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Machine Learning Approaches for Modeling Spammer Behavior

arXiv.org Artificial Intelligence

Spam is commonly known as unsolicited or unwanted email messages in the Internet causing potential threat to Internet Security. Users spend a valuable amount of time deleting spam emails. More importantly, ever increasing spam emails occupy server storage space and consume network bandwidth. Keyword-based spam email filtering strategies will eventually be less successful to model spammer behavior as the spammer constantly changes their tricks to circumvent these filters. The evasive tactics that the spammer uses are patterns and these patterns can be modeled to combat spam. This paper investigates the possibilities of modeling spammer behavioral patterns by well-known classification algorithms such as Na\"ive Bayesian classifier (Na\"ive Bayes), Decision Tree Induction (DTI) and Support Vector Machines (SVMs). Preliminary experimental results demonstrate a promising detection rate of around 92%, which is considerably an enhancement of performance compared to similar spammer behavior modeling research.


Study of Static Classification of Social Spam Profiles in MySpace

AAAI Conferences

Reaching hundreds of millions of users, major social networks have become important target media for spammers. Although practical techniques such as collaborative filters and behavioral analysis are able to reduce spam, they have an inherent lag (to collect sufficient data on the spammer) that also limits their effectiveness. Through an experimental study of over 1.9 million MySpace profiles, we make a case for analysis of static user profile content, possibly as soon as such profiles are created. We compare several machine learning algorithms in their ability to distinguish spam profiles from legitimate profiles. We found that a C4.5 decision tree algorithm achieves the highest accuracy (99.4%) of finding rogue profiles, while naïve Bayes achieves a lower accuracy (92.6%). We also conducted a sensitivity analysis of the algorithms w.r.t. features which may be easily removed by spammers.


Not So Naive Online Bayesian Spam Filter

AAAI Conferences

Spam filtering, as a key problem in electronic communication, has drawn significant attention due to increasingly huge amounts of junk email on the Internet. Content-based filtering is one reliable method in combating with spammers' changing tactics. Naive Bayes (NB) is one of the earliest content-based machine learning methods both in theory and practice in combating with spammers, which is easy to implement while can achieve considerable accuracy. In this paper, the traditional online Bayesian classifier are enhanced  by two ways. First, from theory's point of view, we devise a self-adaptive mechanism to gradually weaken the assumption of independence required by original NB in the online training process, and as a result of that our NSNB is no longer ``naive''. Second, we propose other engineering ways to make the classifier more robust and accuracy. The experiment results show that our NSNB does give state-of-the-art classification performance on online spam filtering on large benchmark data sets while it is extremely fast and takes up little memory in comparison with other statistical methods.


Naive Bayesian Text Classification

AITopics Original Links

Paul Graham popularized the term "Bayesian Classification" (or more accurately "Naïve Bayesian Classification") after his "A Plan for Spam" article was published (http://www.paulgraham.com/spam.html). In fact, text classifiers based on naïve Bayesian and other techniques have been around for many years. Companies such as Autonomy and Interwoven incorporate machine-learning techniques to automatically classify documents of all kinds; one such machine-learning technique is naïve Bayesian text classification. Naïve Bayesian text classifiers are fast, accurate, simple, and easy to implement. In this article, I present a complete naïve Bayesian text classifier written in 100 lines of commented, nonobfuscated Perl.


Visual Spoofing in content based spam detection

arXiv.org Machine Learning

"Subject: Please send money Body: I am so distraught. I thought i could reach out to you to help me out. I came down to United Kingdom for a short vacation unfortunately i was mugged at the park of the hotel i stayed, all cash, credit card and cell phone was stolen from me but luckily for me i still have my passport with me. I've been to the embassy and to the police here but they're not helping issues at all and, my flight leaves in few hours time from now but. I am having problems settling the hotel bills and the hotel manager won't let me leave until i settle my hotel bills. I'm freaked out at the moment." As expected, this email, which definitely seems to be spam, ends up in the junk email folder. However, in this paper we show that visual spoofing achieved by substituting some confusables (characters that look similar) into the above email text will enable the same email to bypass the spam filter. We also propose ways to address this loophole.