@conference {570, title = {Making Blockchain Validators Honest}, booktitle = {2022 Fourth International Conference on Blockchain Computing and Applications (BCCA)}, year = {2022}, abstract = {

The importance of honesty among blockchain validators can not be overemphasized, especially as blockchain is used by many as an underlying technology for the development of various Industry 4.0 solutions. In the blockchain consensus process, validators validate the correctness of transactions and propose new blocks for addition to the blockchain. They are rewarded for this task with the blockchain native token (e.g., ETH on the Ethereum blockchain). This reward is often distributed among validators with respect to their staked amount. An increasing number of validators joining the network leads to a decreasing chance of a validator being chosen for the validation task and thus a reduction in the validation reward. This situation results in some form of competition among the validators, leading them to carry out various malicious actions to influence the blockchain validator selection protocol in order to be chosen. In this paper, we examine the competitive interactions between validators in a blockchain consensus process and propose a model using an infinitely repeated game model that ensures that the validators are deterred from behaving maliciously while also encouraging a self-policing notion due to the extra incentive mechanism of an improved reputation score when a validator can verifiably report malicious activities by others. Further, we discuss the factors that can incentivize or disincentivize a validator to either continue to behave honestly or switch to dishonest behavior.

}, doi = {10.1109/BCCA55292.2022.9921952}, url = {https://ieeexplore.ieee.org/abstract/document/9921952}, author = {Salau, Abiola and Dantu, Ram and Morozov, Kirill and Badruddoja, Syed and Upadhyay, Kritagya} } @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 {503, title = {Towards a Threat Model and Security Analysis for Data Cooperatives}, booktitle = {19th International Conference on Security and Cryptography (SECRYPT 2022)}, year = {2022}, abstract = {

Data cooperative (called {\textquotedblleft}data coop{\textquotedblright} for short) is an emerging approach in the area of secure data management. It promises its users a better protection and control of their data, as compared to the traditional way of their handling by the data collectors (such as governments, big data companies, and others). However, for the success of data coops, existing challenges with respect to data management systems need to be adequately addressed. Especially, they concern terms of security and privacy, as well as the power imbalance between providers/owners and collectors of data. Designing a security and privacy model for a data coop requires a systematic threat modeling approach that identifies the security landscape, attack vectors, threats, and vulnerabilities, as well as the respective mitigation strategies. In this paper, we analyze the security of data cooperatives, identify potential security risks and threats, and suggest adequate countermeasures. We also discuss existing challenges that hinder the widespread adoption of data coops.\ 

}, keywords = {Cybersecurity, Data Cooperatives, Secure Data Management, Security and Privacy., Threat Model}, doi = {10.5220/0011328700003283}, author = {Salau, Abiola and Dantu, Ram and Morozov, Kirill and Upadhyay, Kritagya and Badruddoja, Syed} } @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 {522, title = {Can{\textquoteright}t Understand SLAs? Use the Smart Contract}, booktitle = {2021 Third IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)}, year = {2021}, publisher = {IEEE}, organization = {IEEE}, address = {Atlanta, GA, USA}, abstract = {

A Service Level Agreement (SLA) is a special kind of legal contract that binds a vendor to its customers where the vendor commits to provide certain services in exchange for certain payment from the customers. On the other hand, a Smart Contract is a contract that is a computer program that also binds multiple parties into given agreements but is a set of a precise rules and is self-enforceable and self-executable. Since almost all legal contracts are ambiguous by nature and are complex to read and understand, we perform a novel study on how we can replace the traditional vague legal contract with the smart contact and the effect of the ambiguity on the smart contract by performing a thorough analysis on SLAs by measuring their ambiguities in various aspects. We take several samples of real SLAs from six different popular broadband vendors. We use four random SLAs to train the machine learning model to classify and then detect ambiguous words in two unseen SLAs which were the SLAs of Ziply Fiber and Century Link. As different people form different interpretations while reading the ambiguous legal contracts, we generate various human interpretations from the machine detected ambiguous words and convert all those generated interpretations into Smart Contracts to perform testing in Ethereum-based Blockchain to identify the most ambiguous as well as accurate interpretation of the SLA. From our analysis and observation, we were able to find out the most ambiguous interpretation of SLAs and we concluded that the SLA of Ziply Fiber was more ambiguous in general compared to the SLA of Century Link. Moreover, our proposed approach to detect ambiguous terms and to translate an ambiguous legal contract to a smart legal contract using a formal language to measure the degree of ambiguity can be extrapolated and replicated to legal contracts from other types of industries as well.

}, keywords = {ambiguity, ambiguity index, blockchain, clauses, complexity, Ethereum, interpretations, machine learning, service level agreement, SLA, smart contract, smart legal contract}, isbn = {978-1-6654-1623-8}, doi = {10.1109/TPSISA52974.2021.00015}, url = {https://ieeexplore.ieee.org/abstract/document/9750250}, author = {Upadhyay, Kritagya and Dantu, Ram and He, Yanyan and Badruddoja, Syed and Salau, Abiola} } @conference {523, title = {A Collaborative and Adaptive Feedback System for Physical Exercises}, booktitle = {2021 IEEE 7th International Conference on Collaboration and Internet Computing (CIC)}, year = {2021}, publisher = {IEEE}, organization = {IEEE}, address = {Atlanta, GA, USA}, abstract = {

Maintaining motivation to meet physical exercise goals is a big challenge in virtual/home-based exercise guidance systems. Lack of motivation, long-maintained bad daily routines, and fear of injury are some of the reasons that cause this hesitation. This paper proposes a reinforcement learning-based virtual exercise assistant capable of providing encouragement and customized feedback on body movement form over time. Repeated arm curls were observed and tracked using single and dual-camera systems using the Posenet pose estimation library. To accumulate enough experience across individuals, the reinforcement learning model was collaboratively trained by subjects. The proposed system is tested on 36 subjects. Behavioral changes are apparent in 31 of the 36 subjects, with 31 subjects reducing movement errors over time and 15 subjects completely eliminating the errors. The system was analyzed for which types of feedback provided the highest expected value, and feedback directly related to the previous mistake provided the highest valued feedback ( p\<0.0133 ). The result showed that the Reinforcement Learning system provides meaningful feedback and positively impacts behavior progress.

}, keywords = {Distributed Machine Learning, Human-Computer Interaction, Pose Estimation, Reinforcement Learning}, isbn = {978-1-6654-1625-2}, doi = {10.1109/CIC52973.2021.00012}, url = {https://ieeexplore.ieee.org/abstract/document/9707166}, author = {Ranasinghe, Ishan and Yuan, Chengping and Dantu, Ram and Albert, Mark V.} } @conference {543, title = {Cyber-Physiotherapy: Rehabilitation to Training}, booktitle = {2021 IFIP/IEEE International Symposium on Integrated Network Management (IM)}, year = {2021}, publisher = {IEEE}, organization = {IEEE}, address = {Bordeaux, France}, abstract = {

Cutting-edge Human-Computer Interaction (HCI) technologies embedded with Machine Learning (ML) will cause a paradigm shift in various domains, including manufacturing and developing facilities and services for professional and personal use. ML implemented HCIs can help people overcome societal challenges brought about by the COVID-19 pandemic. We introduce a system for people to perform physical exercises at home. This system is intended to help a range of demographics, from non-critical physical therapy patients to experienced weightlifters. More specifically, we propose a method to assess the difficulty of an exercise for visual exercise tracking systems. Pose estimation tracks exercises and reinforcement learning provides autonomous feedback to the user (patient/athlete). This information is processed largely on the client side, allowing the application to run smoothly anywhere in the world.

}, keywords = {Fitts{\textquoteright}s law, Human-Computer Interaction, Index of Difficulty, Pose Estimation, Reinforcement Learning}, isbn = {978-3-903176-32-4}, url = {https://ieeexplore.ieee.org/abstract/document/9464036}, author = {Ranasinghe, Ishan and Dantu, Ram and Albert, Mark V. and Watts, Sam and Ocana, Ruben} } @conference {528, title = {Data Cooperatives for Neighborhood Watch}, booktitle = {2021 IEEE International Conference on Blockchain and Cryptocurrency (ICBC)}, year = {2021}, publisher = {IEEE}, organization = {IEEE}, address = {Sydney, Australia}, abstract = {

The increasing proliferation of user data is moving the world from the era of {\textquoteright}big data{\textquoteright} to a new era of shared data, and some are considering data as a factor of production that is paving the way for a new business economy. In this paper, we propose a solution that uses blockchain technology as a platform for online neighborhood watch using a form of data cooperative among individuals or organizations in the sharing of data through a peer-to-peer mechanism. We prove the concept by implementing a distributed phishing data sharing system that will maintain a community ledger of reported phishing activities with a consensus-based approval of the phishing transaction and a novel reputation scoring system thereby adding reliability to the system and effectively tackling the phishing problem. The data cooperative provides a way for timely multi-party sharing of phishing data among anti-phishing organizations and users of the internet eliminating the current approach of each organization maintaining its database. Our results show that blockchain is effective in complementing the existing methods of phishing detection and serves as a platform for sharing phishing data with respect to scalability, cost, and memory consumption. Also, our results further show that transaction times on the Ropsten test net follow a Gamma distribution. Our approach can be extrapolated to other data sharing systems like medical data, spam calls, discussion forums, etc.

}, keywords = {blockchain, Cybersecurity, data cooperative, data sharing, distributed ledger, peer-to- peer, phishing, smart contract}, isbn = {978-1-6654-3578-9}, doi = {10.1109/ICBC51069.2021.9461056}, url = {https://ieeexplore.ieee.org/abstract/document/9461056}, author = {Salau, Abiola and Dantu, Ram and Upadhyay, Kritagya} } @conference {513, title = {Make Consumers Happy by Defuzzifying the Service Level Agreements}, booktitle = {2021 Third IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)}, year = {2021}, publisher = {IEEE}, organization = {IEEE}, address = {Atlanta, GA, USA}, abstract = {

A Service Level Agreement (SLA) is a special kind of legal contract that binds a vendor to its customers where the vendor commits to provide certain services in exchange for certain payments from the customers. However, when customers do not get the services that they have subscribed for, it becomes a laborious job for customers to contact or visit the company and claim the correct amount of compensation or service credits. On the other hand, a Smart Contract is a contract that is a computer program that also binds multiple parties into given agreements but is a set of precise rules and is self-enforceable and self-executable. In this paper, we have introduced a novel work where we use fuzzy logic inside the Ethereum-based smart contract for two significant objectives. The first objective is to make the claim of the compensation easier and faster for customers by translating the SLA into a smart contract. The second objective is to make the smart contract even smarter and intelligent by implementing fuzzy logic so that customers who have a hard time understanding the legal jargon and ambiguities of the legal contract and SLA to find out if the compensation amount they are getting when the service is poor is good enough. Since fuzzy logic models semantics of linguistic expressions by capturing vagueness in the fuzzy sets, it becomes easier to solve the problem of contractual ambiguities and expedite the process of claiming compensation when implemented in a Blockchain-based smart contract.

}, keywords = {ambiguity, blockchain, clauses, complexity, Ethereum, Fuzzy logic, interpretations, service level agreement, SLA, smart contract, smart legal contract}, isbn = {978-1-6654-1623-8}, doi = {10.1109/TPSISA52974.2021.00011}, url = {https://ieeexplore.ieee.org/abstract/document/9750258}, author = {Upadhyay, Kritagya and Dantu, Ram and He, Yanyan and Salau, Abiola and Badruddoja, Syed} } @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 {518, title = {Paradigm Shift from Paper Contracts to Smart Contracts}, booktitle = {2021 Third IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)}, year = {2021}, publisher = {IEEE}, organization = {IEEE}, address = {Atlanta, GA, USA}, abstract = {

The ambiguity and complexity of the traditional legal contracts have motivated the study and exploration of a better and advanced contract known as blockchain-based smart contracts. A smart contract is a self-executable contract where the terms of the agreement between the involved parties are directly written into the lines of code that resides in the distributed ledger technology known as the blockchain. Obtaining a better understanding of smart contracts to overcome the fundamental issues of traditional legal contracts is vital for the successful and faster dispute settlement process without the intervention of any third-party mediators like courts, banks, lawyers, etc. In this paper, we present a comprehensive overview of the key features of the paradigm shift from traditional paper contracts to smart contracts. In addition, we also discuss why smart contracts are necessary to be legally enforced and the crucial conditions that are required for them to be legally enforceable. Furthermore, we outline recent trends and emerging technologies such as Natural Language Processing, Machine Learning, and the Internet of Things that have been combined together with smart contracts.

}, keywords = {ambiguity, artificial intelligence, blockchain, electronic contract, enforceability, Ethereum, interpretations, smart contract, traditional legal contract}, isbn = {978-1-6654-1623-8}, doi = {10.1109/TPSISA52974.2021.00029}, url = {https://ieeexplore.ieee.org/abstract/document/9750226}, author = {Upadhyay, Kritagya and Dantu, Ram and He, Yanyan and Salau, Abiola and Badruddoja, Syed} } @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 {538, title = {Integrating DOTS With Blockchain Can Secure Massive IoT Sensors}, booktitle = {2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)}, year = {2020}, publisher = {IEEE}, organization = {IEEE}, address = {New Orleans, LA, USA}, abstract = {

This paper presents a novel approach to securing IoT devices by leveraging DDoS Open Threat Signaling (DOTS) architecture on a Blockchain framework. Like many areas of the information technology domain, IoT sensors are also prone to attacks but on a larger scale. There are millions of devices being connected to a central domain to provide different types of services. Since these low-powered IoT devices have constrained technical requirements with less computational capabilities, they lack the capacity to judge their behavior as benign or malignant. IoT relies heavily on the higher level of intelligent nodes to decide on their status. An IoT Controller/Edge server handles the registration and the limited management of devices. Since traditional security is unable to protect the IoT environment sufficiently, we present a Blockchain-based DDoS detection approach to secure and mitigate such attacks in the IoT environment. Our test setup includes dataset from four sensors over two months. These values were tested using a threshold calculation against the variation of temperature, humidity, pressure, and wind direction on that day to find out whether an IoT sensor is under a DDoS attack. Our results show how DOTS can help in detection of attack when mapped on IoT edge computing.

}, keywords = {blockchain, Cybersecurity, DDOS Protection, distributed ledger, Edge Computing, IoT, Smart Contracts}, isbn = {978-1-7281-7445-7}, doi = {10.1109/IPDPSW50202.2020.00156}, url = {https://ieeexplore.ieee.org/abstract/document/9150345}, author = {Badruddoja, Syed and Dantu, Ram and Widick, Logan and Zaccagni, Zachary and Upadhyay, Kritagya} } @conference {547, title = {Is your legal contract ambiguous? Convert to a smart legal contract}, booktitle = {2020 IEEE International Conference on Blockchain (Blockchain)}, year = {2020}, publisher = {IEEE}, organization = {IEEE}, abstract = {

A legal contract is something that is in spoken or in written form, which binds a party or multiple parties into given terms and conditions. On the other hand, a smart contract is also a contract which is a computer program that binds parties into given terms and conditions but unlike a legal contract, it is self-executable, efficient, and unambiguous. Almost all legal contracts are complex while reading because of its ambiguous nature. In this paper, we take a real-world ambiguous legal contact as a test contract, and generate various interpretations from it, convert all those interpretations into the smart legal contracts and identify the most ambiguous and accurate smart legal contract by performing various measurements such as transaction fees and ambiguity index for each interpretation. We came to the conclusion that the most ambiguous legal contract would be the contract with general interpretation as it was more complex when written in the smart contract and had many possible interpretations due to ambiguity than the rest of the interpretations.

}, author = {Upadhyay, Kritagya and Dantu, Ram and Zaccagni, Zachary and Badruddoja, Syed} } @conference {403, title = {Blockchain Based Authentication and Authorization Framework for Remote Collaboration Systems}, booktitle = {2019 IEEE 20th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)}, year = {2019}, month = {June}, abstract = {

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

}, keywords = {Access Control, authorization, blockchain, Digital Certificate, IoT, remote collaboration, Security}, doi = {10.1109/WoWMoM.2019.8792994}, author = {Widick, Logan and Ranasinghe, Ishan and Dantu, Ram and Jonnada, Srikanth} } @conference {397, title = {CMCAP: Ephemeral Sandboxes for Adaptive Access Control}, booktitle = {Proceedings of the 24th ACM Symposium on Access Control Models and Technologies}, year = {2019}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, keywords = {adaptive access control, ephemeral sandboxes, information flow control, intrusion prevention, runtime containment}, isbn = {978-1-4503-6753-0}, doi = {10.1145/3322431.3325414}, url = {http://doi.acm.org/10.1145/3322431.3325414}, author = {Bucuti, Theogene and Dantu, Ram and Morozov, Kirill} } @article {415, title = {Detecting driver distraction using stimuli-response EEG analysis}, journal = {CoRR}, volume = {abs/1904.09100}, year = {2019}, url = {http://arxiv.org/abs/1904.09100}, author = {Bajwa, Garima and Fazeen, Mohamed and Dantu, Ram} } @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 {582, title = {Micro-Accreditation for Matching Employer E-Hire Needs}, booktitle = {2019 IEEE International Conference on Blockchain (Blockchain)}, year = {2019}, abstract = {

This paper presents a novel way to help match employers{\textquoteright} knowledge requirements with students{\textquoteright} knowledge earned using blockchain{\textquoteright}s smart contracts to assure credentials and track student records. This decentralized approach proposes using the micro-accreditation of topics from the CAE framework to courses and associated tasks, while introducing a revolutionary idea of a blockchain-based peer-reviewed rigor score assignment. Our work and result metrics were completed in Ethereum and connected test networks. We concluded this new approach is mostly efficient and scalable depending on the network load, with faster transaction times when the miners are properly incentivized. Future work will include further fine-tuning of the transaction algorithms to improve time, as well as an investigation into a better consensus model for peer review and rigor determination.

}, doi = {10.1109/Blockchain.2019.00054}, author = {Zaccagni, Zachary and Paul, Aditya and Dantu, Ram} } @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} }