@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 {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 {334, title = {Automatic Feedback Control for Graceful Degradation of Real-Time Services in the Face of an Attack}, booktitle = {2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC)}, year = {2018}, month = {Oct}, abstract = {

Distributed denial of service (DDoS) attacks continue to pose a serious threat to various businesses and consumers. With the growth in the number of devices connected to the internet, these attacks continue to grow in number. Despite the availability of security tools, the attacks continue to happen and are causing various businesses to sweat. These tools may take anywhere from a few hours to a few days to counter the attacks, which is unacceptable. In this paper, we put forth a novel feedback control mechanism to minimize the effect of volumetric attacks such as DDoS. During an attack, the feedback control model detects and reduces the impact of the attack by maintaining the service level agreements (SLA) of the network service. The controller makes intelligent decisions to ensure the quality of service (QoS) metrics are gracefully degraded by tuning the micro-firewall rules such as the committed information rate and burst size. A proportional Integral (PI) controller is used as a closed-loop feedback controller to maintain the stability of the network in spite of an attack. This proposed architecture is verified in a lab setup, and we observe that we are able to minimize the degradation of the real-time service so that the user{\textquoteright}s quality of experience (QoE) is preserved. We validate the proposed architecture with a model generated by using the system identification technique. Results from the setup show that the closed-loop feedback control model stabilizes the network in real-time.

}, keywords = {authorisation, automatic feedback control, closed loop systems, closed-loop feedback control model, Computer crime, computer network management, computer network security, DDoS, Distributed Denial of Service attacks, feedback control, Graceful Degradation, Internet, Mathematical model, network service, Packet loss, PI control, proportional Integral controller, QoE, QoS, quality of experience, quality of service, Real Time Services, real-time service, real-time systems, Resilient, service level agreements, service metrics, SLA, state space, Streaming media, System Identification, volumetric attacks}, doi = {10.1109/CIC.2018.00-37}, author = {Vempati, Jagannadh and Ram Dantu} }