01424nas a2200229 4500008004100000245009700041210006900138260000900207520065400216653001900870653001800889653001500907653002400922653000800946653002500954653001300979100001800992700002201010700001501032700002201047856012501069 2019 eng d00aBlockchain Based Authentication and Authorization Framework for Remote Collaboration Systems0 aBlockchain Based Authentication and Authorization Framework for  cJune3 a

Due to the advantages of blockchain technologies, including decentralization, immutability, transparency and security, people try to replace existing problematic architectures /frameworks with blockchain based ones. In this paper we propose a novel authentication and authorization framework based on blockchain technologies to control access to the resources of an IoT device. In this paper, we focus on devices such as the Cyber Handyman used in remote collaboration applications to develop our framework. We tested our smart contracts on the Ropsten test network. Our results showed that it can handle 25 service requests simultaneously.

10aAccess Control10aauthorization10ablockchain10aDigital Certificate10aIoT10aremote collaboration10aSecurity1 aWidick, Logan1 aRanasinghe, Ishan1 aDantu, Ram1 aJonnada, Srikanth uhttps://nsl.cse.unt.edu/content/blockchain-based-authentication-and-authorization-framework-remote-collaboration-systems00416nas a2200097 4500008004100000245007700041210006900118100001900187700001500206856009700221 2018 eng d00aBridging the Gap: Developing Innovative Minds Early On for Cybersecurity0 aBridging the Gap Developing Innovative Minds Early On for Cybers1 aThompson, Mark1 aDantu, Ram uhttps://nsl.cse.unt.edu/content/bridging-gap-developing-innovative-minds-early-cybersecurity03604nas a2200241 4500008004100000022001400041245004300055210004200098300001700140490000600157520297600163653001303139653001403152653001603166653002103182653001503203653001903218100002803237700001503265700001703280700001803297856004703315 2011 eng d a1556-466500aBehavior-based Adaptive Call Predictor0 aBehaviorbased Adaptive Call Predictor a21:1–21:280 v63 a

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’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 “Incoming Call Forecast” and “Intelligent Address Book.” For the Incoming Call Forecast, the CP makes the next-24-hour incoming call prediction based on recent caller’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’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’s calling behavior and reciprocity. We show that the recent trend of the caller/user’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’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.

10abehavior10acall logs10acall matrix10aconvergence time10aPrediction10atrace distance1 aPhithakkitnukoon, Santi1 aDantu, Ram1 aClaxton, Rob1 aEagle, Nathan uhttp://doi.acm.org/10.1145/2019583.201958801439nas a2200157 4500008004100000020002200041245005300063210005200116260003600168520094600204100002801150700001501178700001401193700002001207856005401227 2009 eng d a978-1-4244-4171-600aBBN-based Privacy Management System for Facebook0 aBBNbased Privacy Management System for Facebook aPiscataway, NJ, USAbIEEE Press3 a

Online social networking sites (SNSs) has changed our lifestyle and become a main medium of communication among young adults to stay in touch with their friends, to organize events, to make friends, to promote themselves, to date, etc. To create content rich environment, SNSs make their platform available for third-party developers. The developers can build their applications based on users' social graph containing their personal and social information. Unfortunately, any information users posted on their profile can be harvested and used for unethical purposes due to Facebook's lack of application privacy configuration. In this paper we propose a privacy-management system for Facebook applications. The system can take advantage of the correlation between some profile features and network privacy settings, in this way it can automatically configure a users privacy settings. Our preliminary result show promising result.

1 aBaatarjav, Enkh-Amgalan1 aDantu, Ram1 aTang, Yan1 aCangussu, João uhttp://dl.acm.org/citation.cfm?id=1706428.170646702279nas a2200373 4500008004100000245003800041210003800079520122200117653003001339653001201369653003001381653002501411653002601436653001601462653002001478653001601498653001401514653002601528653002201554653002101576653002701597653002301624653000801647653003301655653001901688653002801707653002301735653002901758100001801787700002801805700002001833700001501853856003701868 2008 eng d00aBehavior analysis of spam botnets0 aBehavior analysis of spam botnets3 a

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%.

10abehavior pattern analysis10aBotnets10aClassification algorithms10aclustering algorithm10aClustering algorithms10aCorrelation10aElectronic mail10afeature set10aFiltering10ainformation filtering10ainvasive software10amaximum variance10apattern classification10apattern clustering10aPCA10aprincipal component analysis10aspam filtering10aTime frequency analysis10aunsolicited e-mail10aunwanted traffic control1 aHusna, Husain1 aPhithakkitnukoon, Santi1 aPalla, Srikanth1 aDantu, Ram uhttps://nsl.cse.unt.edu/node/20001653nas a2200193 4500008004100000020002200041245004800063210004800111260002800159300001400187520108700201100002801288700001801316700001501334700001401349700002201363700002301385856005101408 2008 eng d a978-0-387-77672-900aBehavioral Entropy of a Cellular Phone User0 aBehavioral Entropy of a Cellular Phone User aBoston, MAbSpringer US a160–1673 a

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’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’s calling behavior on various parameters and interesting relationship between randomness levels in individual’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.

1 aPhithakkitnukoon, Santi1 aHusna, Husain1 aDantu, Ram1 aLiu, Huan1 aSalerno, John, J.1 aYoung, Michael, J. uhttp://dx.doi.org/10.1007/978-0-387-77672-9_1800347nas a2200097 4500008004100000245005500041210005300096260004800149100001500197856003700212 2005 eng d00aA Blueprint for Implementing Security in Radiology0 aBlueprint for Implementing Security in Radiology bAHRA Electronic Imaging Conferencec04/20051 aDantu, Ram uhttps://nsl.cse.unt.edu/node/164