• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1306-1307
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• 2015

As a basic research for the detection of GPS spoofing signal we study to generate a GPS fake signal which can mislead GPS receivers, and show that the fake signal is generated and transmitted through a pseudolite and the GPS receivers produce a wrong position as designated in the fake signal.

• The Journal of Korean Institute of Information Technology
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• v.17 no.5
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• pp.47-55
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• 2019

As the damage caused by GPS jamming has been increased, researches for detecting and preventing GPS jamming is being actively studied. This paper deals with a GPS jamming detection method using multiple GPS receiving channels and three-types machine learning techniques. Proposed multiple GPS channels consist of commercial GPS receiver with no anti-jamming function, receiver with just anti-noise jamming function and receiver with anti-noise and anti-spoofing jamming function. This system enables user to identify the characteristics of the jamming signals by comparing the coordinates received at each receiver. In this paper, The five types of jamming signals with different signal characteristics were entered to the system and three kinds of machine learning methods(AB: Adaptive Boosting, SVM: Support Vector Machine, DT: Decision Tree) were applied to perform jamming detection test. The results showed that the DT technique has the best performance with a detection rate of 96.9% when the single machine learning technique was applied. And it is confirmed that DT technique is more effective for GPS jamming detection than the binary classifier techniques because it has low ambiguity and simple hardware. It was also confirmed that SVM could be used only if additional solutions to ambiguity problem are applied.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.63-66
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• 2019

Recently, as the technology of drones develops, a malicious attack using a drones becomes a problem, and an anti-drone technology for detecting an attack dron for a malicious attack is required. However, currently used drone detection systems are expensive and require a lot of manpower. Therefore, in this paper, we propose an anti - drone method using the analysis and algorithms of the anti - drone that can monitor the attack drones. In this paper, we identify and detect attack drones using sniffing, and propose capture and deception algorithm through spoofing using current GPS based detection system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.5
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• pp.1069-1076
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• 2017

Recently $Pok\acute{e}mon$ GO implements an online game with location-based real time augmented reality on mobile. The correct play of this game should be based on collecting the $Pok\acute{e}mon$ that appears as the user moves around by foot, but as the popularity increases, it appears an abuse to play easily. Many people have used an application that provides a mock location service such as Fake GPS, and these applications can be judged to be cheating in online games because they can play games in the house without moving. Detection of such cheating from a client point of view (mobile device) can consume a large amount of resources, which can reduce the speed of the game. It is difficult for developers to apply detection methods that negatively affect game usage and user's satisfaction. Therefore, in this paper, we propose a method to detect users abusing mock location service in online game by route analysis using GPS location record from the server point of view.