@article {37, title = {Nuisance Level of a Voice Call}, journal = {ACM Trans. Multimedia Comput. Commun. Appl.}, volume = {5}, year = {2008}, pages = {6:1{\textendash}6:22}, abstract = {

In our everyday life, we communicate with many people such as family, friends, neighbors, and colleagues. We communicate with them using different communication media such as email, telephone calls, and face-to-face interactions. While email is not real-time and face-to-face communications require geographic proximity, voice and video communications are preferred over other modes of communication. However, real-time voice/video calls may create nuisance to the receiver. In this article, we describe a mathematical model for computing nuisance level of incoming voice/video calls. We computed the closeness and nuisance level using the calling patterns between the caller and the callee. To validate the nuisance model, we collected cell phone call records of real-life people at our university and computed the nuisance value for all voice calls. We validated the nuisance levels using the feedback from those real-life people. Such a nuisance model is useful for predicting unwanted voice and video sessions in an IP communication network.

}, keywords = {behavior, Multimedia communications, nuisance, presence, Security, tolerance, unwantedness}, issn = {1551-6857}, doi = {10.1145/1404880.1404886}, url = {http://doi.acm.org/10.1145/1404880.1404886}, author = {Kolan, Prakash and Ram Dantu and Cangussu, Jo{\~a}o} } @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} } @article {10, title = {Socio-technical Defense Against Voice Spamming}, journal = {ACM Trans. Auton. Adapt. Syst.}, volume = {2}, year = {2007}, abstract = {

Voice over IP (VoIP) is a key enabling technology for migration of circuit-switched PSTN (Public Switched Telephone Network) architectures to packet-based networks. One problem of the present VoIP networks is filtering spam calls referred to as SPIT (Spam over Internet Telephony). Unlike spam in e-mail systems, VoIP spam calls have to be identified in real time. Many of the techniques devised for e-mail spam detection rely upon content analysis, and in the case of VoIP, it is too late to analyze the content (voice) as the user would have already attended the call. Therefore, the real challenge is to block a spam call before the telephone rings. In addition, we believe it is imperative that spam filters integrate human behavioral aspects to gauge the legitimacy of voice calls. We know that, when it comes to receiving or rejecting a voice call, people use the social meaning of trust, reputation, friendship of the calling party and their own mood. In this article, we describe a multi-stage, adaptive spam filter based on presence (location, mood, time), trust, and reputation to detect spam in voice calls. In particular, we describe a closed-loop feedback control between different stages to decide whether an incoming call is spam. We further propose formalism for voice-specific trust and reputation analysis. We base this formal model on a human intuitive behavior for detecting spam based on the called party{\textquoteright}s direct and indirect relationships with the calling party. No VoIP corpus is available for testing the detection mechanism. Therefore, for verifying the detection accuracy, we used a laboratory setup of several soft-phones, real IP phones and a commercial-grade proxy server that receives and processes incoming calls. We experimentally validated the proposed filtering mechanisms by simulating spam calls and measured the filter{\textquoteright}s accuracy by applying the trust and reputation formalism. We observed that, while the filter blocks a second spam call from a spammer calling from the same end IP host and domain, the filter needs only a maximum of three calls{\textemdash}even in the case when spammer moves to a new host and domain. Finally, we present a detailed sensitivity analysis for examining the influence of parameters such as spam volume and network size on the filter{\textquoteright}s accuracy

}, keywords = {behavior, reputation, SIP (Session Initiation Protocol), SPIT (Spam over IP Telephony), tolerance, Trust}, issn = {1556-4665}, doi = {10.1145/1216895.1216897}, url = {http://doi.acm.org/10.1145/1216895.1216897}, author = {Kolan, Prakash and Ram Dantu} }