@conference {575, title = {Making Smart Contracts Predict and Scale}, booktitle = {2022 Fourth International Conference on Blockchain Computing and Applications (BCCA)}, year = {2022}, abstract = {

The machine learning algorithms can predict the events based on the trained models and datasets. However, a reliable prediction requires the model to be trusted and tamper-resistant. Blockchain technology provides trusted output with consensus-based transactions and an immutable distributed ledger. The machine learning algorithms can be trained on blockchain smart contracts to produce trusted models for reliable prediction. But most smart contracts in the blockchain do not support floating-point data type, limiting computations for classification, which can affect the prediction accuracy. In this work, we propose a novel method to produce floating-point equivalent probability estimation to classify labels on-chain with a Naive Bayes algorithm. We derive a mathematical model with Taylor series expansion to compute the ratio of the posterior probability of classes to classify labels using integers. Moreover, we implemented our solution in Ethereum blockchain smart-contract with the Solidity programming language, where we achieved a prediction accuracy comparable to the scikit-learn library in Python. Our derived method is platform-agnostic and can be supported in any blockchain network. Furthermore, machine learning and deep-learning algorithms can borrow the derived method.

}, doi = {10.1109/BCCA55292.2022.9922480}, author = {Badruddoja, Syed and Dantu, Ram and He, Yanyan and Thompson, Mark and Salau, Abiola and Upadhyay, Kritagya} } @conference {577, title = {Quantum Networks: Reset-and-Reuse can be a Game-changer for Entanglement via Distillation}, booktitle = {2022 IEEE International Conference on Quantum Computing and Engineering (QCE)}, year = {2022}, abstract = {

The future quantum network repeater is envisioned to primarily serve the role of creating entanglement between nodes and distilling those entanglements to an optimal level of performance. During our investigation, we implemented a multi-pass protocol for entanglement distillation and tested it on the IBM-Q environment, demonstrating successively improved results after multiple passes. We implemented two versions of multi-pass distillation, BBPSSW and DEJMPS, with a focus on optimizing the use of qubits, via the reset-and-reuse capability of the IBM implementation. The novel feature of reset-and-reuse can be a game-changer and can minimize the number of qubits required for large-scale applications. We also found that, though it is currently not possible to implement a criterion for continued distillation passes as a run-time feedback loop, the process can be studied through post-circuit data analysis. Our results also show that fidelity alone may guide us to discard some approaches that show success based on other metrics, such as entanglement success and success of transmitting a bit of data. The fidelity was experimentally found to be excessively low, for this complex process of multi-pass distillation.

}, doi = {10.1109/QCE53715.2022.00143}, author = {Germain, Julie and Dantu, Ram and Thompson, Mark and Dockendorf, Mark} } @conference {508, title = {Qubit Reset and Refresh: A Gamechanger for Random Number Generation}, booktitle = {Twelveth ACM Conference on Data and Application Security and Privacy (CODASPY )}, year = {2022}, abstract = {

Generation of random binary numbers for cryptographic use is often addressed using pseudorandom number generating functions in compilers and specialized cryptographic packages. Using the IBM{\textquoteright}s Qiskit reset functionality, we were able to implement a straight-forward in-line Python function that returns a list of quantum-generated random numbers, by creating and executing a circuit on IBM quantum systems. We successfully created a list of 1000 1024-bit binary random numbers as well as a list of 40,000 25-bit binary random numbers for randomness testing, using the NIST Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications. The quantum-generated random data we tested showed very strong randomness, according to the NIST suite. Previously, IBM{\textquoteright}s quantum implementation required a single qubit for each bit of data generated in a circuit, making generation of large random numbers impractical. IBM{\textquoteright}s addition of the reset instruction eliminates this restriction and allows for the creation of functions that can generate a larger quantity of data-bit output, using only a small number of qubits.

}, keywords = {Qiskit, QRNG, Quantum Computing, Random Number Generator}, doi = {10.1145/3508398.3519364}, url = {https://dl.acm.org/doi/abs/10.1145/3508398.3519364}, author = {Germain, Julie and Dantu, Ram and Thompson, Mark} } @conference {572, title = {Smarter Contracts to Predict using Deep-Learning Algorithms}, booktitle = {2022 Fourth International Conference on Blockchain Computing and Applications (BCCA)}, year = {2022}, abstract = {

Deep learning techniques can predict cognitive intelligence from large datasets involving complex computations with activation functions. However, the prediction output needs verification for trust and reliability. Moreover, these algorithms suffer from the model{\textquoteright}s provenance to keep track of model updates and developments. Blockchain smart contracts provide a trustable ledger with consensus-based decisions that assure integrity and verifiability. In addition, the immutability feature of blockchain also supports the provenance of data that can help deep learning algorithms. Nevertheless, smart contract languages cannot predict due to the absence of floating-point operations required by activation functions of neural networks. In this paper, we derive a novel method using the Taylor series expansion to compute the floating-point equivalent output for activation functions. We train the deep learning model off-chain using a standard Python programming language. Moreover, we store models and predict on-chain with blockchain smart contracts to produce a trusted forecast. Our experiment and analysis achieved an accuracy (99\%) similar to popular Keras Python library models for the MNIST dataset. Furthermore, any blockchain platform can reproduce the activation function using our derived method. Last but not least, other deep learning algorithms can reuse the mathematical model to predict on-chain.

}, doi = {10.1109/BCCA55292.2022.9922240}, author = {Badruddoja, Syed and Dantu, Ram and He, Yanyan and Thompson, Mark and Salau, Abiola and Upadhyay, Kritagya} } @conference {565, title = {Trusted AI with Blockchain to Empower Metaverse}, booktitle = {2022 Fourth International Conference on Blockchain Computing and Applications (BCCA)}, year = {2022}, abstract = {

The digital experience emerging in the virtual world is a reality with the advent of the metaverse. Augmented reality(AR), virtual reality(VR), extended reality(XR), and artificial intelligence(AI) algorithms would pave the way for an immersive experience for the users in the virtual space. However, the explosion of these technologies broaches new challenges to threaten the success of metaverse due to security risks. The blockchain technology augmented with AI promises to deliver a trusted metaverse for everyone. Nevertheless, smart contracts fail to produce a cognitive prediction, dissuading users from confiding in the metaverse. We arm smart contracts with intelligence to predict using AI algorithms. Moreover, we deploy the smart contracts on the Ethereum blockchain platform and produce a prediction accuracy of 95\% compared to Python scikit-learn-based predictions. Our results show that the prediction delay can obstruct the growth of metaverse applications to accept blockchain technologies. Furthermore, the limitation of blockchain technology can make integration unreasonable. Therefore, we discuss possible scalability solutions that can be part of our future work to help more metaverse applications adopt blockchain solutions.

}, doi = {10.1109/BCCA55292.2022.9922027}, author = {Badruddoja, Syed and Dantu, Ram and He, Yanyan and Thompson, Mark and Salau, Abiola and Upadhyay, Kritagya} } @conference {533, title = {Making Smart Contracts Smarter}, booktitle = {2021 IEEE International Conference on Blockchain and Cryptocurrency (ICBC)}, year = {2021}, publisher = {IEEE}, organization = {IEEE}, address = {Sydney, Australia}, abstract = {

Blockchain technology develops static smart contracts for decentralized business transactions, lacks dynamic decision-making capabilities that limit the possibilities of ever-increasing demands of modern business applications. Artificial intelligence, a computational prediction platform provides intelligent predictions, actions, and recognition that lacks the ability to hold on to the integrity of the prediction result and requires the help of external authorities to secure the system. Blockchain-based AI prediction can cover the gaps of individual technologies and can mutually benefit from one another to develop a decentralized machine learning architecture that promises to yield better security, automation, and dynamism of the application. This paper proposes a Naive Bayes prediction algorithm to perform prediction with inside blockchain smart contracts that promises to open up more opportunities in the field of Blockchain-AI decentralized applications.

}, keywords = {artificial intelligence, blockchain, DApp, machine learning, Naive Bayes, smart contract}, isbn = {978-1-6654-3578-9}, doi = {10.1109/ICBC51069.2021.9461148}, url = {https://ieeexplore.ieee.org/abstract/document/9461148}, author = {Badruddoja, Syed and Dantu, Ram and He, Yanyan and Upadhayay, Kritagya and Thompson, Mark} } @conference {576, title = {Adaptive and Predictive SDN Control During DDoS Attacks}, booktitle = {2020 IEEE International Conference on Intelligence and Security Informatics (ISI)}, year = {2020}, abstract = {

While Distributed Denial of Service (DDoS) attacks continue to plague the Internet, Software Defined Networks (SDNs) offer the promise of configuration elasticity for all devices in the plant to mitigate these attacks and stabilize the network in real-time. Since it is difficult to distinguish between legitimate and attack traffic, we propose a closed-loop feedback control mechanism using a multi-loop proportional, integral (PI) controller and a model predictive controller (MPC) that will regulate the system characteristics with disturbance rejection. Results from our setup implemented in the SDN platform show that our dynamic and predictive control models provide for a graceful degradation of real-time services in the SDN environment in real-time.

}, doi = {10.1109/ISI49825.2020.9280542}, author = {Vempati, Jagannadh and Dantu, Ram and Badruddoja, Syed and Thompson, Mark} } @conference {409, title = {Distributed Ledger for Spammers{\textquoteright} Resume}, booktitle = {2019 IEEE Conference on Communications and Network Security (CNS)}, year = {2019}, month = {June}, abstract = {

Unsolicited, and most likely spoofed, robot calls are not just an annoyance, but also carry a potential threat with the onset of automation, impersonation, and even voice manipulation technologies as malicious elements attempt to use deception to steal sensitive information or invoke action. Despite steps taken to protect consumers, the issue appears to be far from under control. In this paper, we propose a solution to use blockchain as a platform to share spam transactions through a peer-to-peer mechanism that will maintain a global database of reported spam transactions in order to identify and trace spam activity effectively. Storing spam transactions on a distributed ledger with consensus-based approval of transactions adds reliability to the data and can optimize the data points that will be available to spam detection algorithms in order to fight spam effectively. As this is peer to peer-based sharing, there is no need to rely on third-party providers for storing and sharing this data to the users. Every spam call received will be added as a detailed transaction on the blockchain to execute a smart contract that will calculate the trustworthiness of the caller. Call records are used to identify spam transactions while the blockchain ledgers store this data. We discuss the relevance and advantages of a distributed ledger to store these transactions. This paper does not aim at solving the spam problem with an optimized detection algorithm but evaluates the characteristics and performance of the blockchain as a distributed ledger and its relevance to serve as a platform for peer-to-peer spam detection mechanisms. We evaluate different blockchain metrics like transaction processing rates, gas costs and ledger sizes and discuss how they scale in order to store the spam reports data on the blockchain.

}, keywords = {blockchain, Peer-to-peer, Robocalls, smart contract, Spam detection}, doi = {10.1109/CNS.2019.8802789}, author = {Muttavarapu, Anudeep and Dantu, Ram and Thompson, Mark} } @conference {587, title = {Verifying OAuth Implementations Through Encrypted Network Analysis}, booktitle = {Proceedings of the 24th ACM Symposium on Access Control Models and Technologies}, year = {2019}, publisher = {Association for Computing Machinery}, organization = {Association for Computing Machinery}, address = {New York, NY, USA}, abstract = {

Verifying protocol implementations via application analysis can be cumbersome. Rapid development cycles of both the protocol and applications that use it can hinder up-to-date analysis. A better approach is to use formal models to characterize the applications platform and then verify the protocol through analysis of the network traffic tied to the models. To test this method, the popular protocol OAuth is considered. Currently, formal models of OAuth do not take into consideration the mobile environment, and implementation verification is largely based on code analysis. Our preliminary results are two fold; we sketch an extension to a formal model that incorporates the specifics of the Android platform and classify OAuth device types using machine learning on encrypted VPN traffic.

}, keywords = {Android, authorization, formal models, network analysis, oauth}, isbn = {9781450367530}, doi = {10.1145/3322431.3326449}, url = {https://doi.org/10.1145/3322431.3326449}, author = {Talkington, Josh and Dantu, Ram and Morozov, Kirill} } @article {370, title = {Bridging the Gap: Developing Innovative Minds Early On for Cybersecurity}, year = {2018}, author = {Thompson, Mark and Ram Dantu} } @article {364, title = {The Future is Connected: How Can it be Cyber Secure?}, year = {2018}, author = {Davis, Jeff and Khalfan, Shaun and Tiene, Rick and Ram Dantu} } @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} } @conference {382, title = {Performance Analysis of Elliptic Curves for Real-Time Video Encryption}, booktitle = {2018 National Cyber Summit (NCS)}, year = {2018}, month = {June}, abstract = {

The use of real-time video streaming is increasing day-by-day, and its security has become a serious issue now. Video encryption is a challenging task because of its large frame size. Video encryption can be done with symmetric key as well as asymmetric key encryption. Among different asymmetric key encryption technique, ECC performs better than other algorithms like RSA in terms of smaller key size and faster encryption and decryption operation. In this work, we have analyzed the performance of 18 different ECC curves and suggested some suitable curves for real-time video encryption.

}, keywords = {Delays, ECC, Elliptic curve cryptography, encryption, Jitter, real-time systems, Real-time video streaming, Security, Streaming media}, doi = {10.1109/NCS.2018.00015}, author = {Sen, Nilanjan and Ram Dantu and Vempati, Jagannadh and Thompson, Mark} } @conference {270, title = {Prediction of human error using eye movements patterns for unintentional insider threat detection}, booktitle = {2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA)}, year = {2018}, month = {Jan}, abstract = {

Threats from the inside of an organization{\textquoteright}s perimeters are a significant problem since it is difficult to distinguish them from benign activities. Recent reports indicate that the accidental/unintentional incidents account for the majority ofall insider security incidents. Human error is a major factor in unintentional insider threat. In this paper, we propose a novel approach for unintentional insider threat (UIT) detection and mitigation based on eye movement patterns. We perform experiments to capture unique characteristics of a user{\textquoteright}s eye movements as they perform several computer-based activities in different scenarios. The goal is to evaluate the effectiveness of using eye movement patterns in determining a user{\textquoteright}s subjective mental workload which is one of the main contributing factors to human error. We extract eye movement and pupil features which allow us to reliably achieve this goal. We evaluate our proposed approach using several classifiers and examine how different subsets of features affect the performance. The results show about 82\% accuracy on average for users wearing eye glasses and an average accuracy of 84.5\% for users without eye glasses. Our results demonstrate that users{\textquoteright} eye movement patterns and pupil behaviors can reveal valuable clues about their subjective mental workload and could be used in developing effective tools for unintentional insider threat detection and mitigation in real-world environments.

}, keywords = {accidental/unintentional incidents, Companies, eye, eye glasses, eye movement patterns, eye movements patterns, Feature extraction, Gaze tracking, Glass, human error prediction, insider security incidents, pupil feature extraction, Security, security of data, subjective mental workload, Task analysis, UIT detection, unintentional insider threat detection, Visualization}, doi = {10.1109/ISBA.2018.8311479}, author = {Takabi, Hassan and Hashem, Yassir and Ram Dantu} } @conference {340, title = {Uninterrupted Video Surveillance in the Face of an Attack}, booktitle = {2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)}, year = {2018}, month = {Aug}, abstract = {

Distributed denial of service (DDoS) attacks continue to plague businesses and consumers alike, and due to an ever-growing digital landscape, these attacks are expected to grow in size and complexity. Current mitigation techniques ranging from hours to days are completely unacceptable given the cost and inconvenience these attacks place in our society. This paper puts forth three feedback control mechanisms to minimize the effects of DDoS attacks on real-time traffic. The first, called differentiated services code point (DSCP) Markdown, is a passive approach that uses micro firewall rules to lower the priority of out-of-profile packets while a second mechanism actively drops the out-of-profile packets based on rate and burst size parameters. The third technique uses parallel links when feedback is applied to stabilize the network after an attack has been detected. Results from all three techniques have shown to have a positive effect on real-time traffic. The first two approaches were able to stabilize network traffic in real-time, while the parallel links technique resulted in a slight delay. We validate the feedback mechanisms with our model that was generated using the system identification technique. Results show that the feedback architecture provides a fit accuracy with positive results.

}, keywords = {attack mitigation techniques, authorisation, autoregressive processes, burst size parameters, Computer crime, computer network security, DDoS, DDoS attacks, differentiated services code point markdown, DiffServ networks, distributed denial of service attack, DSCP, DSCP Markdown approach, feedback control, feedback mechanisms, Internet, IP networks, Jitter, Mathematical model, micro-firewall, out-of-profile packets, parallel links technique, QoS, quality of service, real-time services, real-time systems, Resilient, Streaming media, system identification technique, uninterrupted video surveillance, video surveillance}, doi = {10.1109/TrustCom/BigDataSE.2018.00121}, author = {Vempati, Jagannadh and Ram Dantu and Thompson, Mark} } @conference {281, title = {Feedback Control for Resiliency in Face of an Attack}, booktitle = {Proceedings of the 12th Annual Conference on Cyber and Information Security Research}, year = {2017}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, abstract = {

Distributed Denial of Service(DDoS) attacks are inevitable. The existing defensive mechanisms are relatively outdated. In this paper, we present a passive mechanism to reduce the impact of an attack on the network. We designed and implemented a robust feedback architecture, to maintain the stability of the network despite attacks. During an attack, the controller of the feedback architecture detects the irregularities in the response time and the necessary changes are made to the configuration to maintain the network in steady state. In this approach first, we model the network using black-box system identification technique. Second, we validate the model with test data by conducting various experiments such as varying the network topology. Last, we test the model with the feedback architecture built in our lab environment. Results show that the feedback architecture provides an average model fit accuracy with positive results.

}, isbn = {978-1-4503-4855-3}, doi = {10.1145/3064814.3064815}, url = {http://doi.acm.org/10.1145/3064814.3064815}, author = {Vempati, Jagannadh and Thompson, Mark and Ram Dantu} } @conference {316, title = {Insider Threat Detection Based on Users{\textquoteright} Mouse Movements and Keystrokes Behavior}, booktitle = {Secure Knowledge Management Workshop}, year = {2017}, month = {10/2017}, abstract = {

Insider threat is considered as one of the most\  serious threats in cybersecurity and has been a prime security\  concern for government and industry. Traditional approaches\  can{\textquoteright}t provide efficient solutions, and the threat keeps raising.\  In this paper, we propose a new approach to insider threat\  detection and prediction based on the user{\textquoteright}s mouse movements\  and keystrokes behavior. We conduct human subject experiments\  with 30 participants and capture their mouse movements and\  keystroke dynamics as they perform several computer-based\  activities in both benign and malicious scenarios. We extract\  features and evaluate our approach using several classifiers and\  statistical analysis measures. The results show that participants\  performing malicious tasks showed faster speed and longer mouse\  movements, and long left click and keystroke duration than the\  benign tasks. Our results suggest that users{\textquoteright} mouse movements\  and keystrokes behavior can reveal valuable knowledge about\  their malicious behavior and can be used as indicators in the\  insider threat monitoring and detection frameworks.

}, author = {Hashem, Yassir and Takabi, Hassan and Ram Dantu} } @conference {269, title = {A Multi-Modal Neuro-Physiological Study of Malicious Insider Threats}, booktitle = {Proceedings of the 2017 International Workshop on Managing Insider Security Threats}, year = {2017}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, keywords = {electroencephalogram (eeg), eye tracking, insider threat, neuroscience}, isbn = {978-1-4503-5177-5}, doi = {10.1145/3139923.3139930}, url = {http://doi.acm.org/10.1145/3139923.3139930}, author = {Hashem, Yassir and Takabi, Hassan and Ram Dantu and Nielsen, Rodney} } @conference {262, title = {Optimized and Secured Transmission and Retrieval of Vital Signs from Remote Devices}, booktitle = {2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)}, year = {2017}, month = {July}, abstract = {

Smartphones and other mobile platforms provide a low cost and easily accessible method of monitoring patient health, and aid healthcare professionals in early detection of disease. Immediate access to the gathered data is an essential factor in effective patient care. But the current processes used for patients{\textquoteright} vital data collection is slow and error prone. This undermines the advantages of remote monitoring that mobile platforms for health monitoring provide. In this paper, we propose to upload the patient health information to the Cloud. We investigate three different models to transfer data from the smartphone to the Cloud-perform all computations in the smartphone, perform all computations in the Cloud, and divide the computations between the smartphone and the Cloud. The second approach was found to be infeasible due to very high latency in data transfer with a delay of 2.84 seconds at an upload speed of 2500 KBytes per second. In order to protect the privacy of patients, it is required by law that the data gathered from remote monitoring by using mobile platforms must be kept private, and be secured before uploading to the Cloud. This paper explores the use of prominent public key encryption algorithms and their performance on a mobile device to securely transmit confidential electronic personal health information to the Cloud. We analyze performance of three common public key encryption schemes -RSA, Diffie-Hellman, and ECC. It is shown that 160 bit key size in ECC scheme provides the same level of security that a 1024 bit key size does in RSA and Diffie-Hellman. Further, the encryption and decryption time required by ECC is three times less than the other two schemes. Hence, ECC not only requires a smaller key size to provide the same level of security, but also faster encryption and decryption times as compared to the other two schemes. This makes ECC algorithms suitable to be implemented in resource constrained mobile platforms. We also compared ECC curves from three different standards - NIST, SECG, and Brainpool - to determine the optimum ECC curve, and key size to encrypt data in the mobile phone platform. It is shown that the Brainpool curve performed better than the other two standards when the key size is less than 521 bits. We also measured the latency of uploading encrypted data in a wide variety of WiFi and mobile networks.

}, keywords = {Brainpool curve, Cloud computing, confidential electronic personal health information, data transfer, decryption, Diffie-Hellman, Diseases, ECC algorithms, ECC curves, encryption, error prone, health care, healthcare professionals, Mobile communication, mobile device, mobile health, mobile networks, mobile phone platform, patient care, patient health information, patient health monitoring, patient vital data collection, Performance evaluation, public key cryptography, public key encryption algorithms, remote devices, remote monitoring, resource constrained mobile platforms, RSA, secured transmission, Security, servers, smart phones, smartphones, vital signs, WiFi}, doi = {10.1109/CHASE.2017.55}, author = {Thiyagaraja, Shanti and Ram Dantu and Shrestha, Pradhumna and Thompson, Mark and Smith, Christopher} } @article {273, title = {Inside the Mind of the Insider: Towards Insider Threat Detection Using Psychophysiological Signals}, journal = {Journal of Internet Services and Information Security}, volume = {6}, year = {2016}, month = {02}, pages = {20-36}, author = {Hashem, Yassir and Takabi, Hassan and GhasemiGol, Mohammad and Ram Dantu} } @conference {264, title = {Realizing Optimal Chest Compression Fraction During Cardiopulmonary Resuscitation}, booktitle = {2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)}, year = {2016}, month = {June}, abstract = {

Cardiopulmonary Resuscitation (CPR) is usually preformed in complex situations with multiple parties with a wide range of capabilities. These situations require intermediate pauses for defibrillation, applying advanced airways, and switching CPR administrators. One of the biggest unknowns if chest compression fraction (CCF), which is the optimal fraction of time spent applying compressions. Using the American Heart Association (AHA) guidelines for CPR and rational actor models from game theory, we show the validity of the recommended CCF set by the by the AHA.

}, keywords = {airways, Cardiac arrest, cardiopulmonary resuscitation, cardiovascular system, Chest Compression Fraction, CPR, CPR administrator switching, defibrillation, defibrillators, game theory, Games, Guidelines, Heart, lung, Measurement, optimal chest compression fraction, pneumodynamics, rational actor models}, doi = {10.1109/CHASE.2016.41}, author = {Talkington, Josh and Ram Dantu} } @conference {88, title = {A framework for secured collaboration in mHealth}, booktitle = {Collaboration Technologies and Systems (CTS), 2015 International Conference on}, year = {2015}, month = {06/2015}, abstract = {

We have designed a novel framework of services, protocols and technologies to ensure the secure collaboration in M2M networks, specifically in mobile health. The promise of mobile health to reform preventive self-care opens new doors for remote monitoring to improve health care communication. With cardiopulmonary resuscitation (CPR) as an example, we classify our M2M elements into services, roles, human-computer protocols and technologies through which we require trust, anonymity, scalability, and active detachment. We simulate a scenario in which a patient needs CPR and through the use of widely available technologies (such as a smartphone and secure web sockets) we demonstrate a technological collaboration that facilitates secure emergency mobile health services.

}, keywords = {cardiology, cardiopulmonary resuscitation, Cardiopulmonary resuscitation(CPR), Collaboration, Computers, CPR, health care, health care communication, human computer interaction, human-computer protocol, M2M network, machine to machine(M2M), Medical services, mHealth, Mobile communication, mobile computing, mobile health, Protocols, remote monitoring, Scalability, secure collaboration, secure emergency mobile health services, secured collaboration, security of data, servers, smart phones, smartphone}, doi = {10.1109/CTS.2015.7210450}, author = {Widick, Logan and Talkington, Josh and Bajwa, Garima and Ram Dantu} } @conference {268, title = {Towards Insider Threat Detection Using Psychophysiological Signals}, booktitle = {Proceedings of the 7th ACM CCS International Workshop on Managing Insider Security Threats}, year = {2015}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, keywords = {brain computer interface, electroencephalograph, insider threat detection, physiological indicators}, isbn = {978-1-4503-3824-0}, doi = {10.1145/2808783.2808792}, url = {http://doi.acm.org/10.1145/2808783.2808792}, author = {Hashem, Yassir and Takabi, Hassan and GhasemiGol, Mohammad and Ram Dantu} } @conference {100, title = {Unintentional bugs to vulnerability mapping in Android applications}, booktitle = {Intelligence and Security Informatics (ISI), 2015 IEEE International Conference on}, year = {2015}, month = {May}, abstract = {

The intention of an Android application, determined by the source code analysis is used to identify potential maliciousness in that application (app). Similarly, it is possible to analyze the unintentional behaviors of an app to identify and reduce the window of vulnerabilities. Unintentional behaviors of an app can be any developmental loopholes such as software bugs overlooked by a developer or introduced by an adversary intentionally. FindBugsTM and Android Lint are a couple of tools that can detect such bugs easily. A software bug can cause many security vulnerabilities (known or unknown) and vice-versa, thus, creating a many-to-many mapping. In our approach, we construct a matrix of mapping between the bugs and the potential vulnerabilities. A software bug detection tool is used to identify a list of bugs and create an empirical list of the vulnerabilities in an app. The many-to-many mapping matrix is obtained by two approaches - severity mapping and probability mapping. These mappings can be used as tools to measure the unknown vulnerabilities and their strength. We believe our study is the first of its kind and it can enhance the security of Android apps in their development phase itself. Also, the reverse mapping matrix (vulnerabilities to bugs) could be used to improve the accuracy of malware detection in Android apps.

}, keywords = {Android (operating system), Android applications, Android Lint, Androids, bugs, Computer bugs, Conferences, development phase, developmental loopholes, FindBugsTM, Humanoid robots, Indexes, invasive software, malware detection, many-to-many mapping matrix, mapping, matrix algebra, probability, probability mapping, program debugging, reverse mapping matrix, Security, security vulnerabilities, severity mapping, Software, software bug detection tool, software bugs, software tools, source code (software), source code analysis, unintentional behavior analysis, unintentional bugs, vulnerability, vulnerability mapping}, doi = {10.1109/ISI.2015.7165966}, author = {Bajwa, Garima and Fazeen, Mohamed and Ram Dantu and Tanpure, Sonal} } @conference {216, title = {Smart phone monitoring of second heart sound split}, booktitle = {2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society}, year = {2014}, month = {Aug}, abstract = {

Heart Auscultation (listening to heart sounds) is the basic element of cardiac diagnosis. The interpretation of these sounds is a difficult skill to acquire. In this work we have developed an application to detect, monitor, and analyze the split in second heart sound (S2) using a smart phone. The application records the heartbeat using a stethoscope connected to the smart phone. The audio signal is converted into the frequency domain using Fast Fourier Transform to detect the first and second heart sounds (S1 and S2). S2 is extracted and fed into the Discrete Wavelet Transform (DWT) and then to Continuous Wavelet Transform (CWT) to detect the Aortic (A2) and the Pulmonic (P2) components, which are used to calculate the split in S2. With our application, users can continuously monitor their second heart sound irrespective of ages and check for a split in their hearts with a low-cost, easily available equipment.

}, keywords = {acoustic signal processing, aortic component, biomedical equipment, Biomedical monitoring, cardiac diagnosis, cardiology, continuous wavelet transform, Continuous wavelet transforms, discrete wavelet transform, discrete wavelet transforms, fast Fourier transform, fast Fourier transforms, frequency domain, Heart, heart auscultation, heartbeat recording, medical signal processing, Monitoring, patient diagnosis, patient monitoring, pulmonic component, second heart sound split, smart phone monitoring, smart phones, stethoscope}, doi = {10.1109/EMBC.2014.6944050}, author = {Thiyagaraja, Shanti and Vempati, Jagannadh and Ram Dantu and Sarma, Tom and Dantu, Siva} } @article {15, title = {Cuffless Differential Blood Pressure Estimation Using Smart Phones}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {60}, year = {2013}, pages = {1080 - 1089}, abstract = {

Smart phones today have become increasingly popular with the general public for their diverse functionalities such as navigation, social networking, and multimedia facilities. These phones are equipped with high-end processors, high-resolution cameras, and built-in sensors such as accelerometer, orientation-sensor, and light-sensor. According to comScore survey, 26.2\% of U.S. adults use smart phones in their daily lives. Motivated by this statistic and the diverse capability of smart phones, we focus on utilizing them for biomedical applications. We present a new application of the smart phone with its built-in camera and microphone replacing the traditional stethoscope and cuff-based measurement technique, to quantify vital signs such as heart rate and blood pressure. We propose two differential blood pressure estimating techniques using the heartbeat and pulse data. The first method uses two smart phones whereas the second method replaces one of the phones with a customized external microphone. We estimate the systolic and diastolic pressure in the two techniques by computing the pulse pressure and the stroke volume from the data recorded. By comparing the estimated blood pressure values with those measured using a commercial blood pressure meter, we obtained encouraging results of 95{\textendash}100\% accuracy.

}, keywords = {Blood pressure, cuffless, Estimation, finger pulse, Heart, Microphones, mobile camera, Mobile communication, mobile phone, Synchronization, Valves, vascular transit time}, doi = {10.1109/TBME.2012.2211078}, author = {Chandrasekaran, Vikram and Ram Dantu and Jonnada, Srikanth and Thiyagaraja, Shanti and Subbu, Kalyan} } @conference {95, title = {Finger Blood Flow Monitoring Using Smart Phones}, booktitle = {Proceedings of the 8th International Conference on Body Area Networks}, year = {2013}, publisher = {ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)}, organization = {ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)}, address = {ICST, Brussels, Belgium, Belgium}, abstract = {

The use of smart phones in healthcare applications is growing steadily. The inbuilt sensors are used to estimate the value of physiological data from human body. With progressive innovation, smart phone based medical applications will continue to be developed at an exponential rate. In this paper, we show that a smart phone can be used to monitor the blood flow in finger, based on the pulse height obtained from the fingertips. This is achieved by using the camera lens and the flash light of the smart phone. The height of the pulse rises along with the surrounding temperature indicating that the blood flow increases when the temperature becomes warmer. This study shows that there is a potential to monitor regulation of body temperature using smart phone.

}, keywords = {finger blood flow, finger pulse, mobile health, smart phone}, isbn = {978-1-936968-89-3}, doi = {10.4108/icst.bodynets.2013.253704}, url = {http://dx.doi.org/10.4108/icst.bodynets.2013.253704}, author = {Thiyagaraja, Shanti and Ram Dantu} } @article {72, title = {Experiences During a Collegiate Cyber Defense Competition}, journal = {Journal of Applied Security Research}, volume = {5}, year = {2010}, pages = {382{\textendash}396}, abstract = {

The objective of this article is to encourage schools to participate in the Collegiate Cyber Defense Competition (CCDC) and to ease their entry by providing information about the event and describing the experiences of both the student participants and the educators. This article focuses mainly on the recent experience of a University of North Texas student team at the Southwest Regional CCDC 2008 hosted by Del Mar College in Corpus Christi, Texas. It describes the entire process of participating in the CCDC, including announcements, team formation, task assignments, preparations, and actual team experience during the competition and provides suggestions on strategies for future competitions

}, keywords = {Cyber defense competition, network configurations, operating systems, servers}, author = {Sroufe, Paul and Tate, Steve and Ram Dantu and Cankaya, Ebru} } @conference {53, title = {BBN-based Privacy Management System for Facebook}, 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 = {

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{\textquoteright} 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{\textquoteright}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.

}, isbn = {978-1-4244-4171-6}, url = {http://dl.acm.org/citation.cfm?id=1706428.1706467}, author = {Baatarjav, Enkh-Amgalan and Ram Dantu and Tang, Yan and Cangussu, Jo{\~a}o} } @conference {38, title = {Discovery of Social Groups Using Call Detail Records}, booktitle = {On the Move to Meaningful Internet Systems: OTM 2008 Workshops}, year = {2008}, month = {11/2008}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, address = {Monterrey, Mexico}, abstract = {

In this paper we propose the affinity model for classifying social groups based on mobile phone call detail records. We use affinity to measure the similarity between probability distributions. Since phone calls are stochastic process, it makes more sense to use probability affinity to classify the social groups. This work is useful for enhancing homeland security, detecting unwanted calls (e.g., spam) and product marketing. For validation of our results, we used actual call logs of 100 users collected at MIT by the Reality Mining Project group for a period of 8 months. The experimental results show that our model achieves good performance with high accuracy.

}, isbn = {978-3-540-88875-8}, doi = {10.1007/978-3-540-88875-8_72}, url = {http://dx.doi.org/10.1007/978-3-540-88875-8_72}, author = {Zhang, Huiqi and Ram Dantu}, editor = {Meersman, Robert and Tari, Zahir and Herrero, Pilar} } @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 {81, title = {Sensitivity Analysis of an Attack Containment Model}, booktitle = {Proceedings of the 2005 IEEE International Conference on Intelligence and Security Informatics}, year = {2005}, publisher = {Springer-Verlag}, organization = {Springer-Verlag}, address = {Atlanta, GA}, abstract = {

A feedback control model has been previously proposed to regulate the number of connections at different levels of a network. This regulation is applied in the presence of a worm attack resulting in a slow down of the spreading worm allowing time to human reaction to properly eliminate the worm in the infected hosts. The feedback model constitutes of two queues, one for safe connections and another for suspected connections. The behavior of the proposed model is based on three input parameters to the model. These parameters are: (i) the portion of new connection requests to be sent to the suspect queue, (ii) the number of requests to be transferred from the suspect to the safe queue, and (iii) the time out value of the requests waiting in the suspect queue. The more we understand the effects of these parameters on the model, the better we can calibrate the model. Based on this necessity, a sensitivity analysis of the model is presented here. The analysis allows for the computation of the effects of changing parameters in the output of the model. In addition, the use of a sensitivity matrix permits the computations of not only changes in one parameter but also combined changes of these parameters. From the sensitivity analysis we have verified our assumption that the changes in the input parameters have no effect on the overall system stability. However, there will be a short period of instability before reaching a stable state.

}, isbn = {3-540-25999-6, 978-3-540-25999-2}, doi = {10.1007/11427995_11}, url = {http://dx.doi.org/10.1007/11427995_11}, author = {Ram Dantu and Cangussu, Jo{\~a}o and Turi, Janos} }