• Journal of Aerospace System Engineering
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• v.17 no.4
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• pp.133-143
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• 2023

As drones (also known as UAV) become popular with advanced information and communication technology (ICT), they have been utilized for various fields (agriculture, architecture, and so on). However, malicious attackers with advanced drones may pose a threat to critical national infrastructures. Thus, anti-drone systems have been developed to respond to drone threats. In particular, remote identification data (R-ID)-based UAV detection and identification systems that detect and identify illegal drones with R-ID broadcasted by drones have been developed, and are widely employed worldwide. However, this R-ID-based UAV detection/identification system is vulnerable to security due to wireless broadcast characteristics. In this paper, we analyze the security vulnerabilities of DJI Aeroscope, a representative example of the R-ID-based UAV detection and identification system, and propose a replay-attack-based neutralization method using the analyzed vulnerabilities. To validate the proposed method, it is implemented as a software program, and verified against four types of attacks in real test environments. The results demonstrate that the proposed neutralization method is an effective neutralization method for R-ID-based UAV detection and identification systems.

• Journal of Satellite, Information and Communications
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• v.12 no.4
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• pp.26-33
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• 2017

A drone performs a mission through remote control or automatic control, which uses wireless communications technology. Recently the increasing use of drones, the drone signal RF detection is necessary. In this paper, we propose an efficient dron RF detection method through simulations considering Wi-Fi, Bluetooth and dedicated protocol dron communication method in ISM(Industry Science Medical) band.. After configuring an environment where a common terminal and a drone signal are mixed, a general terminal and a drone signal are distinguished from each other by using a RF characteristic according to a dron movement. The proposed drone RF detection method is the WRMD(Windowed RSSI Moving Detection) operation and the Doppler frequency identification method. The simulation environments consist to mixed for two signals and four signals. We analysis the performance to proposed drone RF detection technique thorough detection rate.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1217-1228
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• 2023

In this paper, technical and patent analysses of anti-drone technology, which aim to neutralize drone attacks are conducted. We conducted research on the technical definition of anti-drone, the technical elements of anti-drone systems, and investigated the patents related to anti-drone and drone filed domestically and internationally over the past 10 years, starting from 2011. For domestic patents, we examined the number of patent applications related to anti-drone and the overall domestic patent applications over the past 10 years. Regarding international filings, we investigated the patent applications related to anti-drone filed in the United States, Europe, Japan, China, and under the PCT system in the past 10 years. We conducted a search for patents related to anti-drone, including neutralization techniques identified under the keyword "anti-drone," patents related to drone detection and identification techniques, and patents related to drone neutralization techniques. Through the conducted research, a total of 91 patents were filed for drone detection techniques. Out of these, 5 patents, accounting for 5.5%, were filed by public institutions. In the case of patents filed for drone identification techniques, there were a total of 174 patents. Among these, 4 patents, which is 2.3%, were filed by public institutions.

• Journal of the Korea Society of Computer and Information
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• v.29 no.10
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• pp.59-68
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• 2024

In this study, we proposed an efficient model that can detect and classify the drones and related devices based on radio frequency signals. In order to increase the applicability in the battlefield, proposed model was designed to be lightweight, to ensure rapid detection and high detection accuracy. Data preprocessing was performed by applying a Discrete Fourier Transform (DFT) that is faster than Hilbert-Huang Transform (HHT). We adopted the LightGBM model as the learning model, which can be easily used by non-professionals and guarantees excellent performance in terms of classification speed and accuracy. CardRF dataset was used to verify the performance of the proposed model. As a result of the experiment, the accuracy of 3 classes classification for detecting and classifying drones, WiFi, and Bluetooth device was 99.63% when the number of sample points was set to 100k and 99.40% when set to 500k during the data preprocessing with DFT. And, in the 10 classes classification for 6 drones, 2 Bluetooth devices, and 2 WiFi devices, the accuracy was 95.65% for 100k and 96.83% for 500k, confirming significantly improved detection performance compared to previous studies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.5
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• pp.619-627
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• 2021

Recent advance in low cost and light-weight drones has extended their application areas in both military and private sectors. Accordingly surveillance program against unfriendly drones has become an important issue. Drone detection and classification technique has long been emphasized in order to prevent attacks or accidents by commercial drones in urban areas. Most commercial drones have small sizes and low reflection and hence typical sensors that use acoustic, infrared, or radar signals exhibit limited performances. Recently, artificial intelligence algorithm has been actively exploited to enhance radar image identification performance. In this paper, we adopt machined learning algorithm for high resolution radar imaging in drone detection and classification applications. For this purpose, simulation is carried out against commercial drone models and compared with experimental data obtained through high resolution radar field test.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.233-236
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• 2021

본 논문에서는 국내 드론 사용량이 증가하고 있으나 드론을 제재하기 위한 수단과 AI를 활용한 드론 콘텐츠가 부족하다. 상기 문제점을 해결하기 위해 Darknet 과 YOLO_mark를 사용하여 디바이스를 학습시켜 손쉽게 드론 인식 및 구별을 할 수 있게 구현하였다. 이를 통해 기존 드론 제재 수단의 한계를 극복하고 손쉽게 이용할 수 있다. 나아가 본 논문을 이용하여 군◦경에서 드론 식별 등으로 활용할 수 있다.

• Journal of Advanced Navigation Technology
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• v.20 no.6
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• pp.604-611
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• 2016

Drone, which refers to unmanned aerial vehicles (UAV), industries are improving rapidly and exceeding existing level of remote controlled aircraft models. Also, they are applying automation and cloud network technology. Recently, the ability of drones can bring serious threats to public safety such as explosives and unmanned aircraft carrying hazardous materials. On the purpose of reducing these kinds of threats, it is necessary to detect these illegal drones, using acoustic feature extraction and classifying technology. In this paper, we introduce sound feature vector extraction method by harmonic feature extraction method (HLA). Feature vector extraction method based on HLA make it possible to distinguish drone sound, extracting features of sound data. In order to assess the performance of distinguishing sounds which exists in outdoor environment, we analyzed various sounds of things and real drones, and classified sounds of drone and others as simulation of each sound source.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.397-403
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• 2024

North Korea's rapidly advancing drone development and operational capabilities have become a significant threat to South Korea's security. The drone incursions by North Korea in 2014, 2017, and 2022 demonstrate the technological advancement and provocative potential of North Korean drones. This study aims to closely analyze the military threats posed by North Korean drones and seek effective countermeasures. The research examines the development level of North Korean drone technology, its military applications, the characteristics and patterns of recent drone incursions, the adequacy and limitations of South Korea's current response systems, and future countermeasures. For this purpose, domestic and international research literature and media reports were reviewed, and specific North Korean drone incursion cases were analyzed. The results indicate that North Korea's small drones possess technological features such as small size, low altitude, low-speed flight, long-duration flight, and reconnaissance equipment. These drones pose threats that can be utilized for reconnaissance, surveillance, surprise attacks, and terrorism. Additionally, South Korea's current response systems reveal limitations such as inadequate detection and identification capabilities, low interception success rates, lack of an integrated response system, and insufficient specialized personnel and equipment. Therefore, this study suggests various technical, policy, and international cooperative countermeasures, including the development of drone detection and identification technologies, the utilization of diverse drone neutralization technologies, the establishment of legal and institutional foundations, the construction of a cooperative framework among relevant agencies, and the strengthening of international cooperation. The study particularly emphasizes the importance of raising awareness of the North Korean drone threat across South Korean society and unifying national efforts to respond to these threats.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.257-262
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• 2023

Recently there are increasing number of claims that it is necessary to build a countermeasure in case of aggressive threats by small drones. During Russia-Ukraine war ignited by Russian invasion on February 2022, attacking drones have been being used widely to damage other country's national major facilities. On December 2022, 5 drones sent by North Korea made a flight around Seoul, South Korea about 7 hours, but it was not successful to search and track them. Furthermore, none of these were destroyed and shot down. Counter-drone system is essential system to search and identify unintended small drones and disable them. This paper is for proposing required functions for building a counter-drone system for national major facilities. We conducted focus group interviews with relevant government officials and analyzed their suggestions on how to augment protection capabilities to defend against small drone attacks.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.2
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• pp.147-157
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• 2018

There are increasing demands to provide early warning against intruding drones and cope with potential threats. Commercial anti-drone systems are mostly based on simple target detection by radar reflections. In real scenario, however, it becomes essential to obtain drone radar signatures so that hostile targets are recognized in advance. We present experimental test results that micro-Doppler radar signature delivers partial information on multi-rotor platforms and exhibits limited performance in drone recognition and classification. Afterward, we attempt to generate high resolution profile of flying drone targets. To this purpose, wide bands radar signals are employed to carry out inverse synthetic aperture radar(ISAR) imaging against moving drones. Following theoretical analysis, experimental field tests are carried out to acquire real target signals. Our preliminary tests demonstrate that high resolution ISAR imaging provides effective measures to detect and classify multiple drone targets in air.