3314ijnlc13.pdf - See discussions stats and author profiles...

This preview shows page 1 - 3 out of 13 pages.

See discussions, stats, and author profiles for this publication at: Analysis of an Image Spam in Email Based on Content Analysis Article in International Journal on Natural Language Computing · June 2014 DOI: 10.5121/ijnlc.2014.3313 CITATIONS 12 READS 1,056 2 authors , including: Some of the authors of this publication are also working on these related projects: Image spam filter View project Brain Tumer detection View project Vijay Prasad Assam Don Bosco University 7 PUBLICATIONS 40 CITATIONS SEE PROFILE All content following this page was uploaded by Vijay Prasad on 01 December 2014. The user has requested enhancement of the downloaded file.
International Journal on Natural Language Computing (IJNLC) Vol. 3, No.3, June 2014 10.5121/ijnlc.2014.3313 129 A NALYSIS OF AN I MAGE S PAM IN E MAIL B ASED ON C ONTENT A NALYSIS Meghali Das 1 and Vijay Prasad 2 1 Dept. of Computer Science & Engineering and IT, Don Bosco College of Engineering and Technology, Guwahati, India 2 Dept. of Computer Science & Engineering and IT, Don Bosco College of Engineering and Technology, Guwahati, India A BSTRACT Researchers initially have addressed the problem of spam detection as a text classification or categorization problem. However, as spammers’ continue to develop new techniques and the type of email content becomes more disparate, text-based anti-spam approaches alone are not sufficiently enough in preventing spam. In an attempt to defeat the anti-spam development technologies, spammers have recently adopted the image spam trick to make the scrutiny of emails’ body text inefficient. The main idea behind this project is to design a spam detection system. The system will be enabled to analyze the content of emails, in particular the artificially generated image sent as attachment in an email. The system will analyze the image content and classify the embedded image as spam or legitimate hence classify the email accordingly . K EYWORDS Spam Filtering, Image Spam, Content Based Filtering 1. I NTRODUCTION As the scope and use of Internet grows the type of information has been more multimedia enriched to attract larger number of users. Electronic mail is currently the most efficacious and sought-after mode of communication. However, like any other dynamic medium, it is prone to misusage. Such an instance of misuse is the blind posting of unwanted email messages, also known as spam, to large and random recipients. Spam messages are sent using bulk-mailers and address lists gathered from web pages and newsgroup annals. Radicati in the year 2009 estimated that 247 billion email messages were sent per day predicted to double by 2013 [Radicati 2009] [1, 2]. Spam, or unsolicited bulk mail, though sent out in various shape and form, nevertheless, may possess a number of similar characteristics in terms of structure, content, and distribution approaches. The generation and distribution is justified from a spammer’s prospect as the effort and cost involved in sending a large number of emails is minimal and the probable return considering the large number of email users is huge. According to a survey the overall cost

  • Left Quote Icon

    Student Picture

  • Left Quote Icon

    Student Picture

  • Left Quote Icon

    Student Picture