@article {28, title = {Behavior-based Adaptive Call Predictor}, journal = {ACM Trans. Auton. Adapt. Syst.}, volume = {6}, year = {2011}, pages = {21:1{\textendash}21:28}, abstract = {

Predicting future calls can be the next advanced feature of the next-generation telecommunication networks as the service providers are looking to offer new services to their customers. Call prediction can be useful to many applications such as planning daily schedules, avoiding unwanted communications (e.g. voice spam), and resource planning in call centers. Predicting calls is a very challenging task. We believe that this is an emerging area of research in ambient intelligence where the electronic devices are sensitive and responsive to people{\textquoteright}s needs and behavior. In particular, we believe that the results of this research will lead to higher productivity and quality of life. In this article, we present a Call Predictor (CP) that offers two new advanced features for the next-generation phones namely {\textquotedblleft}Incoming Call Forecast{\textquotedblright} and {\textquotedblleft}Intelligent Address Book.{\textquotedblright} For the Incoming Call Forecast, the CP makes the next-24-hour incoming call prediction based on recent caller{\textquoteright}s behavior and reciprocity. For the Intelligent Address Book, the CP generates a list of most likely contacts/numbers to be dialed at any given time based on the user{\textquoteright}s behavior and reciprocity. The CP consists of two major components: Probability Estimator (PE) and Trend Detector (TD). The PE computes the probability of receiving/initiating a call based on the caller/user{\textquoteright}s calling behavior and reciprocity. We show that the recent trend of the caller/user{\textquoteright}s calling pattern has higher correlation to the future pattern than the pattern derived from the entire historical data. The TD detects the recent trend of the caller/user{\textquoteright}s calling pattern and computes the adequacy of historical data in terms of reversed time (time that runs towards the past) based on a trace distance. The recent behavior detection mechanism allows CP to adapt its computation in response to the new calling behaviors. Therefore, CP is adaptive to the recent behavior. For our analysis, we use the real-life call logs of 94 mobile phone users over nine months, which were collected by the Reality Mining Project group at MIT. The performance of the CP is validated for two months based on seven months of training data. The experimental results show that the CP performs reasonably well as an incoming call predictor (Incoming Call Forecast) with false positive rate of 8\%, false negative rate of 1\%, and error rate of 9\%, and as an outgoing call predictor (Intelligent Address Book) with the accuracy of 70\% when the list has five entries. The functionality of the CP can be useful in assisting its user in carrying out everyday life activities such as scheduling daily plans by using the Incoming Call Forecast, and saving time from searching for the phone number in a typically lengthy contact book by using the Intelligent Address Book. Furthermore, we describe other useful applications of CP besides its own aforementioned features including Call Firewall and Call Reminder.

}, keywords = {behavior, call logs, call matrix, convergence time, Prediction, trace distance}, issn = {1556-4665}, doi = {10.1145/2019583.2019588}, url = {http://doi.acm.org/10.1145/2019583.2019588}, author = {Phithakkitnukoon, Santi and Ram Dantu and Claxton, Rob and Eagle, Nathan} } @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 = {

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\%.

}, 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 = {

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.

}, 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.} }