01661nas a2200241 4500008004100000022001400041245003500055210003500090300001500125490000600140520105900146653001301205653003001218653001301248653001301261653001301274653001401287653001701301100001901318700001501337700002001352856004701372 2008 eng d a1551-685700aNuisance Level of a Voice Call0 aNuisance Level of a Voice Call a6:1–6:220 v53 a

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.

10abehavior10aMultimedia communications10anuisance10apresence10aSecurity10atolerance10aunwantedness1 aKolan, Prakash1 aDantu, Ram1 aCangussu, João uhttp://doi.acm.org/10.1145/1404880.140488602969nas a2200397 4500008004100000245007400041210006900115260000800184520175100192653002101943653003801964653004602002653001302048653001602061653002102077653001902098653002002117653002202137653001402159653002602173653002402199653002302223653003902246653001402285653004602299653002502345653002802370653003002398653001402428653001402442653002202456100001902478700002202497700001502519856003702534 2007 eng d00aAutomatic Calibration Using Receiver Operating Characteristics Curves0 aAutomatic Calibration Using Receiver Operating Characteristics C cJan3 a

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's participating entities changes rapidly, filters must adapt quickly by using end user's preferences about receiving that service request message. Many adaptive filters learn from the participating entities' behavior; however, none configure themselves automatically to an end user'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'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's feedback. To demonstrate the mechanism'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's accuracy. Further, computing and updating the optimum threshold in realtime does not impede the filter's temporal performance because we update thresholds after each call'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.

10aadaptive filters10aapplication-level filter learning10aautomatic threshold-calibration mechanism10abehavior10acalibration10aComputer science10aComputer worms10aElectronic mail10aend user feedback10aFiltering10ainformation filtering10ainformation filters10aInternet telephony10alearning (artificial intelligence)10aProtocols10areceiver operating characteristics curves10asensitivity analysis10aservice request message10atelecommunication traffic10aThreshold10atolerance10aViruses (medical)1 aKolan, Prakash1 aVaithilingam, Ram1 aDantu, Ram uhttps://nsl.cse.unt.edu/node/22902897nas a2200205 4500008004100000022001400041245005100055210005000106490000600156520232400162653001302486653001502499653003802514653003402552653001402586653001002600100001902610700001502629856004702644 2007 eng d a1556-466500aSocio-technical Defense Against Voice Spamming0 aSociotechnical Defense Against Voice Spamming0 v23 a

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’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’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—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’s accuracy

10abehavior10areputation10aSIP (Session Initiation Protocol)10aSPIT (Spam over IP Telephony)10atolerance10aTrust1 aKolan, Prakash1 aDantu, Ram uhttp://doi.acm.org/10.1145/1216895.1216897