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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Le trafic anormal qui provoque divers problèmes sur Internet, tels que les flux P2P, les attaques DDoS et les vers Internet, est en augmentation ; par conséquent, l’importance des méthodes permettant d’identifier et de contrôler le trafic anormal augmente également. Bien que l’application de techniques d’exploration d’ensembles d’éléments fréquents soit une manière prometteuse d’analyser le trafic Internet, l’énorme quantité de données sur Internet empêche ces techniques d’être efficaces. Pour surmonter ce problème, nous avons développé une méthode simple d'exploration d'ensembles d'éléments fréquents qui n'utilise qu'une petite quantité de mémoire mais qui est efficace même avec les grands volumes de données associés au trafic Internet haut débit. Utiliser notre méthode implique également d’analyser le nombre d’éléments distincts dans les itemsets trouvés, ce qui permet d’identifier un trafic anormal. Nous avons utilisé une implémentation basée sur le cache de notre méthode pour analyser des données réelles sur Internet et avons démontré qu'une telle implémentation peut être utilisée pour fournir une analyse en ligne des données tout en utilisant seulement une petite quantité de mémoire.
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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 -