The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
O tráfego anormal que causa vários problemas na Internet, como fluxos P2P, ataques DDoS e worms da Internet, está aumentando; portanto, a importância de métodos que identifiquem e controlem o tráfego anormal também está aumentando. Embora a aplicação de técnicas de mineração de conjuntos de itens frequentes seja uma forma promissora de analisar o tráfego da Internet, a enorme quantidade de dados na Internet impede que tais técnicas sejam eficazes. Para superar esse problema, desenvolvemos um método simples de mineração frequente de conjuntos de itens que usa apenas uma pequena quantidade de memória, mas é eficaz mesmo com grandes volumes de dados associados ao tráfego de Internet de banda larga. A utilização do nosso método também envolve a análise do número de elementos distintos nos conjuntos de itens encontrados, o que ajuda a identificar tráfego anormal. Usamos uma implementação de nosso método baseada em cache para analisar dados reais na Internet e demonstramos que tal implementação pode ser usada para fornecer análise de dados on-line usando apenas uma pequena quantidade de memória.
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Yusuke SHOMURA, Yoshinori WATANABE, Kenichi YOSHIDA, "Analyzing the Number of Varieties in Frequently Found Flows" in IEICE TRANSACTIONS on Communications,
vol. E91-B, no. 6, pp. 1896-1905, June 2008, doi: 10.1093/ietcom/e91-b.6.1896.
Abstract: Abnormal traffic that causes various problems on the Internet, such as P2P flows, DDoS attacks, and Internet worms, is increasing; therefore, the importance of methods that identify and control abnormal traffic is also increasing. Though the application of frequent-itemset-mining techniques is a promising way to analyze Internet traffic, the huge amount of data on the Internet prevents such techniques from being effective. To overcome this problem, we have developed a simple frequent-itemset-mining method that uses only a small amount of memory but is effective even with the large volumes of data associated with broadband Internet traffic. Using our method also involves analyzing the number of distinct elements in the itemsets found, which helps identify abnormal traffic. We used a cache-based implementation of our method to analyze actual data on the Internet and demonstrated that such an implementation can be used to provide on-line analysis of data while using only a small amount of memory.
URL: https://global.ieice.org/en_transactions/communications/10.1093/ietcom/e91-b.6.1896/_p
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@ARTICLE{e91-b_6_1896,
author={Yusuke SHOMURA, Yoshinori WATANABE, Kenichi YOSHIDA, },
journal={IEICE TRANSACTIONS on Communications},
title={Analyzing the Number of Varieties in Frequently Found Flows},
year={2008},
volume={E91-B},
number={6},
pages={1896-1905},
abstract={Abnormal traffic that causes various problems on the Internet, such as P2P flows, DDoS attacks, and Internet worms, is increasing; therefore, the importance of methods that identify and control abnormal traffic is also increasing. Though the application of frequent-itemset-mining techniques is a promising way to analyze Internet traffic, the huge amount of data on the Internet prevents such techniques from being effective. To overcome this problem, we have developed a simple frequent-itemset-mining method that uses only a small amount of memory but is effective even with the large volumes of data associated with broadband Internet traffic. Using our method also involves analyzing the number of distinct elements in the itemsets found, which helps identify abnormal traffic. We used a cache-based implementation of our method to analyze actual data on the Internet and demonstrated that such an implementation can be used to provide on-line analysis of data while using only a small amount of memory.},
keywords={},
doi={10.1093/ietcom/e91-b.6.1896},
ISSN={1745-1345},
month={June},}
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TY - JOUR
TI - Analyzing the Number of Varieties in Frequently Found Flows
T2 - IEICE TRANSACTIONS on Communications
SP - 1896
EP - 1905
AU - Yusuke SHOMURA
AU - Yoshinori WATANABE
AU - Kenichi YOSHIDA
PY - 2008
DO - 10.1093/ietcom/e91-b.6.1896
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E91-B
IS - 6
JA - IEICE TRANSACTIONS on Communications
Y1 - June 2008
AB - Abnormal traffic that causes various problems on the Internet, such as P2P flows, DDoS attacks, and Internet worms, is increasing; therefore, the importance of methods that identify and control abnormal traffic is also increasing. Though the application of frequent-itemset-mining techniques is a promising way to analyze Internet traffic, the huge amount of data on the Internet prevents such techniques from being effective. To overcome this problem, we have developed a simple frequent-itemset-mining method that uses only a small amount of memory but is effective even with the large volumes of data associated with broadband Internet traffic. Using our method also involves analyzing the number of distinct elements in the itemsets found, which helps identify abnormal traffic. We used a cache-based implementation of our method to analyze actual data on the Internet and demonstrated that such an implementation can be used to provide on-line analysis of data while using only a small amount of memory.
ER -