• Title/Summary/Keyword: Prefix Vector

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Balanced Binary Search Using Prefix Vector for IP Address Lookup (프리픽스 벡터를 사용한 균형 이진 IP 주소 검색 구조)

  • Kim, Hyeong-Gee;Lim, Hye-Sook
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.33 no.5B
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    • pp.285-295
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    • 2008
  • Internet routers perform packet forwarding which determines a next hop for each incoming packet using the packet's destination IP address. IP address lookup becomes one of the major challenges because it should be performed in wire-speed for every incoming packet under the circumstance of the advancement in link technologies and the growth of the number of the Internet users. Many binary search algorithms have been proposed for fast IP address lookup. However, tree-based binary search algorithms are usually unbalanced, and they do not provide very good search performance. Even for binary search algorithms providing balanced search, they have drawbacks requiring prefix duplication. In this paper, a new binary search algorithm which provides the balanced binary search and the number of its entries is much less than the number of original prefixes. This is possible because of composing the binary search tree only with disjoint prefixes of the prefix set. Each node has a prefix vector that has the prefix nesting information. The number of memory accesses of the proposed algorithm becomes much less than that of prior binary search algorithms, and hence its performance for IP address lookup is considerably improved.

A Subsequence Matching Technique that Supports Time Warping Efficiently (타임 워핑을 지원하는 효율적인 서브시퀀스 매칭 기법)

  • Park, Sang-Hyun;Kim, Sang-Wook;Cho, June-Suh;Lee, Hoen-Gil
    • Journal of Industrial Technology
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    • v.21 no.A
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    • pp.167-179
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    • 2001
  • This paper discusses an index-based subsequence matching that supports time warping in large sequence databases. Time warping enables finding sequences with similar patterns even when they are of different lengths. In earlier work, we suggested an efficient method for whole matching under time warping. This method constructs a multidimensional index on a set of feature vectors, which are invariant to time warping, from data sequences. For filtering at feature space, it also applies a lower-bound function, which consistently underestimates the time warping distance as well as satisfies the triangular inequality. In this paper, we incorporate the prefix-querying approach based on sliding windows into the earlier approach. For indexing, we extract a feature vector from every subsequence inside a sliding window and construct a multi-dimensional index using a feature vector as indexing attributes. For query precessing, we perform a series of index searches using the feature vectors of qualifying query prefixes. Our approach provides effective and scalable subsequence matching even with a large volume of a database. We also prove that our approach does not incur false dismissal. To verily the superiority of our method, we perform extensive experiments. The results reseal that our method achieves significant speedup with real-world S&P 500 stock data and with very large synthetic data.

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Malicious URL Detection by Visual Characteristics with Machine Learning: Roles of HTTPS (시각적 특징과 머신 러닝으로 악성 URL 구분: HTTPS의 역할)

  • Sung-Won HONG;Min-Soo KANG
    • Journal of Korean Artificial Intelligence Association
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    • v.1 no.2
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    • pp.1-6
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    • 2023
  • In this paper, we present a new method for classifying malicious URLs to reduce cases of learning difficulties due to unfamiliar and difficult terms related to information protection. This study plans to extract only visually distinguishable features within the URL structure and compare them through map learning algorithms, and to compare the contribution values of the best map learning algorithm methods to extract features that have the most impact on classifying malicious URLs. As research data, Kaggle used data that classified 7,046 malicious URLs and 7.046 normal URLs. As a result of the study, among the three supervised learning algorithms used (Decision Tree, Support Vector Machine, and Logistic Regression), the Decision Tree algorithm showed the best performance with 83% accuracy, 83.1% F1-score and 83.6% Recall values. It was confirmed that the contribution value of https is the highest among whether to use https, sub domain, and prefix and suffix, which can be visually distinguished through the feature contribution of Decision Tree. Although it has been difficult to learn unfamiliar and difficult terms so far, this study will be able to provide an intuitive judgment method without explanation of the terms and prove its usefulness in the field of malicious URL detection.

A Weighted Block Adaptive Estimation for STBC Single-Carrier System in Frequency-Selective Time-Varying Channels (다중 경로 시변 채널 환경에서 시공간 블록 부호 단일 반송파 시스템을 위한 가중치 블록 적응형 채널 추정 알고리즘)

  • Baek, Jong-Seob;Kwon, Hyuk-Jae;Seo, Jong-Soo
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.32 no.3C
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    • pp.338-347
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    • 2007
  • In this paper, a weighted block adaptive channel estimation (WBA-CE) for a space-time block-coded (STBC) single-carrier transmission with a cyclic-prefix is proposed. In operation of the WBA-CE, a STBC matrix-wise block for filter input symbols is first formulated. Applying a weighted a posteriori error vector-based least-square (LS) criterion for this block, the coefficient correction terms of the WBA-CE are then computed. An approximate steady-state excess mean-square error (EMSE) of the WBA-CE for the stationary optimal coefficient is also analyzed. Simulation results show in a time-varying typical urban (TU) channel that the proposed channel estimator provides better bit-error-rate (BER) performances than conventional algorithms such as the NLMS and RLS channel estimators.

An Index-Based Approach for Subsequence Matching Under Time Warping in Sequence Databases (시퀀스 데이터베이스에서 타임 워핑을 지원하는 효과적인 인덱스 기반 서브시퀀스 매칭)

  • Park, Sang-Hyeon;Kim, Sang-Uk;Jo, Jun-Seo;Lee, Heon-Gil
    • The KIPS Transactions:PartD
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    • v.9D no.2
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    • pp.173-184
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    • 2002
  • This paper discuss an index-based subsequence matching that supports time warping in large sequence databases. Time warping enables finding sequences with similar patterns even when they are of different lengths. In earlier work, Kim et al. suggested an efficient method for whole matching under time warping. This method constructs a multidimensional index on a set of feature vectors, which are invariant to time warping, from data sequences. For filtering at feature space, it also applies a lower-bound function, which consistently underestimates the time warping distance as well as satisfies the triangular inequality. In this paper, we incorporate the prefix-querying approach based on sliding windows into the earlier approach. For indexing, we extract a feature vector from every subsequence inside a sliding window and construct a multidimensional index using a feature vector as indexing attributes. For query processing, we perform a series of index searches using the feature vectors of qualifying query prefixes. Our approach provides effective and scalable subsequence matching even with a large volume of a database. We also prove that our approach does not incur false dismissal. To verify the superiority of our approach, we perform extensive experiments. The results reveal that our approach achieves significant speedup with real-world S&P 500 stock data and with very large synthetic data.