Towards Secure Wireless Mesh Networks for UAV Swarm Connectivity: Current Threats, Research, and Opportunities

Lopez, Martin Andreoni; Baddeley, Michael; Lunardi, Willian T.; Pandey, Anshul; Giacalone, Jean‐Pierre

doi:10.1109/dcoss52077.2021.00059

Cited by 18 publications

(5 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, unlike many other methods in other detection approaches, authentication would not be required; consequently, misidentification due to potential stolen credential would be less of a risk. Cryptography, which is a widely used technique for authentication, also consumes a lot of energy [11]. Accordingly, since UAVs operate on limited energy, for some applications, it may be desirable to cut down on their cryptographic usage.…”

Section: B Sybil Attack Detection In Ioftmentioning

confidence: 99%

“…It refers to the situation when a malicious node falsely claims to have numerous identities [8] [9]. There are several incentives for a node to act in such a way; in the context of FANETs, examples are such as to allow it to illegitimately acquire more weight in a voting system and to create an illusion of traffic congestion in a particular area [10] [11]. Countermeasures for Sybil attack include prevention, detection and mitigation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sybil Attack Detection in Internet of Flying Things-IoFT: A Machine Learning Approach

Chulerttiyawong

Jamalipour

2023

IEEE Internet Things J.

View full text Add to dashboard Cite

Sybil attack refers to the situation when a malicious node falsely claims to have numerous identities and is known to be one of the security threats to the Internet of Things (IoT). Due to recent increase usage of unmanned aerial vehicles (UAVs) in various applications, Sybil attack has been identified as a threat to the flying ad hoc network (FANET) paradigm and its integration with the IoT to form the Internet of Flying Things (IoFT). In this paper, we propose an intelligent Sybil attack detection approach for FANETs-based IoFT using physical layer characteristics of the radio signals emitted from the UAVs as detected by two ground nodes. A supervised machine learning approach is employed and experimented with several different classifiers available in the Weka workbench platform. The experiment was carried out based on two features of the radio signals, namely, the received signal strength difference (RSSD) and the time difference of arrival (TDoA). Simulation results revealed that the proposed scheme can achieve a high correct classification accuracy of above 91% on average, even for smart malicious nodes with power control capability operating at power levels not directly trained. In addition to its high performance, the proposed scheme is also less susceptible to various attacks commonly carried out on the upper layers, such as data spoofing, due to the use of only intrinsically generated physical layer data. Furthermore, no additional communications overheads of the UAV nodes are required for the functionality of this scheme.

show abstract

Section: B Sybil Attack Detection In Ioftmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sybil Attack Detection in Internet of Flying Things-IoFT: A Machine Learning Approach

Chulerttiyawong

Jamalipour

2023

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…This involves maintaining inter-UAV distances, flying formation and flying height of the UAV grid. The synchronisation and coordination is achieved through continuous flow of Message Queuing Telemetry Transport (MQTT, [46], [47]) commands and instructions between UAVs over wireless network.…”

Section: Ecms System Modelmentioning

confidence: 99%

ECMS: Energy-Efficient Collaborative Multi-UAV Surveillance System for Inaccessible Regions

Singhal

Barick

2022

IEEE Access

View full text Add to dashboard Cite

The evolution and popular adaptation of drone technology in diverse applications has necessitated advancement of UAV communication framework. UAVs inherently support features like mobility, flexibility, adaptive altitude, which make them a preferable option for dynamic surveillance of remote locations. Multiple UAVs can cooperatively work to accomplish surveillance missions more efficiently. However, the intermittent network connectivity and the limited onboard energy storage impose a great challenge on UAV-assisted remote surveillance. This paper presents an Energy-efficient Collaborative Multi-UAV Surveillance (ECMS) system for surveillance of inaccessible regions. The system employs an optimal Multi-UAV Collaborative Monocular Vision (MCMV) topology to facilitate the surveillance with zero blind spot using minimum number of drones. We also propose an application-aware Multi-Path Weighted Load-balancing (MWL) routing protocol for handling congestion by distributing traffic among all available resources in UAV network and adaptively selecting the of source datarate (i.e. switching video resolution). The simulation results demonstrate that the proposed surveillance system achieves coverage with lesser number of UAVs compared to the existing systems. It also achieves higher throughput, higher packet-delivery ratio, higher residual energy of UAVs, and lower end-to-end delay.INDEX TERMS Unmanned aerial vehicle, remote surveillance, UAV network topology, multi-path routing, load balancing.

show abstract

“…These security threats have been deemed as a critical concern due to the increasing reliance on wireless services [1]. A swarm of Unmanned Aerial Vehicles (UAVs), for example, commonly employ off-the-shelf infrastructure-less wireless communication (such as 802.11s in mesh mode) which can be significantly affected by external threats [2].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, we provide an implementation of different types of jammers on a HackRF 1 . Specifically, the main contributions of this work are: i) Presenting the Bit Error Rate (BER) performance analysis for the IEEE 802.11n communication system in the presence of jammers and under the assumption of Gaussian noise and digitally modulated (QPSK) waveforms; ii) Validation of the analysis through MATLAB simulation 2 : evaluating the impact of these jamming waveforms (Gaussian noise and QPSK) on the performance of IEEE 802.11n communications; iii) The development and implementation of 'JamRF', a jamming toolkit for the HackRF SDR; and iv) Investigating the impact of the considered different jamming techniques on IEEE 802.11n communications through practical experimentation within an RF isolation chamber.…”

Section: Introductionmentioning

confidence: 99%

JamRF: Performance Analysis, Evaluation, and Implementation of RF Jamming Over Wi-Fi

et al. 2022

Self Cite

View full text Add to dashboard Cite

Jamming attacks significantly degrade the performance of wireless communication systems and can lead to significant overhead in terms of re-transmissions and increased power consumption. Although different jamming techniques are discussed in the literature, numerous open-source implementations have used expensive equipment in the range of thousands of dollars with the exception of a few. These implementations have also tended to be partial band, and do not cover the whole available bandwidth of the system under attack. In this work, we demonstrate that flexible, reliable, and low priced software-defined radio (SDR) jamming is feasible by designing and implementing different types of jammers against IEEE 802.11n networks. First, to demonstrate the optimal jamming waveform, we present an analytical bit error rate expression of the system under attack by employing two common jamming waveforms: Gaussian noise and digitally modulated. Then, we validate this analysis through simulations using the MATLAB WLAN toolbox. Afterwards, we implement JamRF, a toolkit that employs a low-cost SDR to implement numerous types of jammers to validate the analysis. Obtained results showed that, to jam the whole 2.4GHz spectrum, a stateful-reactive jammer employing random channel hopping jamming strategy, achieves a packet loss ratio above 90%.

show abstract

Towards Secure Wireless Mesh Networks for UAV Swarm Connectivity: Current Threats, Research, and Opportunities

Cited by 18 publications

References 39 publications

Sybil Attack Detection in Internet of Flying Things-IoFT: A Machine Learning Approach

Sybil Attack Detection in Internet of Flying Things-IoFT: A Machine Learning Approach

ECMS: Energy-Efficient Collaborative Multi-UAV Surveillance System for Inaccessible Regions

JamRF: Performance Analysis, Evaluation, and Implementation of RF Jamming Over Wi-Fi

Contact Info

Product

Resources

About