@conference {17,
	title = {Behavior analysis of spam botnets},
	booktitle = {2008 3rd International Conference on Communication Systems Software and Middleware and Workshops (COMSWARE {\textquoteright}08)},
	year = {2008},
	abstract = {<p>Compromised computers, known as bots, are the major source of spamming and their detection helps greatly improve control of unwanted traffic. In this work we investigate the behavior patterns of spammers based on their underlying similarities in spamming. To our knowledge, no work has been reported on identifying spam botnets based on spammerspsila temporal characteristics. Our study shows that the relationship among spammers demonstrates highly clustering structures based on features such as content length, time of arrival, frequency of email, active time, inter-arrival time, and content type. Although the dimensions of the collected feature set is low, we perform principal component analysis (PCA) on feature set to identify the features which account for the maximum variance in the spamming patterns. Further, we calculate the proximity between different spammers and classify them into various groups. Each group represents similar proximity. Spammers in the same group inherit similar patterns of spamming a domain. For classification into Botnet groups, we use clustering algorithms such as Hierarchical and K-means.We identify Botnet spammers into a particular group with a precision of 90\%.</p>
},
	keywords = {behavior pattern analysis, Botnets, Classification algorithms, clustering algorithm, Clustering algorithms, Correlation, Electronic mail, feature set, Filtering, information filtering, invasive software, maximum variance, pattern classification, pattern clustering, PCA, principal component analysis, spam filtering, Time frequency analysis, unsolicited e-mail, unwanted traffic control},
	doi = {10.1109/COMSWA.2008.4554418},
	author = {Husna, Husain and Phithakkitnukoon, Santi and Palla, Srikanth and Ram Dantu}
}
@inbook {46,
	title = {Behavioral Entropy of a Cellular Phone User},
	booktitle = {Social Computing, Behavioral Modeling, and Prediction},
	year = {2008},
	pages = {160{\textendash}167},
	publisher = {Springer US},
	organization = {Springer US},
	address = {Boston, MA},
	abstract = {<p>The increase of advanced service offered by cellular networks draws lots of interest from researchers to study the networks and phone user behavior. With the evolution of Voice over IP, cellular phone usage is expected to increase exponentially. In this paper, we analyze the behavior of cellular phone users and identify behavior signatures based on their calling patterns. We quantify and infer the relationship of a person{\textquoteright}s randomness levels using information entropy based on the location of the user, time of the call, inter-connected time, and duration of the call. We use real-life call logs of 94 mobile phone users collected at MIT by the Reality Mining Project group for a period of nine months. We are able to capture the user{\textquoteright}s calling behavior on various parameters and interesting relationship between randomness levels in individual{\textquoteright}s life and calling pattern using correlation coeffi- cients and factor analysis. This study extends our understanding of cellular phone user behavior and characterizes cellular phone users in forms of randomness level.</p>
},
	isbn = {978-0-387-77672-9},
	doi = {10.1007/978-0-387-77672-9_18},
	url = {http://dx.doi.org/10.1007/978-0-387-77672-9_18},
	author = {Phithakkitnukoon, Santi and Husna, Husain and Ram Dantu},
	editor = {Liu, Huan and Salerno, John J. and Young, Michael J.}
}
