Abstract: Spam is any unwanted electronic message or material
in any form posted too many people. As the world is growing as
global world, social networking sites play an important role in
making world global providing people from different parts of the
world a platform to meet and express their views. Among different
social networking sites Facebook become the leading one. With
increase in usage different users start abusive use of Facebook by
posting or creating ways to post spam. This paper highlights the
potential spam types nowadays Facebook users’ faces. This paper
also provide the reason how user become victim to spam attack. A
methodology is proposed in the end discusses how to handle different
types of spam.
Abstract: Image spam is a kind of email spam where the spam
text is embedded with an image. It is a new spamming technique
being used by spammers to send their messages to bulk of internet
users. Spam email has become a big problem in the lives of internet
users, causing time consumption and economic losses. The main
objective of this paper is to detect the image spam by using histogram
properties of an image. Though there are many techniques to
automatically detect and avoid this problem, spammers employing
new tricks to bypass those techniques, as a result those techniques are
inefficient to detect the spam mails. In this paper we have proposed a
new method to detect the image spam. Here the image features are
extracted by using RGB histogram, HSV histogram and combination
of both RGB and HSV histogram. Based on the optimized image
feature set classification is done by using k- Nearest Neighbor(k-NN)
algorithm. Experimental result shows that our method has achieved
better accuracy. From the result it is known that combination of RGB
and HSV histogram with k-NN algorithm gives the best accuracy in
spam detection.
Abstract: Due to the constant increase in the volume of information available to applications in fields varying from medical diagnosis to web search engines, accurate support of similarity becomes an important task. This is also the case of spam filtering techniques where the similarities between the known and incoming messages are the fundaments of making the spam/not spam decision. We present a novel approach to filtering based solely on layout, whose goal is not only to correctly identify spam, but also warn about major emerging threats. We propose a mathematical formulation of the email message layout and based on it we elaborate an algorithm to separate different types of emails and find the new, numerically relevant spam types.