With the amount of email spam received these days it is hard to imagine that spammers act individually. Nowadays, most of the spam emails have been sent from a collection of compromised machines controlled by some spammers. These compromised computers are often called bots, using which the spammers can send massive volume of spam within a short period of time. The motivation of this work is to understand and analyze the behavior of spammers through a large collection of spam mails. My research examined a the data set collected over a 2.5-year period and developed an algorithm which would give the botnet features and then classify them into various groups. Principal component analysis was used to study the association patterns of group of spammers and the individual behavior of a spammer in a given domain. This is based on the features which capture maximum variance of information we have clustered. Presence information is a growing tool towards more efficient communication and providing new services and features within a business setting and much more. The main contribution in my thesis is to propose the willingness estimator that can estimate the callee's willingness without his/her involvement, the model estimates willingness level based on call history. Finally, the accuracy of the proposed willingness estimator is validated with the actual call logs.

The advent of the Internet has generated a proliferation of online information-rich environments, which provide information consumers with an unprecedented amount of freely available information. However, the openness of these environments has also made them vulnerable to a new class of attacks called Denial of Information (DoI) attacks. Attackers launch these attacks by deliberately inserting low quality information into information-rich environments to promote that information or to deny access to high quality information. These attacks directly threaten the usefulness and dependability of online information-rich environments, and as a result, an important research question is how to automatically identify and remove this low quality information from these environments. The first contribution of this thesis research is a set of techniques for automatically recognizing and countering various forms of DoI attacks in email systems. We develop a new DoI attack based on camouflaged messages, and we show that spam producers and information consumers are entrenched in a spam arms race. To break free of this arms race, we propose two solutions. One solution involves refining the statistical learning process by associating disproportionate weights to spam and legitimate features, and the other solution leverages the existence of non-textual email features (e.g., URLs) to make the classification process more resilient against attacks. The second contribution of this thesis is a framework for collecting, analyzing, and classifying examples of DoI attacks in the World Wide Web. We propose a fully automatic Web spam collection technique and use it to create the Webb Spam Corpus -- a first-of-its-kind, large-scale, and publicly available Web spam data set. Then, we perform the first large-scale characterization of Web spam using content and HTTP session analysis. Next, we present a lightweight, predictive approach to Web spam classification that relies exclusively on HTTP session information. The final contribution of this thesis research is a collection of techniques that detect and help prevent DoI attacks within social environments. First, we provide detailed descriptions for each of these attacks. Then, we propose a novel technique for capturing examples of social spam, and we use our collected data to perform the first characterization of social spammers and their behaviors.

The motivation of this work is to understand E-shape analysis and how it can be applied to various classification tasks. It has a powerful feature to not only look at what information is contained, but rather how that information looks. This new technique gives E-shape analysis the ability to be language independent and to some extent size independent. In this thesis, I present a new mechanism to characterize an email without using content or context called E-shape analysis for email. I explore the applications of the email shape by carrying out a case study; botnet detection and two possible applications: spam filtering and social-context based finger printing. The second part of this thesis takes what I apply E-shape analysis to activity recognition of humans. Using the Android platform and a T-Mobile G1 phone I collect data from the triaxial accelerometer and use it to classify the motion behavior of a subject.