"May I borrow Your Filter?" Exchanging Filters to Combat Spam in a Community

Leveraging social networks in computer systems can be effective in dealing with a number of trust and security issues. Spam is one such issue where the "wisdom of crowds" can be harnessed by mining the collective knowledge of ordinary individuals. In this paper, we present a mechanism through which members of a virtual community can exchange information to combat spam. Previous attempts at collaborative spam filtering have concentrated on digest-based indexing techniques to share digests or fingerprints of emails that are known to be spam. We take a different approach and allow users to share their spam filters instead, thus dramatically reducing the amount of traffic generated in the network. The resultant diversity in the filters and cooperation in a community allows it to respond to spam in an autonomic fashion. As a test case for exchanging filters we use the popular SpamAssassin spam filtering software and show that exchanging spam filters provides an alternative method to improve spam filtering performance

Spam Classification Using Machine Learning Techniques - Sinespam

Most e-mail readers spend a non-trivial amount of time regularly deleting junk e-mail (spam) messages, even as an expanding volume of such e-mail occupies server storage space and consumes network bandwidth. An ongoing challenge, therefore, rests within the development and refinement of automatic classifiers that can distinguish legitimate e-mail from spam. Some published studies have examined spam detectors using Naïve Bayesian approaches and large feature sets of binary attributes that determine the existence of common keywords in spam, and many commercial applications also use Naïve Bayesian techniques. Spammers recognize these attempts to prevent their messages and have developed tactics to circumvent these filters, but these evasive tactics are themselves patterns that human readers can often identify quickly. This work had the objectives of developing an alternative approach using a neural network (NN) classifier brained on a corpus of e-mail messages from several users. The features selection used in this work is one of the major improvements, because the feature set uses descriptive characteristics of words and messages similar to those that a human reader would use to identify spam, and the model to select the best feature set, was based on forward feature selection. Another objective in this work was to improve the spam detection near 95% of accuracy using Artificial Neural Networks; actually nobody has reached more than 89% of accuracy using ANN

Image Spam Classification using Deep Learning

Image classification is a fundamental problem of computer vision and pattern recognition. Spam is unwanted bulk content and image spam is unwanted content embedded inside the images. Image spam creates threat to the email based communication systems. Nowadays, a lot of unsolicited content is circulated over the internet. While a lot of machine learning techniques are successful in detecting textual based spam, this is not the case for image spams, which can easily evade these textual-spam detection systems. In this project, we explore and evaluate four deep learning techniques that detect image spams. First, we study neural networks and the deep neural networks, which we train on various image features. We explore their robustness on an improved dataset, which was especially build in order to outsmart current image spam detection techniques. Finally, we design two convolution neural network architectures and provide experimental results for these alongside the existing VGG19 transfer learning model for detecting image spams. Our work offers a new tool for detecting image spams and is compared against recent related tools

GridEmail: A Case for Economically Regulated Internet-based Interpersonal Communications

Email has emerged as a dominant form of electronic communication between people. Spam is a major problem for email users, with estimates of up to 56% of email falling into that category. Control of Spam is being attempted with technical and legislative methods. In this paper we look at email and spam from a supply-demand perspective. We propose Gridemail, an email system based on an economy of communicating parties, where participants? motivations are represented as pricing policies and profiles. This system is expected to help people regulate their personal communications to suit their conditions, and help in removing unwanted messages.Comment: 15 pages, 10 figures, A Technical Report from Grid Computing and Distributed Systems Laboratory, University of Melbourne, Australi