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De novo 시퀀스 어셈블리의 overlap 단계의 최근 연구 실험 분석
임지혁(Jihyuk Lim),김선(Sun Kim),박근수(Kunsoo Park) Korean Institute of Information Scientists and Eng 2018 정보과학회논문지 Vol.45 No.3
Given a set of DNA read sequences, de novo sequence assembly reconstructs a target sequence without a reference sequence. For reconstruction, the assembly needs the overlap phase, which computes all overlaps between every pair of reads. Since the overlap phase is the most time-consuming part of the whole assembly, the performance of the assembly depends on that of the overlap phase. There have been extensive studies on the overlap phase in various fields. Among them, three state-of-the-art results for the overlap phase are Readjoiner, SOF, and Lim-Park algorithm. Recently, a rapid development of sequencing technology has made it possible to produce a large read dataset at a low cost, and many platforms for generating a DNA read dataset have been developed. Since the platforms produce datasets with different statistical characteristics, a performance evaluation for the overlap phase should consider datasets with these characteristics. In this paper, we compare and analyze the performances of the three algorithms with various large datasets.
X-means 클러스터링을 이용한 악성 트래픽 탐지 방법
한명지(Myoungji Han),임지혁(Jihyuk Lim),최준용(Junyong Choi),김현준(Hyunjoon Kim),서정주(Jungjoo Seo),유철(Cheol Yu),김성렬(Sung-Ryul Kim),박근수(Kunsoo Park) Korean Institute of Information Scientists and Eng 2014 정보과학회논문지 Vol.41 No.9
Malicious traffic, such as DDoS attack and botnet communications, refers to traffic that is generated for the purpose of disturbing internet networks or harming certain networks, servers, or hosts. As malicious traffic has been constantly evolving in terms of both quality and quantity, there have been many researches fighting against it. In this paper, we propose an effective malicious traffic detection method that exploits the X-means clustering algorithm. We also suggest how to analyze statistical characteristics of malicious traffic and to define metrics that are used when clustering. Finally, we verify effectiveness of our method by experiments with two released traffic data.