An efficient e-mail filtering using time priority measurement

Kadoya, Yuki; Fuketa, Masao; Atlam, El‐Sayed; Morita, Ken; Kashiji, Shinkaku; Aoe, Jun-ichi

doi:10.1016/j.ins.2003.12.003

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…Also, Oda and White proposed an artificial immune system using several methods to classify messages and to protect e-mail users from spam [19]. Kadoya et al [11] and Sumitomo et al [22] defined multi-attribute rules to measure the complex time priority for e-mail filtering. Pattern machine learning methods were used in their method that aims at avoiding messages missed during the e-mail detection.…”

Section: E-mail Bombsmentioning

confidence: 99%

An intelligent method to block e-mail bombs

Chang

2007

Appl Intell

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It is hard to block e-mail bombs because they are usually sent by normal SMTP (Simple Mail Transfer Protocol) applications with fake mail sender addresses and IP addresses. Fortunately, original network packets contain real IP address information anyway. Collecting and analyzing these packet contents can help an administrator to realize where the e-mail bombs are coming from and block them. This article presents a simple method that uses a bandwidth manager device to collect and analyze packets to get e-mail bombs information as well as to block e-mail bomb source IP addresses in routers. In practical application experiences at the computer center in a university, this method blocked e-mail bombs simply and effectively. Furthermore, a fuzzy inference system was also designed to help identify e-mail bombs. Its fuzzy membership functions could be adapted using the fuzzy neural network learning method. In brief, the proposed method affords an automatic and adaptable alarm to find e-mail bombs.

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Section: E-mail Bombsmentioning

confidence: 99%

An intelligent method to block e-mail bombs

Chang

2007

Appl Intell

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“…To date, a variety of recommendation techniques have been developed. Of these, collaborative filtering (CF) has been the most successful [2,19,21,29,31,57,58,60]. It identifies customers whose tastes are similar to those of the target customer and recommends items that those customers have liked.…”

Section: Introductionmentioning

confidence: 99%

Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations

Lee

Cho

Kim

2010

Information Sciences

204

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“…Among the technologies proposed, collaborative filtering (CF) [4,6,11,17,20] has proved to be one of the most effective for its simplicity in both theory and implementation. Unlike other content-based information-filtering techniques [8,9,23], the key idea of CF is that a user is likely to prefer items that other users with similar interests prefer. Owing to the different techniques used to describe and calculate the similarities between users, CF algorithms are normally categorized into two general classes [1]: memory-based and model-based algorithms.…”

Section: Introductionmentioning

confidence: 99%