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Evaluation of Spam Detection and Prevention Frameworks for Email and Image Spam - A State of Art. Pedram Hayati, Vidyasagar Potdar Digital Ecosystems and Business Intelligence (DEBI) Institute Curtin University of Technology http://debii.curtin.edu.au/~pedram/

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evaluation of spam detection and prevention frameworks for email and image spam a state of art

Evaluation of Spam Detection and Prevention Frameworks for Email and Image Spam - A State of Art

Pedram Hayati, Vidyasagar Potdar

Digital Ecosystems and Business Intelligence (DEBI) Institute

Curtin University of Technology

http://debii.curtin.edu.au/~pedram/

http://debii.curtin.edu.au/~vidy/

outline
Outline
  • INTRODUCTION
  • SPAMMING MOTIVATIONS
  • ANTI-SPAM STRATEGIES
  • EMAIL SPAM
    • EMAIL SPAM METHODS
    • EMAIL SPAM SURVEY
  • IMAGE SPAM
    • IMAGE SPAM METHODS
    • IMAGE SPAM SURVER
  • CONCLOUSION
introduction
INTRODUCTION
  • Rapid adoption of the Internet
  • The ease with which content can be generated and published has also made it easier to create spam.
  • Spam can be simply stated as information which does not add value to the web user
  • E.g. inappropriate, unsolicited, repeated and irrelevant content in email messages, search results, blogs, forums, social communities and product reviews.
  • the aim of this paper is to survey the current literature in the field of anti-spam with focus on specific anti-spam techniques used in email spam and image spam.
spamming motivations
SPAMMING MOTIVATIONS
  • Revenue Generation
    • publishing advertisements on websites. Google AdSense™
  • Higher Search Engine Ranking
    • incorporate search engine optimization techniques to get their website a higher rank in search results
  • Promoting Products and Services
    • spammers are paid by companies to promote their products or services
  • Stealing Information
    • setup hidden programs in user’s computers to gain back door entry
  • Phishing
    • to steal sensitive information (such as credit card numbers, password, etc.)
anti spam strategies
ANTI-SPAM STRATEGIES
  • Spam Detection Strategy
    • Try to identify the likelihood of spam in a system either automatically or manually
  • Spam Prevention Strategy
    • Deal with the problem of spam in different way. In this strategy, system designers create challenges for the spammers and make spamming a difficult task
anti spam strategies1
ANTI-SPAM STRATEGIES

Spam Detection Strategy

Spam Prevention Strategy

email spam
EMAIL SPAM
  • Email spam refers to sending irrelevant, inappropriate and unsolicited email messages to numerous people
  • Low entrance barrier and low cost of sending emails, which makes it one of the most popular forms of spam
  • The purpose of email spam is advertising, promotion, and spreading backdoors or malicious programs
email spam survey
EMAIL SPAM SURVEY
  • Majority of methods are detection based.
  • Majority is not suitable for non-English language.
  • Behaviour-based methods can not detect spam on the fly and spammers can manipulate their behavior to game the system easily.
image spam
IMAGE SPAM
  • Spammers put their spam content into the images.
  • They embed text such as advertisement text in the images and attach these images to emails.
  • Anti-spam filters that analyse content of email cannot detect spam text in images
image spam survey
IMAGE SPAM SURVEY
  • OSR-based methods are vulnerable to content obscuring tactics and reasonably slow.
  • Meta-based methods can be easily exploit by changing image file attributes.
  • Template-based methods are vulnerable to real world images.
conclousion
CONCLOUSION
  • Behaviour-based methods need time to create user profile can not detect spam on the fly.
  • Needed methods to detect spam on non-English content.
  • OSR-based methods are slow and vulnerable.
  • Template-based methods are vulnerable to real world images