Fuzzy granular classifier approach for spam detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F17%3A50005536" target="_blank" >RIV/62690094:18450/17:50005536 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.3233/JIFS-169133" target="_blank" >http://dx.doi.org/10.3233/JIFS-169133</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3233/JIFS-169133" target="_blank" >10.3233/JIFS-169133</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fuzzy granular classifier approach for spam detection

Popis výsledku v původním jazyce

Spam email problem is a major shortcoming of email technology for computer security. In this research, a granular classifier model is proposed to discover hyper-boxes in the geometry of information granules for spam detection in three steps. In the first step, the k-means clustering algorithm is applied to find the seed_points to build the granular structure of the spam and non-spam patterns. Moreover, the key part of the spam and non-spam classifiers’ structure is captured by applying the interval analysis through the high homogeneity of the patterns. In the second step, PSO algorithm is hybridized with the k-means to optimize the formalized information granules’ performance. The size of the hyperboxes is expanded away from the center of the granules by PSO. There are some patterns that do not placed in any of the created clusters and known as noise points. In the third step, the membership function in fuzzy sets is applied to solve the noise points’ problem by allocating the noise points through the membership grades. The proposed model is evaluated based on the accuracy, misclassification and coverage criteria. Experimental results reveal that the performance of our proposed model is increased through applying Particle Swarm Optimization and fuzzy set.

Název v anglickém jazyce

Fuzzy granular classifier approach for spam detection

Popis výsledku anglicky

Spam email problem is a major shortcoming of email technology for computer security. In this research, a granular classifier model is proposed to discover hyper-boxes in the geometry of information granules for spam detection in three steps. In the first step, the k-means clustering algorithm is applied to find the seed_points to build the granular structure of the spam and non-spam patterns. Moreover, the key part of the spam and non-spam classifiers’ structure is captured by applying the interval analysis through the high homogeneity of the patterns. In the second step, PSO algorithm is hybridized with the k-means to optimize the formalized information granules’ performance. The size of the hyperboxes is expanded away from the center of the granules by PSO. There are some patterns that do not placed in any of the created clusters and known as noise points. In the third step, the membership function in fuzzy sets is applied to solve the noise points’ problem by allocating the noise points through the membership grades. The proposed model is evaluated based on the accuracy, misclassification and coverage criteria. Experimental results reveal that the performance of our proposed model is increased through applying Particle Swarm Optimization and fuzzy set.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of intelligent and fuzzy systems

ISSN

1064-1246

e-ISSN

—

Svazek periodika

32

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

9

Strana od-do

1355-1363

Kód UT WoS článku

000395520700019

EID výsledku v databázi Scopus

—