@article {300, title = {A novel heart-mobile interface for detection and classification of heart sounds}, journal = {Biomedical Signal Processing and Control}, volume = {45}, year = {2018}, pages = {313 - 324}, abstract = {

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{\textquoteright}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.

}, keywords = {Hidden Markov Model}, issn = {1746-8094}, doi = {https://doi.org/10.1016/j.bspc.2018.05.008}, url = {https://www.sciencedirect.com/science/article/pii/S1746809418301101}, author = {Thiyagaraja, Shanti and Ram Dantu and Shrestha, Pradhumna and Chitnis, Anurag and Thompson, Mark and Anumandla, Pruthvi and Sarma, Tom and Dantu, Siva} } @article {94, title = {Neurokey: towards a new paradigm of cancelable biometrics-based key generation using electroencephalograms}, journal = {Computers \& Security}, year = {2016}, pages = {-}, abstract = {

\ 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{\textquoteright}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{\textquoteright}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 {\.I}I\} 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).

}, keywords = {Authentication, biometrics, cryptographic keys, cryptography, electroencephalograms, key generation, Security}, issn = {0167-4048}, doi = {http://dx.doi.org/10.1016/j.cose.2016.06.001}, url = {http://www.sciencedirect.com/science/article/pii/S0167404816300669}, author = {Bajwa, Garima and Ram Dantu} } @conference {217, title = {NFC Based Two-Pass Mobile Authentication}, booktitle = {Research in Attacks, Intrusions, and Defenses: 16th International Symposium, RAID 2013, Rodney Bay, St. Lucia, October 23-25, 2013, Proceedings}, year = {2013}, publisher = {Springer}, organization = {Springer}, author = {Vempati, Jagannadh and Bajwa, Garima and Ram Dantu} } @article {63, title = {Next generation 9-1-1: Architecture and challenges in realizing an IP-multimedia-based emergency service}, journal = {Journal of Homeland Security and Emergency Management}, volume = {7}, year = {2010}, abstract = {

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.

}, author = {Gupta, Neeraj and Ram Dantu and Schulzrinne, Henning and Goulart, Anna and Magnussen, Walt} } @article {20, title = {Network risk management using attacker profiling}, journal = {Security and Communication Networks}, volume = {2}, year = {2009}, pages = {83{\textendash}96}, abstract = {

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 {\textcopyright} 2008 John Wiley \& Sons, Ltd.

}, keywords = {attack graphs, behavior, risk management}, issn = {1939-0122}, doi = {10.1002/sec.58}, url = {http://dx.doi.org/10.1002/sec.58}, author = {Ram Dantu and Kolan, Prakash and Cangussu, Jo{\~a}o} } @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} }