Anti-spam Filter Based on Data Mining and Statistical Test

Lai, Gu-Hsin; Chou, Chao-Wei; Chen, Chia-Mei; Ou, Ya-Hua

doi:10.1007/978-3-642-01209-9_17

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…From a different perspective, some research studies focused their efforts on maintaining those rules generated through the use of RS [36][37][38]. These works proposed different frameworks to share generated rules from servers with the final goal of giving adequate support to a collaborative community interested in spam filtering.…”

Section: Related Work: Applying Rs To Antispam Filteringmentioning

confidence: 99%

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Boosting Accuracy of Classical Machine Learning Antispam Classifiers in Real Scenarios by Applying Rough Set Theory

rez-Díaz

Fdez-Riverola

et al. 2016

Scientific Programming

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Nowadays, spam deliveries represent a major problem to benefit from the wide range of Internet-based communication forms. Despite the existence of different well-known intelligent techniques for fighting spam, only some specific implementations of Naïve Bayes algorithm are finally used in real environments for performance reasons. As long as some of these algorithms suffer from a large number of false positive errors, in this work we propose a rough set postprocessing approach able to significantly improve their accuracy. In order to demonstrate the advantages of the proposed method, we carried out a straightforward study based on a publicly available standard corpus (SpamAssassin), which compares the performance of previously successful well-known antispam classifiers (i.e., Support Vector Machines, AdaBoost, Flexible Bayes, and Naïve Bayes) with and without the application of our developed technique. Results clearly evidence the suitability of our rough set postprocessing approach for increasing the accuracy of previous successful antispam classifiers when working in real scenarios.

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Section: Related Work: Applying Rs To Antispam Filteringmentioning

confidence: 99%

“…Additionally, the work of Lai et al [37] introduces the generation of rules by means of RS, genetic algorithms, and reinforcement learning. Finally, the study carried out by Lai et al [38] proposed novel methods to generate rules and validate their precision.…”

Section: Related Work: Applying Rs To Antispam Filteringmentioning

confidence: 99%

Boosting Accuracy of Classical Machine Learning Antispam Classifiers in Real Scenarios by Applying Rough Set Theory

rez-Díaz

Fdez-Riverola

et al. 2016

Scientific Programming

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Rough sets for spam filtering: Selecting appropriate decision rules for boundary e-mail classification

PéRez-DíAz

Ruano-Ordás

Méndez

et al. 2012

Applied Soft Computing

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New method of detection and wiping of sensitive information

Pecherle

Győrödi

et al. 2011

2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing

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One of the biggest problems in sensitive data wiping is to determine if a file is sensitive or not. Data wiping applications have improved a lot, but they cannot determine by themselves if a file is sensitive. The method we propose tries to determine if a file is sensitive by using a pre-defined set of rules initially specified by the user. These rules can update themselves in time, by "learning" from data patterns in previous sensitive files. The hard drive is monitored continuously to detect new sensitive data. Also, our method replaces the standard delete function from Windows with a secure wiping algorithm (overwriting data several times with pre-defined passes), but only for files that are determined to be sensitive.

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Anti-spam Filter Based on Data Mining and Statistical Test

Cited by 3 publications

References 5 publications

Boosting Accuracy of Classical Machine Learning Antispam Classifiers in Real Scenarios by Applying Rough Set Theory

Boosting Accuracy of Classical Machine Learning Antispam Classifiers in Real Scenarios by Applying Rough Set Theory

Rough sets for spam filtering: Selecting appropriate decision rules for boundary e-mail classification

New method of detection and wiping of sensitive information

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