@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} } @conference {58, title = {Automatic Calibration Using Receiver Operating Characteristics Curves}, booktitle = {2007 2nd International Conference on Communication Systems Software and Middleware}, year = {2007}, month = {Jan}, abstract = {

Application-level filters, such as e-mail and VoIP spam filters, that analyze dynamic behavior changes are replacing static signature-recognition filters. These application-level filters learn behavior and use that knowledge to filter unwanted requests. Because behavior of a service request{\textquoteright}s participating entities changes rapidly, filters must adapt quickly by using end user{\textquoteright}s preferences about receiving that service request message. Many adaptive filters learn from the participating entities{\textquoteright} behavior; however, none configure themselves automatically to an end user{\textquoteright}s changing tolerance levels. Also, filter administrators cannot manually change the threshold for each service request in real time. Traditional adaptive filters fail when administrators must optimize multiple filter thresholds manually and often. Thus, to improve a filter{\textquoteright}s learning, we must automate its threshold-update process. We propose an automatic threshold-calibration mechanism using Receiver Operating Characteristics (ROC) curves that updates the threshold based on an end user{\textquoteright}s feedback. To demonstrate the mechanism{\textquoteright}s real-time applicability, we integrated it in a Voice over IP (VoIP) spam filter that analyzes incoming Spam over IP Telephony (SPIT) calls. Using this mechanism, we observed good improvement in the VoIP spam filter{\textquoteright}s accuracy. Further, computing and updating the optimum threshold in realtime does not impede the filter{\textquoteright}s temporal performance because we update thresholds after each call{\textquoteright}s completion. Because we reach an optimum threshold for any initial setting, this mechanism works efficiently when we cannot predict end-user behavior. Furthermore, automatic calibration proves efficient when using multiple threshold values.

}, keywords = {adaptive filters, application-level filter learning, automatic threshold-calibration mechanism, behavior, calibration, Computer science, Computer worms, Electronic mail, end user feedback, Filtering, information filtering, information filters, Internet telephony, learning (artificial intelligence), Protocols, receiver operating characteristics curves, sensitivity analysis, service request message, telecommunication traffic, Threshold, tolerance, Viruses (medical)}, doi = {10.1109/COMSWA.2007.382484}, author = {Kolan, Prakash and Vaithilingam, Ram and Ram Dantu} } @conference {86, title = {Unwanted SMTP Paths and Relays}, booktitle = {2007 2nd International Conference on Communication Systems Software and Middleware}, year = {2007}, month = {Jan}, abstract = {

Based on the social interactions of an email user, incoming email traffic can be divided into different categories such as, telemarketing, Opt-in family members and friends. Due to a lack of knowledge in the different categories, most of the existing spam filters are prone to high false positives and false negatives. Moreover, a majority of the spammers obfuscate their email content inorder to circumvent the content-based spam filters. However, they do not have access to all the fields in the email header. Our classification method is based on the path traversed by email (instead of content analysis) since we believe that spammers cannot forge all the fields in the email header. We based our classification on three kinds of analyses on the header: i) EndToEnd path analysis, which tries to establish the legitimacy of the path taken by an email and classifies them as either spam or non-spam; ii) Relay analysis, which verifies the trustworthiness of the relays participating in the relaying of emails; iii) Emails wantedness analysis, which measure the recipients wantedness of the senders emails. We use the IMAP message status flags such as, message has been read, deleted, answered, flagged, and draft as an implicit feed back from the user in Emails wantedness analysis. Finally we classify the incoming emails as i) socially close (such as, legitimate emails from family, and friends), ii) socially distinct emails from strangers, iii) spam emails (for example, emails from telemarketers, and spammers) and iv) opt-in emails. Based on the relation between spamminess of the path taken by spam emails and the unwantedness values of the spammers, we classify spammers as i) prospective spammers, ii) suspects, iii) recent spammers and iv) serial spammers. Overall, our method resulted in far less false positives compared to current filters like SpamAssassin. We achieved a precision of 98.65\% which is better than the precisions achieved by SPF and DNSBL blacklists.

}, keywords = {Computer science, content-based spam filters, Counterfeiting, Credit cards, email spam filters, emails wantedness analysis, end-to-end path analysis, Filters, IMAP message status flags, information filtering, information filters, Information security, Legislation, Multimedia communication, relay analysis, Relays, SMTP paths analysis, unsolicited e-mail, Unsolicited electronic mail, Web page design}, doi = {10.1109/COMSWA.2007.382440}, author = {Palla, Srikanth and Ram Dantu} } @article {180, title = {Securing voice over IP}, journal = {IEEE Network}, volume = {20}, year = {2006}, month = {Sept}, pages = {4-5}, keywords = {Authentication, Fingerprint recognition, information filtering, information filters, Information security, Internet telephony, Markup languages, Protection, Protocols, Standardization}, issn = {0890-8044}, doi = {10.1109/MNET.2006.1705876}, author = {Ram Dantu and Ghosal, Dipak and Schulzrinne, Henning} }