• Review of KIISC
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• v.30 no.2
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• pp.49-57
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• 2020

군사 등의 특수 목적으로만 사용되었던 무인비행체 드론이 최근 상용 수준의 간결한 구조와 저가화가 가능해지면서 여러 제조업체를 통하여 민간분야의 다양한 응용에 활용 가능함을 증명하고 있다. 그러나 제조업체들 간의 시장 우위 선점을 위한 과열 경쟁으로 인하여 보안 안전성 검증 단계를 거치지 않은 드론과 이에 수반되는 애플리케이션이 시장에 바로 출시되면서 이들이 우리 사회를 향한 공격도구로 활용될 수 있다는 새로운 잠재적 위협이 우려되고 있다, 이와 관련하여 현재 드론과 애플리케이션과의 연결 및 데이터 통신 과정에서 완성도가 낮은 접근제어 기술이나 암호화되지 않은 통신방식을 비롯하여 드론 내부 소프트웨어의 코딩 상의 문제점 등에 의하여 다양한 취약점이 노출되고 있는 상태이다. 이러한 취약점들로 인하여 드론의 인증 해제 및 하이재킹을 통한 불법 영상촬영이나 개인정보의 유출 등을 비롯하여 특정 목표물을 향한 드론의 고의적 추락 등이 발생할 경우 재산 피해뿐만 아니라 인명 피해까지 발생할 수 있다. 특히, 현재의 드론 응용은 초기단계여서 향후 다양한 응용과 유관 기술들이 폭넓게 전개되어야 하는 시점에서 이러한 사고 가능성은 매우 심각하게 인식되어야 할 것이다. 더구나 제4차 산업혁명 시대의 드론은 비상시를 위한 다이나믹 모바일 게이트웨이 역할까지도 수행하여야 하는 환경에서 악의적인 행위는 전체 사회로 확산될 우려도 있으므로 미래 사회의 드론을 위한 안전문제는 매우 시급하고 중대하다고 할 수 있다. 이에 본 고에서는 현재까지 발표된 드론에 대한 다양한 보안위협을 조사하고 이러한 보안 위협을 요소별로 분류하여 정리하였다. 본 기고가 간단하게나마 정리한 내용을 통하여 다양한 보안위협에 대한 대응기술을 준비하기 위한 시발점이 되었으면 한다.

• Journal of Advanced Navigation Technology
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• v.24 no.5
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• pp.393-407
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• 2020

In order to reduce the threat by illegal unmanned aerial vehicles, a tracking system based on sound was implemented. There are three main points to the drone acoustic tracking method. First, it scans the space through variable beam formation to find a sound source and records the sound using a microphone array. Second, it classifies it into a hidden Markov model (HMM) to find out whether the sound source exists or not, and finally, the sound source is In the case of a drone, a sound source recorded and stored as a tracking reference signal based on an adaptive beam pattern is used. The simulation was performed in both the ideal condition without background noise and interference sound and the non-ideal condition with background noise and interference sound, and evaluated the tracking performance of illegal drones. The drone tracking system designed the criteria for determining the presence or absence of a drone according to the improvement of the search distance performance according to the microphone array performance and the degree of sound pattern matching, and reflected in the design of the speech reading circuit.

• 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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.4
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• pp.633-646
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• 2023

The increases in UAVs(Unman Aerial Vehicle) such as drone result in safety issues and the threat of illegal drone as well. Recognizing the need for Drone forensics, domestic and foreign organizations and agencies are trying to establish drone forensic guidelines. The definition of Drone forensic artifacts and examination of forensic tools must be provided, in order to establish a practical drone forensic framework on security sites and also the concept of drone live forensic which provides meaningful data that can be extracted in a live state. In this study, the drone forensic methodology covering various types of drones is explained, and the practical forensic methodology with live forensic PoC(Proof Of Concept) tools; LiPFo(Live-PX4-Forenensic) is proposed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.4
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• pp.477-485
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• 2019

As the drones industry evolved, the security of the drone network has been important. In this paper, MTD (Moving Target Detection) technique is applied to the drone network for improving security. The existing MTD scheme has a risk that the hash value is exposed during the wireless communication process, and it is restricted to apply the one-to-many network. Therefore, we proposed PHT (Preposition Hash Table) scheme to prevent exposure of hash values during wireless communication. By reducing the risk of cryptographic key exposure, the use time of the cryptographic key can be extended and the security of the drone network will be improved. In addition, the cryptographic key exchange is not performed during flight, it is advantageous to apply PHT for a swarm drone network. Through simulation, we confirmed that the proposed scheme can contribute to the security of the drone network.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.338-341
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• 2020

최근 무인이동체 기술과 IoT(사물인터넷)의 발전에 따라 드론에 대한 관심과 사용이 꾸준히 증가하고 있다. 드론은 취미용으로 사람들에게 재미를 주는 것에서 나아가 긴급서비스, 조기정보, 모니터링 등 이용되는 분야가 다양하고 사람들의 편의에 맞게 분야와 활용목적이 점점 늘어나고 있는 추세이다. 하지만 이에 따라 불법몰카나 드론을 사용한 테러 등 악의적으로 드론을 악용하는 사례 역시 빈번하게 발생하고 있다. 이를 예방하고 사전에 차단하기 위하여 본 논문에서는 주파수 송수신기인 Hack-RF One과 라즈베리파이, 안테나를 활용하여 Anti-Drone 시스템 프로토타입을 구현하였다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.4
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• pp.609-619
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• 2023

Recently, crimes using drones, one of the IoT industries have been continuously reported. In particular, drones are characterized by easy access and free movement, so they are used for various crimes such as transporting explosives, transporting drugs, and illegal recording. In order to analyze and investigate these criminal acts, drone forensic research is highly emphasized. Media data, PII, and flight records are digital forensic artifacts that can be acquired from drones, in particluar flight records are important artifacts since they can be used to trace drone activities. Therefore, in this paper, the characteristics of the deleted flight record files of DJI drones are presented and verified using the Phantom3, Phantom4 andMini2 models, two drones with differences in characteristics. Additionally, the recovery level is analyzed using the flight record file characteristics, and lastly, drones with the capacity to recover flight records for each drone model and drone models without it are classified.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.881-890
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• 2022

As individual and group users actively use drones, the risks (Intrusion, Information leakage, and Sircraft crashes and so on) in no-fly zones are also increasing. Therefore, it is necessary to build a system that can detect drones intruding into the no-fly zone. General acoustic drone detection researches do not derive location-independent performance by directly learning drone sound including environmental noise in a deep learning model to overcome environmental noise. In this paper, we propose a drone detection system that collects sounds including environmental noise, and detects drones by removing noise from target sound. After removing environmental noise from the collected sound, the proposed system predicts the drone sound using Mel spectrogram and CNN deep learning. As a result, It is confirmed that the drone detection performance, which was weak due to unstudied environmental noises, can be improved by more than 7%.

• Convergence Security Journal
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• v.18 no.5_2
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• pp.3-10
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• 2018

The drone is an unmanned aircraft that can be steered and controlled using radio waves on the ground, and the pilot moves unmanned without boarding. The history of the unmanned airplane began with military use, and the first unmanned aerial flight was the first successful flight of the 'Sperry Aerial Torpedo' drones built in the United States in 1917 with a bomb. With the development of unmanned aerial technology, the use of military drones has expanded to a wider field. Recently, the use of drones has been utilized in various fields such as agriculture, industry, logistics, broadcasting, and safety, and the scale of the market is also expanding. Although the drones are becoming indispensable to penetrate our lives, they can be used for bad purposes depending on the intended use of the user, but the risk factors are overlooked. Therefore, technical defects related to drones and accidents caused by operator's mistakes can not be completely prevented. However, privacy infringement, security leakage, and terrorism, which may be caused by illegal use of drones, It will not be inhibited and will accelerate.

• Electronics and Telecommunications Trends
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• v.37 no.1
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• pp.10-20
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• 2022

A drone without attaching guns or bombs can be a dangerous weapon, since its motor speed is greater than 3000 rpm, which is similar to that of a mower powered by a LiPo battery. The anti-drone system is the only means of detecting and neutralizing drone attacks. Many defense companies around the world provide solutions using various types of equipment (for example, radar, cameras, jamming guns, and net guns). ETRI has also developed a Low-Altitude Drone Detection (LADD) system consisting of Ku-band radar and an Electro-Optical/Infra-Red (EO/IR) camera. In this paper, we summarize recent technical advances in anti-drone systems around the world and introduce the features and describe the performance of the LADD system.