@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} } @article {42, title = {Mobile Social Group Sizes and Scaling Ratio}, journal = {AI Soc.}, volume = {26}, year = {2011}, pages = {71{\textendash}85}, abstract = {

Social data mining has become an emerging area of research in information and communication technology fields. The scope of social data mining has expanded significantly in the recent years with the advance of telecommunication technologies and the rapidly increasing accessibility of computing resources and mobile devices. People increasingly engage in and rely on phone communications for both personal and business purposes. Hence, mobile phones become an indispensable part of life for many people. In this article, we perform social data mining on mobile social networking by presenting a simple but efficient method to define social closeness and social grouping, which are then used to identify social sizes and scaling ratio of close to {\textquotedblleft}8{\textquotedblright}. We conclude that social mobile network is a subset of the face-to-face social network, and both groupings are not necessary the same, hence the scaling ratios are distinct. Mobile social data mining.

}, issn = {0951-5666}, doi = {10.1007/s00146-009-0230-5}, url = {http://dx.doi.org/10.1007/s00146-009-0230-5}, author = {Phithakkitnukoon, Santi and Ram Dantu} } @article {52, title = {Towards Ubiquitous Computing with Call Prediction}, journal = {SIGMOBILE Mob. Comput. Commun. Rev.}, volume = {15}, year = {2011}, pages = {52{\textendash}64}, abstract = {

With the long-awaited era of the pervasive computing approaches, the handheld devices such as personal mobile phones begin to evolve into ubiquitous computing devices. At this early stage of the evolution, we propose a model of a call predictor based on the naive Bayesian classifier. As an incoming call predictor, our model makes use of the user{\^a}€™s call history to generate a list of numbers/contacts that are the most likely to be the callers within the next hour. On the other hand, when the user wants to make an outgoing call (e.g., user flips open the phone or unlocks the phone, etc.), the outgoing call predictor generates a list of number/contacts to be called. Our model has been evaluated with the real-life call logs and it shows a promising result in accuracy.

}, issn = {1559-1662}, doi = {10.1145/1978622.1978628}, url = {http://doi.acm.org/10.1145/1978622.1978628}, author = {Phithakkitnukoon, Santi and Ram Dantu} } @article {44, title = {ContextAlert: Context-aware alert mode for a mobile phone}, journal = {International Journal of Pervasive Computing and Communications}, volume = {6}, year = {2010}, month = {Sep}, pages = {1{\textendash}23}, abstract = {

Purpose {\textendash} Mobile computing research has been focused on developing technologies for handheld devices such as mobile phones, notebook computers, and mobile IP. Today, emphasis is increasing on context-aware computing, which aims to build the intelligence into mobile devices to sense and respond to the user{\textquoteright}s context. The purpose of this paper is to present a context-aware mobile computing model (ContextAlert) that senses the user{\textquoteright}s context and intelligently configures the mobile phone alert mode accordingly. Design/methodology/approach {\textendash} The paper proposes a three-step approach in designing the model based on the embedded sensor data (accelerometer, GPS antenna, and microphone) of a G1 Adriod phone. As adaptivity is essential for context-aware computing, within this model a new learning mechanism is presented to maintain a constant adaptivity rate for new learning while keeping the catastrophic forgetting problem minimal. Findings {\textendash} The model has been evaluated in many aspects using data collected from human subjects. The experiment results show that the proposed model performs well and yields a promising result. Originality/value {\textendash} This paper is distinguished from other previous papers by: first, using multiple sensors embeded in the mobile phone, which is more realistic for detecting the user{\textquoteright}s context than having various sensors attached to different parts of user{\textquoteright}s body; second, by being a novel model that uses sensed contextual information to provide a service that better synchronizes the user{\textquoteright}s daily life with a context-aware alert mode. With this service, the user can avoid the problems such as forgetting to switch to vibrate mode while in a meeting or a movie theater, and taking the risk of picking up a phone call while driving, and third, being an adaptive learning algorithm that maintains a constant adaptivity rate for new learning while keeping the catastrophic forgetting problem minimal.

}, issn = {1742-7371}, doi = {10.1108/17427371011084266}, author = {Phithakkitnukoon, Santi and Ram Dantu} } @inbook {54, title = {Email Shape Analysis}, booktitle = {Distributed Computing and Networking: 11th International Conference, ICDCN 2010, Kolkata, India, January 3-6, 2010. Proceedings}, year = {2010}, pages = {18{\textendash}29}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, address = {Berlin, Heidelberg}, abstract = {

Email has become an integral part of everyday life. Without a second thought we receive bills, bank statements, and sales promotions all to our inbox. Each email has hidden features that can be extracted. In this paper, we present a new mechanism to characterize an email without using content or context called Email Shape Analysis. We explore the applications of the email shape by carrying out a case study; botnet detection and two possible applications: spam filtering, and social-context based finger printing. Our in-depth analysis of botnet detection leads to very high accuracy of tracing templates and spam campaigns. However, when it comes to spam filtering we do not propose new method but rather a complementing method to the already high accuracy Bayesian spam filter. We also look at its ability to classify individual senders in personal email inbox{\textquoteright}s.

}, isbn = {978-3-642-11322-2}, doi = {10.1007/978-3-642-11322-2_7}, url = {http://dx.doi.org/10.1007/978-3-642-11322-2_7}, author = {Sroufe, Paul and Phithakkitnukoon, Santi and Ram Dantu and Cangussu, Jo{\~a}o}, editor = {Kant, Krishna and Pemmaraju, Sriram V. and Sivalingam, Krishna M. and Wu, Jie} } @conference {40, title = {Mobile Social Closeness and Communication Patterns}, booktitle = {Proceedings of the 7th IEEE Conference on Consumer Communications and Networking Conference}, year = {2010}, publisher = {IEEE Press}, organization = {IEEE Press}, address = {Piscataway, NJ, USA}, abstract = {

As mobile networks expand rapidly to facilitate the rising number of mobile phone population, more mobile social services are being developed and offered. To create an efficient social functionality, characteristics of mobile social network must be studied. Social closeness is one of the basic fundamentals of any kind of social networking. In this paper, closeness in mobile social network is the subject of the study, from which social grouping scheme is proposed and validated against the feedbacks of human subjects. Based on the proposed grouping scheme, a study of the impact of the mobile social closeness to the similarity in calling patterns and reciprocity is presented.

}, isbn = {978-1-4244-5175-3}, url = {http://dl.acm.org/citation.cfm?id=1834217.1834285}, author = {Phithakkitnukoon, Santi and Ram Dantu} } @conference {237, title = {Socio-technical aspects of video phones}, booktitle = {2010 Second International Conference on COMmunication Systems and NETworks (COMSNETS 2010)}, year = {2010}, month = {Jan}, abstract = {

The widespread use of voice over Internet protocol has paved the way for video over Internet protocol. In the past, certain technical shortcomings have prevented the popularity of videophones in the market. With present-day technology, videophones have just about everything required for day-to-day functions. Under such circumstances, certain socio-technical aspects require attention so that videophones can become as widespread and as technically streamlined as a plain old telephone system (POTS) with its additional benefits. A frequently brushed-upon topic is optimum features in the video phone for day-to-day social interactions. We carried out several experiments on different kinds of codecs and video formats to address two issues: i) the size of a video screen and ii) perception of motion and distance. From the measurements, we observed that a small frame rate with low bandwidth is adequate and can result in satisfactory video quality. We also observed that H263 performs well for all the day-to-day social networking activities. Standing 4 feet from the camera can still give reasonably good video quality in the currently available codecs. We believe that socio-technical issues will emerge more clearly over the next several years and they are germane to deployment of PC-based soft phones as well as hard phones.

}, keywords = {Bandwidth, Cameras, Codecs, Computer science, Internet telephony, plain old telephone system, Protocols, social aspects of automation, Social factors, social interactions, social issues, Social network services, social networking activities, sociotechnical aspects, Streaming media, technical issues, video codecs, video coding, video phones, video quality, Videoconference, videophone quality, videophones, videotelephony, voice over Internet protocol}, doi = {10.1109/COMSNETS.2010.5431971}, author = {Chandrasekaran, Vikram and Dantu, Siva and Kadiyala, Priyanka and Ram Dantu and Phithakkitnukoon, Santi} } @conference {61, title = {Defense Against SPIT Using Community Signals}, booktitle = {Proceedings of the 2009 IEEE International Conference on Intelligence and Security Informatics}, year = {2009}, publisher = {IEEE Press}, organization = {IEEE Press}, address = {Piscataway, NJ, USA}, abstract = {

Internet telephony has recently gained popularity. Voice over IP (VoIP) has emerged as an alternative to the current public switched telephone network (PSTN) system due to its cost efficiency. Spam inherently becomes a problem in VoIP networks. In this paper, we present our ongoing research in defending VoIP networks against spam by presenting a new defense mechanism using community signals.

}, isbn = {978-1-4244-4171-6}, url = {http://dl.acm.org/citation.cfm?id=1706428.1706481}, author = {Phithakkitnukoon, Santi and Ram Dantu} } @conference {29, title = {Email Shape Analysis for Spam Botnet Detection}, booktitle = {Proceedings of the 6th IEEE Conference on Consumer Communications and Networking Conference}, year = {2009}, publisher = {IEEE Press}, organization = {IEEE Press}, address = {Piscataway, NJ, USA}, abstract = {

Botnets have become the major sources of spamming, which generates massive unwanted traffic on networks. An effective detection mechanism can greatly mitigate the problem. In this paper, we present a novel botnet detection mechanism based on the email "shape" analysis that relies on neither content nor reputation analysis. Shape is our new way of characterizing an email by mimicking human visual inspection. A set of email shapes are derived and then used to generate a botnet signature. Our preliminary results show greater than 80\% classification accuracy (without considering email content or reputation analysis). This work investigates the discriminatory power of email shape, for which we believe will be a significant complement to other existing techniques such as a network behavior analysis.

}, isbn = {978-1-4244-2308-8}, url = {http://dl.acm.org/citation.cfm?id=1700527.1700792}, author = {Sroufe, Paul and Phithakkitnukoon, Santi and Ram Dantu and Cangussu, Jo{\~a}o} } @conference {238, title = {Adequacy of data for characterizing caller behavior}, booktitle = {Proceedings of KDD Inter. Workshop on Social Network Mining and Analysis (SNAKDD}, year = {2008}, author = {Phithakkitnukoon, Santi and Ram Dantu} } @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.} } @conference {50, title = {CPL: Enhancing Mobile Phone Functionality by Call Predicted List}, booktitle = {Proceedings of the OTM Confederated International Workshops and Posters on On the Move to Meaningful Internet Systems: 2008 Workshops: ADI, AWeSoMe, COMBEK, EI2N, IWSSA, MONET, OnToContent + QSI, ORM, PerSys, RDDS, SEMELS, and SWWS}, year = {2008}, publisher = {Springer-Verlag}, organization = {Springer-Verlag}, address = {Berlin, Heidelberg}, abstract = {

In this paper, we present a concept of a new advanced feature for a mobile phone that provides its user functionality for predicting future calls. The feature is envisaged as a Call Predicted List (CPL) which makes use of the user{\textquoteright}s call history to build a probabilistic model of calling behavior based on the caller{\textquoteright}s calling patterns and reciprocity. The calling behavior model is then used to generate a list of numbers/contacts that are the most likely to be callers in the next hour. The performance of the CPL is evaluated with the real-life call logs and it shows promising results in accuracy.

}, keywords = {Call prediction, Context-aware computing, mobile phone}, isbn = {978-3-540-88874-1}, doi = {10.1007/978-3-540-88875-8_80}, url = {http://dx.doi.org/10.1007/978-3-540-88875-8_80}, author = {Phithakkitnukoon, Santi and Ram Dantu} } @inbook {12, title = {Group Recommendation System for Facebook}, booktitle = {On the Move to Meaningful Internet Systems: OTM 2008 Workshops, Monterrey, Mexico, November 9-14, 2008. Proceedings}, year = {2008}, pages = {211{\textendash}219}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, address = {Berlin, Heidelberg}, abstract = {

Online social networking has become a part of our everyday lives, and one of the popular online social network (SN) sites on the Internet is Facebook, where users communicate with their friends, join to groups, create groups, play games, and make friends around the world. Also, the vast number of groups are created for different causes and beliefs. However, overwhelming number of groups in one category causes difficulties for users to select a right group to join. To solve this problem, we introduce group recommendation system (GRS) using combination of hierarchical clustering technique and decision tree. We believe that Facebook SN groups can be identified based on their members{\textquoteright} profiles. Number of experiment results showed that GRS can make 73\% accurate recommendation.

}, keywords = {decision tree, recommendation system, Social network}, isbn = {978-3-540-88875-8}, doi = {10.1007/978-3-540-88875-8_41}, url = {http://dx.doi.org/10.1007/978-3-540-88875-8_41}, author = {Baatarjav, Enkh-Amgalan and Phithakkitnukoon, Santi and Ram Dantu}, editor = {Meersman, Robert and Tari, Zahir and Herrero, Pilar} } @conference {49, title = {Inferring Social Groups Using Call Logs}, booktitle = {On the Move to Meaningful Internet Systems: OTM 2008 Workshops}, year = {2008}, month = {11/2008}, pages = {200{\textendash}210}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, address = {Monterrey, Mexico}, abstract = {

Recent increase in population of mobile phone users makes it a valuable source of information for social network analysis. For a given call log, how much can we tell about the person{\textquoteright}s social group? Unnoticeably, phone user{\textquoteright}s calling personality and habit has been concealed in the call logs from which we believe that it can be extracted to infer its user{\textquoteright}s social group information. In this paper, we present an end-to-end system for inferring social networks based on {\textquotedblleft}only{\textquotedblright} call logs using kernel-based na{\"\i}ve Bayesian learning. We also introduce normalized mutual information for feature selection process. Our model is evaluated with real-life call logs where it performs at high accuracy rate of 81.82\%.

}, isbn = {978-3-540-88875-8}, doi = {10.1007/978-3-540-88875-8_40}, url = {http://dx.doi.org/10.1007/978-3-540-88875-8_40}, author = {Phithakkitnukoon, Santi and Ram Dantu}, editor = {Meersman, Robert and Tari, Zahir and Herrero, Pilar} } @conference {30, title = {Privacy Management for Facebook}, booktitle = {4th International Conference on Information Systems Security (ICISS 2008)}, year = {2008}, month = {12/2008}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, address = {Hyderabad, India}, abstract = {

As more people adopt the Internet as a medium of communication, the Internet has developed into a virtual world and this has resulted in many online social networks (SN). MySpace and Facebook, two leading online SN sites, have a combined user base of 170 million as of 2008. SN sites started to offer developers open platforms that provide users{\textquoteright} profile information to the developers. Unfortunately, the applications can also be used to invade privacy and to harvest the users{\textquoteright} profile information without their acknowledgement. To address this vulnerability, we propose a privacy-management system that protects the accessibility of users{\textquoteright} profile. The system uses probabilistic approach based on information revelation of users. Our experimental result shows that the system can achieve high accuracy rate of 75\%.

}, keywords = {Facebook, Privacy, Privacy management, Social network}, doi = {10.1007/978-3-540-89862-7\_23}, author = {Baatarjav, Enkh-Amgalan and Ram Dantu and Phithakkitnukoon, Santi} } @conference {60, title = {Quantifying presence using calling patterns}, booktitle = {Communication Systems Software and Middleware and Workshops, 2008. COMSWARE 2008. 3rd International Conference on}, year = {2008}, month = {Jan}, abstract = {

Presence technology is going to be an integral part of the next generation of communication technology. It can eliminate telephone tag between two parties (caller and callee), which will increase productivity of the parties and reduce unnecessary bandwidth usage of unwanted calls. In this paper, we propose a Willingness Estimator that computes willingness level of a callee for receiving calls from a specified caller. By knowing the willingness value of the callee the caller can decide on proceeding with the call or not. The proposed Willingness Estimator is tested with real mobile user data, and these results are highly accurate. We measure willingness based on calling patterns (arrival time, location, day) of a caller to a callee and this could serve as one of the future presence based services. The results can be used in telecommunication networks such as PSTN, Cellular networks, and Voice over IP networks.

}, keywords = {cellular networks, Communications technology, Computer science, Computer security, Intrusion detection, Laboratories, Mobile handsets, mobile user data, Network servers, next generation communication, Next generation networking, presence technology, Productivity, PSTN, telecommunication networks, telephony, user calling pattern, voice over IP networks, willingness estimator}, doi = {10.1109/COMSWA.2008.4554403}, author = {Husna, Husain and Phithakkitnukoon, Santi and Baatarjav, Enkh-Amgalan and Ram Dantu} } @conference {32, title = {Traffic Shaping of Spam Botnets}, booktitle = {2008 5th IEEE Consumer Communications and Networking Conference}, year = {2008}, month = {01/2008}, address = {Las Vegas, NV}, abstract = {

Compromised computers, known as bots, are the major source of spamming. Detecting them can help greatly improve control of unwanted traffic. In this paper, we develop a traffic control mechanism to detect and delay the traffic of suspicious senders and bots. By delaying spammer{\textquoteright}s traffic, it has been reported that 90\% of spam emails can be eliminated. In our proposed mechanism, we group spammers based on their behavior and transmission patterns. These patterns of spammers show high correlation between group members irrespective of geographic location, network ID, content, and kind of receivers. After identification of these botnet groups we applied traffic shaping techniques a pre-filtering analysis to avoid use of automated machines(bots) to spam a particular domain. Thus the source for majority of spam is blocked before reaching email servers. We also identify how randomly the botnets behave and how easy it is to capture a botnet behavior, based on Information theory. To our knowledge, there is no work reported on detecting and mitigating botnets based on their behavior and in particular transmission patterns.

}, keywords = {automated machine, compromised computer, E-mail server, Internet, prefiltering analysis, spam botnets, spammer traffic, spamming, suspicious senders, telecommunication network management, telecommunication traffic, traffic control mechanism, traffic shaping, unsolicited e-mail}, doi = {10.1109/ccnc08.2007.181}, author = {Husna, Husain and Phithakkitnukoon, Santi and Ram Dantu} } @article {43, title = {VoIP Security: Attacks and Solutions}, journal = {Information Security Journal: A Global Perspective}, volume = {17}, year = {2008}, pages = {114{\textendash}123}, abstract = {

Voice over IP (VoIP) technology is being extensively and rapidly deployed. Flexibility and cost efficiency are the key factors luring enterprises to transition to VoIP. Some security problems may surface with the widespread deployment of VoIP. This article presents an overview of VoIP systems and its security issues. First, we briefly describe basic VoIP architecture and its fundamental differences compared to PSTN. Next, basic VoIP protocols used for signaling and media transport, as well as defense mechanisms are described. Finally, current and potential VoIP attacks along with the approaches that have been adopted to counter the attacks are discussed.

}, keywords = {attack, defense mechanism, PSTN, Security, VoIP}, issn = {1939-3555}, doi = {10.1080/19393550802308618}, url = {http://dx.doi.org/10.1080/19393550802308618}, author = {Phithakkitnukoon, Santi and Ram Dantu and Baatarjav, Enkh-Amgalan} } @conference {45, title = {Predicting Calls: New Service for an Intelligent Phone}, booktitle = {10th IFIP/IEEE International Conference on Management of Multimedia and Mobile Networks and Services, MMNS 2007}, year = {2007}, month = {11/2007}, address = { San Jos{\'e}, USA}, abstract = {

Predicting future calls can be the next advanced feature of the intelligent phone as the phone service providers are looking to offer new services to their customers. Call prediction can be useful to many applications such as planning daily schedule and attending unwanted communications (e.g. voice spam). Predicting calls is a very challenging task. We believe that this is a new area of research. In this paper, we propose a Call Predictor (CP) that computes the probability of receiving calls and makes call prediction based on caller{\textquoteright}s behavior and reciprocity. The proposed call predictor is tested with the actual call logs. The experimental results show that the call predictor performs reasonably well with false positive rate of 2.4416\%, false negative rate of 2.9191\%, and error rate of 5.3606\%.

}, keywords = {Arrival time, behavior, call matrix, Callee, Caller, Communications, Incoming calls, Inter-arrival time, Inter-arrival/departure time, Kernel density estimation, Outgoing calls, Probability density function (pdf), Receiving call probability, Reciprocity}, doi = {10.1007/978-3-540-75869-3\_3}, author = {Phithakkitnukoon, Santi and Ram Dantu} }