• Proceedings of the Korean Information Science Society Conference
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• 2010.11a
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• pp.95-96
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• 2010

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.5
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• pp.915-922
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• 2017

The rapid increase in users of mobile smart devices has greatly expanded their range of activities. Compare to conventional mobile devices, smart devices have higher security requirements because they manage and use various kind of confidential information of the owners. However, the cation schemes provided by conventional smart devices are vulnerable to recent attacks such as shoulder surfing, recording, and smudge attacks, which are the social engineering attacks among the types of security attacks targeting the smart devices. In this paper, we propose a novel authentication method that is robust against social engineering attacks but sufficiently considering user's convenience. The proposed method is robust by using combination of a graphical authentication method and a text-based authentication method. Furthermore, our method is easier to memorize the password compare to the conventional graphical authentication methods.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.34-39
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• 2009

Four-digit PIN(Personal Identification Number) is a well-known user authentication method used for many applications including ATMs and mobile phones. However, it is vulnerable to shoulder surfing attacks(SSAs). In this paper, we present new PIN entry methods which are secure against SSA and easy to use. We compare the usability and security of these methods with those of the existing methods.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.312-313
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• 2012

요즈음 스마트폰의 사용이 증가함에 따라 스마트폰 보안이 매우 중요한 주제가 되었다. 이 논문에서는 여러 가지 공격에 안전한 스마트폰의 회전식 그래픽 패스워드를 제안한다. 제안된 그래픽 패스워드 방식은 사용자의 편의성을 증대시키고 스머지(Smudge) 공격, 무작위 공격, 훔쳐보기 공격 등에 안전하기 때문에 스마트폰의 보안성을 높일 수 있다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.4
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• pp.903-910
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• 2016

As smart mobile devices having touchscreens are growingly deployed, a pattern lock system, which is one of the graphical password systems, has become a major authentication mechanism. However, a user's unlocking behaviour leaves smudges on a touchscreen and they are vulnerable to the so-called smudge attacks. Smudges can help an adversary guess a secret pattern correctly. Several advanced pattern lock systems, such as TinyLock, have been developed to resist the smudge attacks. In this paper, we study an automated smudge attack that employs machine learning techniques and its effectiveness in comparison to the human-only smudge attacks. We also compare Android pattern lock and TinyLock schemes in terms of security. Our study shows that the automated smudge attacks are significantly advanced to the human-only attacks with regard to a success ratio, and though the TinyLock system is more secure than the Android pattern lock system.