Taha Eghtesad |

Adversarial Reinforcement Learning for Detecting False Data Injection Attacks in Vehicular Routing

Fri, 01 May 2026 00:00:00 +0000

Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks

Wed, 01 May 2024 00:00:00 +0000

Acceptance Rate: 25%

Bug Hunters’ Perspectives on the Challenges and Benefits of the Bug Bounty Ecosystem

Tue, 01 Aug 2023 00:00:00 +0000

Decentralized Computation Market for Stream Processing Applications

Thu, 01 Sep 2022 00:00:00 +0000

Safe and Private Forward-Trading Platform for Transactive Microgrids

Fri, 01 Jan 2021 00:00:00 +0000

Adversarial Deep Reinforcement Learning based Adaptive Moving Target Defense

Thu, 01 Oct 2020 00:00:00 +0000

Blockchains for Transactive Energy Systems: Opportunities, Challenges, and Approaches

Tue, 01 Sep 2020 00:00:00 +0000

Microtransit Solutions for Underserved Communities

Sat, 01 Aug 2020 00:00:00 +0000

Public transportation infrastructure is an essential component in cultivating equitable communities. However, public transit agencies have historically struggled to achieve this since they are often severely stressed in terms of resources as they have to make the trade-off between concentrating service into routes that serve large numbers of people and spreading service out to ensure that people everywhere have access to at least some service. A solution that holds great promise for improving public transit systems is the integration of fixed-route services with microtransit systems: multi-passenger transportation services that serve passengers using dynamically generated routes and may expect passengers to make their way to and from common pick-up or drop-off points. However, most microtransit systems have failed in the past due to the lack of community engagement, inability to handle the uncertainty of operations when integrating the fixed transit, and inability to handle the system-level optimization challenges.

This project takes a socio-relational approach to community engagement in collaboration with the Chattanooga Area Regional Transportation Authority (CARTA), designs a community-centric micro-transit service that augments fixed-line public transit networks (improving transit accessibility), and demonstrate its effectiveness in the representative city of Chattanooga. The outcome of the project will be a deployment-ready software system that can be used by an agency to design and operate a micro-transit service effectively. The algorithmic toolchain will be complemented by mechanisms to optimally select the parameters and sustainably manage the data required by the algorithms. In addition, this project will provide a set of exemplar case studies and a validated social methodology to engage the community and learn their requirements, which will be fed into the algorithms. This will potentially impact a wide range of cities in the U.S. that do not have well-developed transit systems as the project will not only provide a reusable operations system but also demonstrate how integrated socio-technical research and strong community engagement can provide a pattern for sustainability and expansion.

The intellectual merit of this project lies in the novel community engagement approach and combined operations research and data-driven, learning-based integrated system optimization. Towards this goal, the project will make four key contributions.

First, the project will develop a novel targeted outreach approach that uses the relational networks of social capital (e.g., outreach to community centers, congregations and faith communities, schools, and similar organizational structures) and builds a categorical demand model to design an innovative micro-transit system. The project’s hypothesis is that the behavioral impact on public-transit ridership with the proposed method will be significantly higher than with an approach that focuses only on the economic or time-saving benefits of the improved transit system.
Second, the project will introduce a sustainable and resilient data-integration platform that dynamically adjusts the location of the sensor data used to affect the design parameters and assess performance of the transit system. This is crucial because cloud computing is still very expensive for community partners, especially for real-time high-velocity and high-volume data analysis. Further, this data store provides us an opportunity to take a privacy-by-design approach for the datasets collected during the project. Specifically, the project will develop novel integrated anonymization mixers for multi-modal datasets (e.g., mixing information of different modalities, such as location traces and transactions, together in a spatiotemporal-transactional mixer) that achieve a given level of privacy (quantified using the notation of differential privacy) while maximizing the accuracy of transit queries, relying on not just the privacy-properties of the data but also on the needs of the queries.
Third, the project will develop uncertainty-aware fleet management and dispatch algorithms that incorporate demand aggregation and environmental uncertainty caused by congestion, incidents, and their impact on the system (both for the users and fleet operations).
Fourth, the project will leverage recent advances in active learning for non-stationary environments with contextual side information to design algorithms that will aid in the exploration and optimal selection of hyperparameters for microtransit algorithms.

Mechanisms for Outsourcing Computation via a Decentralized Market

Wed, 01 Jul 2020 00:00:00 +0000

Cyber-Attacks and Mitigation in Blockchain Based Transactive Energy Systems

Mon, 01 Jun 2020 00:00:00 +0000

Blockchain Security and Applications

Mon, 25 May 2020 00:00:00 +0000

This project studies the following research topics:

Security of blockchain-based consensus protocols
Secure smart contracts
Applications of blockchains

Efficient and Scalable Bug-Bounty Programs

Fri, 01 Mar 2019 00:00:00 +0000

Many organizations and companies have recently chosen to use so-called bug-bounty programs (also known as vulnerability reward programs), which allow outside security experts to evaluate the security of an organization’s products and services and to report security vulnerabilities in exchange for rewards. Bug-bounty programs provide unique benefits by allowing organizations to publicly signal their commitment to security and to harness the diverse expertise of thousands of security experts in an affordable way. Despite their rapidly growing popularity, bug-bounty programs are not well understood and can be mismanaged. As a result, bug bounty programs can waste substantial resources and they rarely live up to their potential to improve cybersecurity. This project will significantly improve the efficiency of bug-bounty programs by collecting and publishing comprehensive datasets on the bug-bounty ecosystem, by establishing a sound theory of bug-bounty programs, and by providing practical recommendations for organizations and regulators. The project will directly benefit organizations and companies by enabling them to manage bug-bounty programs more efficiently, which will allow them to eliminate security vulnerabilities at a lower cost; and it will also benefit users by improving the security of software products and services.

This project comprises four research thrusts. The first thrust will build a dataset that captures the entire bug-bounty ecosystem by collecting activity data and rule descriptions from public bug-bounty programs, conducting interviews and focus-group studies with experts who regularly participate in programs, collecting social-media posts, and incorporating existing datasets, such as vulnerability databases. The second thrust will analyze this dataset to understand the ecosystem and its actors’ incentives and actions, and to characterize processes, such as discovering and reporting vulnerabilities and rewarding reports. To code textual data, the project will first develop a terminology and taxonomy of bug-bounty related concepts. Driven by the results of the data analysis, the third thrust will develop a formal model of the bug-bounty ecosystem, which will incorporate technological processes as well as behavioral and economic incentives. To capture all aspects of such a complex ecosystem, the project will employ models and techniques from multiple disciplines and areas, including economics and cybersecurity. Building on this model, the fourth thrust will propose and evaluate novel approaches for improving the efficiency and scalability of bug-bounty programs. The project will consider policies for individual bug-bounty programs, such as rules for rewarding vulnerability reports, as well as mechanisms for regulating bug-bounty programs and for instigating coordination between them.

Blockchains for Transactive Energy Systems

Fri, 01 Sep 2017 00:00:00 +0000

Due to rapid growth in renewable energy resources and improvements in battery technology, power grids are undergoing major changes, which create significant management and control challenges. To tackle these challenges, decentralized solutions are needed, which can support the evolution of electrical power distribution systems. Transactive energy is a decentralized solution for dynamically balancing demand and supply, in which consumers, prosumers (i.e., consumers with energy storage or generation capabilities), providers, etc. can trade energy in an open market.

However, transactive energy solutions must also satisfy security, safety, and privacy requirements, which often seem to contradict each other. For example, to provide safety, detailed energy consumption and production information might need to be disseminated, but this threatens the privacy of prosumers. As another example, the complex and computationally expensive solutions required to provide security might not abide the real-time constraints of power systems.

In this project, we create a transactive energy system based on blockchain technology, using the distributed ledger provided by a blockchain to implement an energy trading platform. We develop protocols, smart contracts, middleware, and control algorithms to provide security, safety, and privacy for transactive energy.