Integration of SDR and UAS for Malicious Wi-Fi Hotspots Detection

Wang, Jian; Juárez, Nicolás Corona; Kohm, Emma; Liu, Yongxin; Yuan, Jiawei; Song, Houbing

doi:10.1109/icnsurv.2019.8735296

Cited by 16 publications

(9 citation statements)

References 16 publications

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“…Chen et al [95] Young and Bostian [27] Miko and Nemeth [96] Harounabadi et al [97] Brown et al [98] Reyes et al [10] Guevara et al [99] Anderson et al [100] Mikó and Németh [101] Andryeyev et al [102] Saleem et al [9] VonEhr et al [103] Jacob et al [15] Tato et al [104] Murphy et al [105] Sboui et al [106] Gutierrez et al [107] Gonzalez and Fung [108] Horapong et al [109] Zhang et al [110] Ghazzai et al [111] Cai et al [112] Noble et al [113] Petrolo et al [114] Shi et al [115] Huang et al [116] Shi et al [117] Liu et al [118] Sklivanitis et al [119] Pärlin et al [120] Dunne and Keenlance [121] Shamaei et al [122] Santana et al [51] Jadon et al [123] Torabi et al [124] Xu et al [125] Pan et al [126] Shen et al [127] Aftab et al [128] Adane [129] Almasoud and Kamal [130] Matheou et al [131] Wang et al [132] Murphy et al…”

Section: Q1 Q2 Q3 Q4 Q5mentioning

confidence: 99%

Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview

Santana

Cristo

Branco

2021

Sensors

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Unmanned Aerial Vehicles (UAVs) demand technologies so they can not only fly autonomously, but also communicate with base stations, flight controllers, computers, devices, or even other UAVs. Still, UAVs usually operate within unlicensed spectrum bands, competing against the increasing number of mobile devices and other wireless networks. Combining UAVs with Cognitive Radio (CR) may increase their general communication performance, thus allowing them to execute missions where the conventional UAVs face limitations. CR provides a smart wireless communication which, instead of using a transmission frequency defined in the hardware, uses software transmission. CR smartly uses free transmission channels and/or chooses them according to application’s requirements. Moreover, CR is considered a key enabler for deploying technologies that require high connectivity, such as Smart Cities, 5G, Internet of Things (IoT), and the Internet of Flying Things (IoFT). This paper presents an overview on the field of CR for UAV communications and its state-of-the-art, testbed alternatives for real data experiments, as well as specifications to build a simple and low-cost testbed, and indicates key opportunities and future challenges in the field.

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Section: Q1 Q2 Q3 Q4 Q5mentioning

confidence: 99%

Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview

Santana

Cristo

Branco

2021

Sensors

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“…The use of drones is booming around the world with a large variety of potential applications: wireless acoustic networking for amateur drone surveillance [ 1 ], updating of UAV networking using the software-defined radios (SDR) and software-defined networking (SDN) [ 2 ], the multi-agent reinforcement learning (MARL) framework [ 3 ] and malicious Wi-Fi hotspots detection [ 4 ]. In particular, the use of the UAV camera has become very important in the field of detecting abnormal behaviour in video footage.…”

Section: Introductionmentioning

confidence: 99%

End-to-End Deep One-Class Learning for Anomaly Detection in UAV Video Stream

et al. 2021

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In recent years, the use of drones for surveillance tasks has been on the rise worldwide. However, in the context of anomaly detection, only normal events are available for the learning process. Therefore, the implementation of a generative learning method in an unsupervised mode to solve this problem becomes fundamental. In this context, we propose a new end-to-end architecture capable of generating optical flow images from original UAV images and extracting compact spatio-temporal characteristics for anomaly detection purposes. It is designed with a custom loss function as a sum of three terms, the reconstruction loss (Rl), the generation loss (Gl) and the compactness loss (Cl) to ensure an efficient classification of the “deep-one” class. In addition, we propose to minimize the effect of UAV motion in video processing by applying background subtraction on optical flow images. We tested our method on very complex datasets called the mini-drone video dataset, and obtained results surpassing existing techniques’ performances with an AUC of 85.3.

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“…Many efforts have been dedicated to find optimal routing for communication between nodes, where the nodes may be UAVs and base stations and compose the so-called aerial Wireless Sensor Network (aerial WSN). Many of the algorithms for routing inside aerial WSN use the well-known Ad-hoc On-demand Distance Vector (AODV) [ 2 , 3 ] routing, however routing algorithms such as Optimized Link State Routing (OLSR) can be also implemented. The handicap of AODV is that the path discovery requires a high amount of time for the establishment of the initial forwarding routes [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

On Analyzing Routing Selection for Aerial Autonomous Vehicles Connected to Mobile Network

Batalla

Mavromoustakis

Mastorakis

et al. 2021

Sensors

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This paper proposes a two-phase algorithm for multi-criteria selection of packet forwarding in unmanned aerial vehicles (UAV), which communicate with the control station through commercial mobile network. The selection of proper data forwarding in the two radio link: From UAV to the antenna and from the antenna to the control station, are independent but subject to constrains. The proposed approach is independent of the intra-domain forwarding, so it may be useful for a number of different scenarios of Unmanned Aerial Vehicles connectivity (e.g., a swarm of drones). In the implementation developed in this paper, the connection is served by three different mobile network operators in order to ensure reliable connectivity. The proposed algorithm makes use of Machine Learning tools that are properly trained for predicting the behavior of the link connectivity during the flight duration. The results presented in the last section validate the algorithm and the training process of the machines.

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Integration of SDR and UAS for Malicious Wi-Fi Hotspots Detection

Cited by 16 publications

References 16 publications

Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview

Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview

End-to-End Deep One-Class Learning for Anomaly Detection in UAV Video Stream

On Analyzing Routing Selection for Aerial Autonomous Vehicles Connected to Mobile Network

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