Evaluation of Spam Detection and Prevention Frameworks for and Image Spam - A State of Art Pedram Hayati, Vidyasagar Potdar Digital Ecosystems and Business Intelligence (DEBI) Institute Curtin University of Technology

Outline INTRODUCTION SPAMMING MOTIVATIONS ANTI-SPAM STRATEGIES SPAM – SPAM METHODS – SPAM SURVEY IMAGE SPAM –IMAGE SPAM METHODS –IMAGE SPAM SURVER CONCLOUSION

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 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 spam and image spam.

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 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 STRATEGIES Spam Detection StrategySpam Prevention Strategy

SPAM spam refers to sending irrelevant, inappropriate and unsolicited messages to numerous people Low entrance barrier and low cost of sending s, which makes it one of the most popular forms of spam The purpose of spam is advertising, promotion, and spreading backdoors or malicious programs

SPAM METHODS

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 Spammers put their spam content into the images. They embed text such as advertisement text in the images and attach these images to s. Anti-spam filters that analyse content of cannot detect spam text in images

IMAGE SPAM METHODS

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 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

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