Spam detection based on nearest community classifier

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86096394" target="_blank" >RIV/61989100:27240/15:86096394 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/15:86096394

Výsledek na webu

<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7312096&newsearch=true&queryText=Spam%20detection%20based%20on%20nearest%20community%20classifier" target="_blank" >http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7312096&newsearch=true&queryText=Spam%20detection%20based%20on%20nearest%20community%20classifier</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/INCoS.2015.75" target="_blank" >10.1109/INCoS.2015.75</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Spam detection based on nearest community classifier

Popis výsledku v původním jazyce

Undesirable emails (spam) are increasingly becoming a big problem nowadays, not only for users, but also for Internet service providers. Therefore, the design of new algorithms detecting the spam is currently one of the research hot-topics. We define tworequirements and use them simultaneously. The first requirement is a low rate of falsely detected emails which has an impact on the algorithm performance. The second requirement is a fast detection of spams. It minimizes the delay in receiving emails. In this paper, we focus our effort on the first requirement. To solve this problem we applied network community analysis. The approach is to find communities-groups of same emails. In this paper, we present a new nearest community classifier and apply itin the field of spam detection. The obtained results are very close to Bayesian Spam Filter. We achieved 93.78% accuracy. The algorithm can detect 80.72% of spam emails and 98.01% non-spam emails.

Název v anglickém jazyce

Spam detection based on nearest community classifier

Popis výsledku anglicky

Undesirable emails (spam) are increasingly becoming a big problem nowadays, not only for users, but also for Internet service providers. Therefore, the design of new algorithms detecting the spam is currently one of the research hot-topics. We define tworequirements and use them simultaneously. The first requirement is a low rate of falsely detected emails which has an impact on the algorithm performance. The second requirement is a fast detection of spams. It minimizes the delay in receiving emails. In this paper, we focus our effort on the first requirement. To solve this problem we applied network community analysis. The approach is to find communities-groups of same emails. In this paper, we present a new nearest community classifier and apply itin the field of spam detection. The obtained results are very close to Bayesian Spam Filter. We achieved 93.78% accuracy. The algorithm can detect 80.72% of spam emails and 98.01% non-spam emails.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

<a href="/cs/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: Centrum excelence IT4Innovations</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Intelligent Networking and Collaborative Systems INCoS-2015 : 7th International Conference : proceedings : September 2-4, 2015, Taipei, Tchaj-wan

ISBN

978-1-4673-7694-5

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

354-359

Název nakladatele

IEEE

Místo vydání

Vienna

Místo konání akce

Taipei

Datum konání akce

2. 9. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—