryptography (ECC)-based device identity proofing mechanism for drone authentication. In [9], an authentication mechanism based on Machine Learning (ML) algorithms for autonomous IoT systems. In [10], the authors proposed a lightweight key-based mutual authentication between anonymous UAVs through the shared ground station.…”

Section: Drones Authentication a Device Authenticationmentioning

confidence: 99%

A Proxy Signature-Based Swarm Drone Authentication With Leader Selection in 5G Networks

et al. 2022

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Drones are imperative for the 5G architecture as a mobile source to expand network coverage and support seamless services, particularly through enabling device-to-device (D2D) communication. Such deployment of drones in D2D settings raises various security threats in drone communication. While the existing D2D communication security standard within the 4G cellular architecture may address some of these issues, the standard includes heavy traffic toward the network core servers. If this security standard is to be adopted in the 5G D2D security services with the same traffic load, it may negatively impact the 5G network performance. Therefore, this paper proposes a lightweight proxy signature-based authentication mechanism for a swarm of drones compatible with the 5G D2D standard mechanisms. This paper proposes a distributed delegation-based authentication mechanism to reduce the traffic overhead toward the 5G core network. In this scheme, the legitimate drones are authorized as proxy delegated signers to perform authentication on behalf of the core network. Furthermore, we propose a mechanism to elect and relocate a new leader relay drone from the existing drone swarm. We implemented the proposed authentication algorithm in the 5G D2D-based communication package over NS-3 while performing the computational calculations on a RaspberryPi3 device to mimic the drone calculation process and delays. The performance of the proposed authentication shows a promising reduction in the authentication time and shows lightweight and reliable compatibility.

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“…The evolution of computer vision capabilities has made it possible to identify particular objects from various media sources, such as images and live streaming of videos [6][7][8][9][10][11]. One of the first papers on railway track object detection was proposed by Yuan et al They used the enhanced Ostu algorithm to segment the rail picture and calculated the rail surface area, although the selection of thresholds for this method was not uniform [12].…”

Section: Related Workmentioning

confidence: 99%

Aerial Footage Analysis Using Computer Vision for Efficient Detection of Points of Interest Near Railway Tracks

et al. 2022

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Object detection is a fundamental part of computer vision, with a wide range of real-world applications. It involves the detection of various objects in digital images or video. In this paper, we propose a proof of concept usage of computer vision algorithms to improve the maintenance of railway tracks operated by Via Rail Canada. Via Rail operates about 500 trains running on 12,500 km of tracks. These tracks pass through long stretches of sparsely populated lands. Maintaining these tracks is challenging due to the sheer amount of resources required to identify the points of interest (POI), such as growing vegetation, missing or broken ties, and water pooling around the tracks. We aim to use the YOLO algorithm to identify these points of interest with the help of aerial footage. The solution shows promising results in detecting the POI based on unmanned aerial vehicle (UAV) images. Overall, we achieved a precision of 74% across all POI and a mean average precision @ 0.5 (mAP @ 0.5) of 70.7%. The most successful detection was the one related to missing ties, vegetation, and water pooling, with an average accuracy of 85% across all three POI.

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Drones' Face off: Authentication by Machine Learning in Autonomous IoT Systems

Cited by 15 publications

References 14 publications

Security Threats and Mitigation Techniques in UAV Communications: A Comprehensive Survey

Security Threats and Mitigation Techniques in UAV Communications: A Comprehensive Survey

A Proxy Signature-Based Swarm Drone Authentication With Leader Selection in 5G Networks

Aerial Footage Analysis Using Computer Vision for Efficient Detection of Points of Interest Near Railway Tracks

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