Anti-Drone System with Multiple Surveillance Technologies: Architecture, Implementation, and Challenges

Shi, Xiufang; Yang, Chaoqun; Xie, Weige; Liang, Chao; Shi, Zhiguo; Chen, Jiming

doi:10.1109/mcom.2018.1700430

Cited by 267 publications

(118 citation statements)

References 7 publications

Supporting

Mentioning

118

Contrasting

Order By: Relevance

“…Some of them involve the use of jammers to disable the remote controller of the drone, hence forcing it to land, the use of other drones to chase the not-authorized one and, finally, weapons to shoot the drone down [8]. While several start-ups have already developed different anti-drone solutions mainly based on radar detection and jamming, an interesting academic solution is represented by ADS-ZJU [9], where the authors propose an integrated approach combining multiple passive surveillance technologies to realize drone detection, localization, and radiofrequency jamming. At the same time, several projects on drone detection have been funded by the European Union within the H2020 program, including SafeShore 1 and Aladdin 2 , to name a few (see Sec.…”

Section: Introductionmentioning

confidence: 99%

PiNcH: An effective, efficient, and robust solution to drone detection via network traffic analysis

et al. 2020

View full text Add to dashboard Cite

We propose Picking a Needle in a Haystack (PiNcH ), a methodology to detect the presence of a drone, its current status, and its movements by leveraging just the communication traffic exchanged between the drone and its Remote Controller (RC). PiNcH is built applying standard classification algorithms to the eavesdropped traffic, analyzing features such as packets inter-arrival time and size. PiNcH is fully passive and it requires just cheap and general-purpose hardware. To evaluate the effectiveness of our solution, we collected real communication traces originated by a drone running the widespread ArduCopter open-source firmware, currently mounted on-board of a wide range (30+) of commercial amateur drones. Then, we tested our solution against different publicly available wireless traces. The results prove that PiNcH can efficiently and effectively: (i) identify the presence of the drone in several heterogeneous scenarios; (ii) identify the current state of a powered-on drone, i.e., flying or lying on the ground; (iii) discriminate the movements of the drone; and, finally, (iv) enjoy a reduced upper bound on the time required to identify a drone with the requested level of assurance. The effectiveness of PiNcH has been also evaluated in the presence of both heavy packet loss and evasion attacks. In this latter case, the adversary modifies on purpose the profile of the traffic of the drone-RC link to avoid the detection. In both the cited cases, PiNcH continues enjoying a remarkable performance. Further, the comparison against state of the art solution confirms the superior performance of PiNcH in several scenarios. Note that all the drone-controller generated data traces have been released as open-source, to allow replicability and foster follow-up. Finally, the quality and viability of our solution, do prove that network traffic analysis can be successfully adopted for drone identification and status discrimination, and pave the way for future research in the area.

show abstract

Section: Introductionmentioning

confidence: 99%

PiNcH: An effective, efficient, and robust solution to drone detection via network traffic analysis

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Multerer et al [42] build an antidrone system which consists of a 3D Frequency Modulated Continuous Wave (FMCW) Multiple Input Multiple Output (MIMO) radar and a directional jammer. Moreover, Shi et al [15] developed an anti-drone system which combines multiple passive surveillance technologies to realize drone detection, localization, and radio frequency jamming. Pärlin et al [43] investigated the threat model to neutralize remotely controlled Unmanned Aerial Vehicle by RF jamming.…”

Section: Defeating Drones With Jammingmentioning

confidence: 99%

“…PIETRO TEDESCHI received the bachelor's degree in computer and automation engineering with a thesis on the Analysis of Security Protocols for the Internet of Things, in IEEE 802. 15 ROBERTO DI PIETRO was in the capacity of Global Head Security Research at Nokia Bell Labs and an Associate Professor of computer science with the University of Padua, Italy. He is currently a Full Professor of cybersecurity with HBKU-CSE.…”

mentioning

confidence: 99%

Leveraging Jamming to Help Drones Complete Their Mission

2020

View full text Add to dashboard Cite

We propose JAM-ME, an autonomous jamming-assisted navigation system that allows a drone to accomplish its mission even in the presence of an anti-drone jamming protection system. In this contribution, we review the current state-of-the-art highlighting how current solutions to respond to drones are completely ineffective against JAM-ME. In particular, we introduce our solution and its theoretical framework, and later we relax a few constraints of the baseline model (adding wind drift, and allowing the target to be randomly placed within the jammed area). Moreover, we run extensive simulations that do support our findings: even under the combined action of jamming and wind drift, the drone can reach a target randomly deployed in the jamming area-any other drone, under the same conditions, would have simply failed. As per the overhead, under very conservative assumptions, JAM-ME experiences an average overhead of 70%; however, note that such overhead can be reduced by at least a tenfold factor in practical settings-discussed in the paper. Finally, we highlight the intrinsic limitations of our solution, potential countermeasures, and new jamming strategies that can pave the way to further research.

show abstract

“…With the rapid development of drone technology, more and more drones have been utilized for commercial and other purposes, which threaten public safety and air traffic to some extent [1]. Therefore, the issue of drone detection has received considerable critical attention in recent years, which aims to detect drone at the early phase when the drones only occupy less than 1% area of each image.…”

Section: Introductionmentioning

confidence: 99%

TIB-Net: Drone Detection Network With Tiny Iterative Backbone

et al. 2020

View full text Add to dashboard Cite

With the widespread application of drone in commercial and industrial fields, drone detection has received increasing attention in public safety and others. However, due to various appearance of smallsize drones, changeable and complex environments, and limited memory resources of edge computing devices, drone detection remains a challenging task nowadays. Although deep convolutional neural network (CNN) has shown powerful performance in object detection in recent years, most existing CNN-based methods cannot balance detection performance and model size well. To solve the problem, we develop a drone detection network with tiny iterative backbone named TIB-Net. In this network, we propose a structure called cyclic pathway, which enhances the capability to extract effective features of small object, and integrate it into existing efficient method Extremely Tiny Face Detector (EXTD). This method not only significantly improves the accuracy of drone detection, but also keeps the model size at an acceptable level. Furthermore, we integrate spatial attention module into our network backbone to emphasize information of small object, which can better locate small-size drone and further improve detection performance. In addition, we present massive manual annotations of object bounding boxes for our collected 2860 drone images as a drone benchmark dataset, which is now publicly available 1. In this work, we conduct a series of experiments on our collected dataset to evaluate TIB-Net, and the result shows that our proposed method achieves mean average precision of 89.2% with model size of 697.0KB, which achieves the state-of-the-art results compared with existing methods.

show abstract

Anti-Drone System with Multiple Surveillance Technologies: Architecture, Implementation, and Challenges

Cited by 267 publications

References 7 publications

PiNcH: An effective, efficient, and robust solution to drone detection via network traffic analysis

PiNcH: An effective, efficient, and robust solution to drone detection via network traffic analysis

Leveraging Jamming to Help Drones Complete Their Mission

TIB-Net: Drone Detection Network With Tiny Iterative Backbone

Contact Info

Product

Resources

About