• Journal of Digital Contents Society
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• v.18 no.3
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• pp.525-534
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• 2017

The open set recognition method should be used for the cases that the classes of test data are not known completely in the training phase. So it is required to include two processes of classification and the validation test. This kind of research is very necessary for commercialization of face recognition modules, but few domestic researches results about it have been published. In this paper, we propose an open set face recognition method that includes two sequential validation phases. In the first phase, with dummy classes we perform classification based on sparse representation. Here, when the test data is classified into a dummy class, we conclude that the data is invalid. If the data is classified into one of the regular training classes, for second validation test we extract four features and apply them for the proposed decision function. In experiments, we proposed a simulation method for open set recognition and showed that the proposed validation test outperform SCI of the well-known validation method

• Proceedings of the Korea Information Processing Society Conference
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• 2000.10a
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• pp.605-608
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• 2000

본 논문에서는 자바로 작성된 에이전트가 신뢰할 수 없는 호스트에서도 자신의 코드와 데이터를 노출시키지 않고 안전하게 수행할 수 있게 하는 3가지 시간제한 블랙박스 보안 방법 즉 변수 분해, 코드 어포스캐이팅 그리고 클래스 지연로딩을 제안한다. 변수 분해는 호스트가 에이전트 수행을 감시하여 에이전트 내의 데이터를 파악하는 것을 어렵게 하며, 코드 어포스캐이팅은 실행코드에 더미코드와 가상코드를 삽입하여 역컴파일 등과 같은 기법으로도 코드의 의미 분석을 힘들게 하여 에이전트를 보호한다. 그리고 클래스 지연로딩은 변수에 접근하는 함수들을 암호화하여 실행시간까지 그 접근 방법의 공개를 차단한다. 제안하는 블랙박스 보안방법들은 호스트가 에이전트 내부를 파악하는데 상당한 시간을 소요하게 하여 적어도 에이전트가 실행되는 동안은 에이전트가 보호되게 하는 보안 방법이다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.3
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• pp.493-501
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• 2020

For the obfuscation of Flow Analysis on the Android operating system, the size of the Flow Graph can be large enough to make analysis difficult. To this end, a library in the form of aar was implemented so that it could be inserted into the application in the form of an external library. The library is designed to have up to five child nodes from the entry point in the dummy code, and for each depth has 2n+1 numbers of methods from 100 to 900 for each node, so it consists of a total of 2,500 entry points. In addition, entry points consist of a total of 150 views in XML, each of which is connected via asynchronous interface. Thus, the process of creating a Inter-procedural Control Flow Graph has a maximum of 14,175E+11 additional cases. As a result of applying this to application, the Inter Procedure Control Flow Analysis too generates an average of 10,931 edges and 3,015 nodes with an average graph size increase of 36.64%. In addition, in the APK analyzing process showed that up to average 76.33MB of overhead, but only 0.88MB of execution overhead in the user's ART environment.