02039nas a2200229 4500008004100000022001400041245008400055210006900139300001400208490000700222520132800229653002401557100002401581700001501605700002401620700002001644700001901664700002301683700001501706700001601721856007201737 2018 eng d a1746-809400aA novel heart-mobile interface for detection and classification of heart sounds0 anovel heartmobile interface for detection and classification of  a313 - 3240 v453 a

Abstract Diagnosis of heart disease requires that a medical practitioner investigate heart auscultations for irregular sounds, followed by echocardiography and electrocardiography tests. These expensive tests also require specialized technicians to operate. We present a low-cost, patient-centered device for the initial screening of the heart sounds that can be potentially used by the users on themselves. They can later share these readings with their healthcare providers. We have created an innovative mobile-health service platform for analyzing and classifying heart sounds. The presented system enables remote patient-monitoring by integrating advanced wireless communications with a customized low-cost stethoscope. This system also permits remote management of a patient’s cardiac status while maximizing patient mobility. The smartphone application facilitates recording, processing, visualizing, listening to, and classification of heart sounds. We build our classification model using the Mel-Frequency Cepstral Coefficient and Hidden Markov Model. This application is tested in a hospital environment to collect live recordings from patients with positive results. The smartphone application correctly detected 92.68% of abnormal heart conditions in clinical trials at \{UT\} Southwestern Hospital.

10aHidden Markov Model1 aThiyagaraja, Shanti1 aDantu, Ram1 aShrestha, Pradhumna1 aChitnis, Anurag1 aThompson, Mark1 aAnumandla, Pruthvi1 aSarma, Tom1 aDantu, Siva uhttps://www.sciencedirect.com/science/article/pii/S174680941830110103075nas a2200217 4500008004100000022001400041245011100055210006900166300000600235520238000241653001902621653001502640653002302655653001702678653002602695653001902721653001302740100001802753700001502771856007102786 2016 eng d a0167-404800aNeurokey: towards a new paradigm of cancelable biometrics-based key generation using electroencephalograms0 aNeurokey towards a new paradigm of cancelable biometricsbased ke a-3 a

 Brain waves (Electroencephalograms, EEG) can provide conscious, continuous human authentication for the proposed system. The advantage of brainwave biometry is that it is nearly impossible to forge or duplicate as the neuronal activity of each person is unique even when they think about the same thing.

We propose exploiting the brain as a biometric physical unclonable function (PUF). A user's \{EEG\} signals can be used to generate a unique and repeatable key that is resistant to cryptanalysis and eavesdropping, even against an adversary who obtains all the information regarding the system. Another objective is to implement a simplistic approach of cancelable biometrics by altering one's thoughts.

Features for the first step, Subject Authentication, are obtained from each task using the energy bands obtained from Discrete Fourier Transform and Discrete Wavelet Transform. The second step constituting the Neurokey generation involves feature selection using normalized thresholds and segmentation window protocol.

We applied our methods to two datasets, the first based on five mental activities by seven subjects (325 samples) and the second based on three visually evoked tasks by 120 subjects (10,861 samples). These datasets were used to analyze the key generation process because they varied in the nature of data acquisition, environment, and activities. We determined the feasibility of our system using a smaller dataset first. We obtained a mean subject classification of 98.46% and 91.05% for Dataset I and Dataset İI\} respectively. After an appropriate choice of features, the mean half total error rate for generating Neurokeys was 3.05% for Dataset I and 4.53% for Dataset II, averaged over the subjects, tasks, and electrodes. A unique key was established for each subject and task, and the error rates were analyzed for the Neurokey generation protocol. \{NIST\} statistical suite of randomness tests were applied on all the sequences obtained from the Neurokey generation process.

A consistent, unique key for each subject can be obtained using \{EEG\} signals by collecting data from distinguishable cognitive activities. Moreover, the Neurokey can be changed easily by performing a different cognitive task, providing a means to change the biometrics in case of a compromise (cancelable).

10aAuthentication10abiometrics10acryptographic keys10acryptography10aelectroencephalograms10akey generation10aSecurity1 aBajwa, Garima1 aDantu, Ram uhttp://www.sciencedirect.com/science/article/pii/S016740481630066900358nas a2200121 4500008004100000245004500041210004400086260001300130100002300143700001800166700001500184856003700199 2013 eng d00aNFC Based Two-Pass Mobile Authentication0 aNFC Based TwoPass Mobile Authentication bSpringer1 aVempati, Jagannadh1 aBajwa, Garima1 aDantu, Ram uhttps://nsl.cse.unt.edu/node/17901263nas a2200157 4500008004100000245010900041210006900150490000600219520074700225100001800972700001500990700002501005700001801030700002001048856003701068 2010 eng d00aNext generation 9-1-1: Architecture and challenges in realizing an IP-multimedia-based emergency service0 aNext generation 911 Architecture and challenges in realizing an 0 v73 a

Next generation 9-1-1 (NG-9-1-1) services will enable the use of multimedia (voice, video, text messages, and data) for emergency communications. This will be made possible using a whole new architecture for emergency communications, one that is based on the internet protocol (IP) and open standards. VOIP based communication services vastly improve the effectiveness of relief during mass disasters, as was observed during Katrina. However, delivering the latest multimedia technology to Public Service Answering Points (PSAPs) presents both new opportunities and challenges. In this paper, we review how technology is evolving towards an NG-9-1-1 solution and underscore the challenges and issues that still require investigation.

1 aGupta, Neeraj1 aDantu, Ram1 aSchulzrinne, Henning1 aGoulart, Anna1 aMagnussen, Walt uhttps://nsl.cse.unt.edu/node/13201794nas a2200193 4500008004100000022001400041245005300055210005300108300001200161490000600173520127900179653001801458653001301476653002001489100001501509700001901524700002001543856003701563 2009 eng d a1939-012200aNetwork risk management using attacker profiling0 aNetwork risk management using attacker profiling a83–960 v23 a

Risk management refers to the process of making decisions that minimize the effects of vulnerabilities on the network hosts. This can be a difficult task in the context of high-exploit probability and the difficult to identify new exploits and vulnerabilities. For many years, security engineers have performed risk analysis using economic models for the design and operation of risk-prone, technological systems using attack profiles. Based on the type of attacker identified, security administrators can formulate effective risk management policies for a network. We hypothesize that sequence of network actions by an attacker depends on the social behavior (e.g., skill level, tenacity, financial ability). We extended this and formulated a mechanism to estimate the risk level of critical resources that may be compromised based on attacker behavior. This estimation is accomplished using behavior based attack graphs representing all the possible attack paths to all the critical resources. The risk level is computed based on these graphs and are used as a measure of the vulnerability of the resource and forming an effective basis for a system administrator to perform suitable changes to network configuration. Copyright © 2008 John Wiley & Sons, Ltd.

10aattack graphs10abehavior10arisk management1 aDantu, Ram1 aKolan, Prakash1 aCangussu, João uhttp://dx.doi.org/10.1002/sec.5801661nas 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/10.1145/1404880.1404886