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Hope, PriscillaUsing Artificial Neural Networks to Identify Image Spam
Master of Science, University of Akron, 2008, Computer Science

Internet technology has made international communication easy and convenient. This convenience has compelled a number of people to rely on electronic mail for almost all spheres of life – personal, business etc. Scrupulous organizations/individuals have taken undue advantage of this convenience and populate users’ inboxes with unwanted messages making email spam a menace. Even as anti-spam software producers think they have almost solved the problem, spammers come out with new techniques. One such tactic in the spammers’ toolbox comes in the form of image spam – messages that contain little more than a link to an image rendered in an HTML mail reader. The image typically contains the spam message one hopes to avoid, yet it is able to bypass most filters due to the composition and format of these pictures.

This research focuses on identifying these images as spam by using an artificial neural network (ANN), software programs used for recognizing patterns, based on the biological neural networks in our brains. As information propagates through a neural network, it “learns” about the data. A large collection of both spam and non-spam images have being used to train an ANN, and then test the effectiveness of the trained network against an unidentified or already identified set of pictures. This process involves formatting images and adding the desired training values expected by the ANN. Several different ANNS have being trained using different configurations of hidden layers and nodes per layer. A detailed process for preprocessing spam image files is given, followed by a description on how to train an artificial neural network to distinguish between ham and spam. Finally, the trained network is tested against both known and unknown images.

Committee:

Kathy Liszka, PhD (Advisor); Timothy O’Neil (Other); Tim Marguish (Other)

Subjects:

Computer Science

Keywords:

image spam; FANN; artificial neural networks; using artificial neural networks to identify image spam

Wakade, Shruti VijayClassification of Image Spam
Master of Science, University of Akron, 2011, Computer Science

Image spam is one of the most prevalent forms of spam ever since its inception. Spammers have refined their spamming techniques to use smaller, more colorful and photo quality images as spam. In spite of numerous efforts to build efficient spam filters against e-mail spam by researchers and free-mailing services like yahoo mail, Gmail etc spam filters still fail to arrest image spam. This research is an attempt to understand the techniques used in spamming and identifying a set of features that can help in classification of image spam from photographs.

A set of eight features were identified based on observations and existing research in this area. Among the eight features, six features have been introduced by us and two other features have been included from previous research. Data mining techniques were then applied to classify image spam from photographs. Identifying a set of efficient yet computationally inexpensive features was the objective that guided this research work. We achieved classification accuracy of 89% for the test samples. A detailed trail of image spam has been studied to identify the most prevalent types and patterns in image spam. Our results indicate that five of the six features we had introduced proved to be of high significance in identifying image spam from photographs.

Committee:

Kathy J. Liszka, Dr. (Advisor); Zhong-Hui Duan, Dr. (Committee Member); Chien-Chung Chan, Dr. (Committee Member)

Subjects:

Computer Science

Keywords:

Spam; Image Spam; Data Mining; Classification