@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} } @article {370, title = {Bridging the Gap: Developing Innovative Minds Early On for Cybersecurity}, year = {2018}, author = {Thompson, Mark 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 {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 {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} }