Taha Eghtesad
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View article: Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks
Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks Open
The increasing reliance of drivers on navigation applications has made transportation networks more susceptible to data-manipulation attacks by malicious actors. Adversaries may exploit vulnerabilities in the data collection or processing …
View article: Bug Hunters' Perspectives on the Challenges and Benefits of the Bug Bounty Ecosystem
Bug Hunters' Perspectives on the Challenges and Benefits of the Bug Bounty Ecosystem Open
Although researchers have characterized the bug-bounty ecosystem from the point of view of platforms and programs, minimal effort has been made to understand the perspectives of the main workers: bug hunters. To improve bug bounties, it is…
View article: Safe and Private Forward-trading Platform for Transactive Microgrids
Safe and Private Forward-trading Platform for Transactive Microgrids Open
Power grids are evolving at an unprecedented pace due to the rapid growth of distributed energy resources (DER) in communities. These resources are very different from traditional power sources, as they are located closer to loads and thus…
View article: Blockchains for Transactive Energy Systems: Opportunities, Challenges, and Approaches
Blockchains for Transactive Energy Systems: Opportunities, Challenges, and Approaches Open
The emergence of blockchains and smart contracts has renewed interest in electrical cyberphysical systems, especially transactive energy systems. Here, to address the associated challenges, we present TRANSAX, a blockchain-based transactiv…
View article: Mechanisms for outsourcing computation via a decentralized market
Mechanisms for outsourcing computation via a decentralized market Open
As the number of personal computing and IoT devices grows rapidly, so does the amount of computational power that is available at the edge. Since many of these devices are often idle, there is a vast amount of computational power that is c…
View article: Deep Reinforcement Learning based Adaptive Moving Target Defense.
Deep Reinforcement Learning based Adaptive Moving Target Defense. Open
Moving target defense (MTD) is a proactive defense approach that aims to thwart attacks by continuously changing the attack surface of a system (e.g., changing host or network configurations), thereby increasing the adversary's uncertainty…
View article: Adversarial Deep Reinforcement Learning based Adaptive Moving Target\n Defense
Adversarial Deep Reinforcement Learning based Adaptive Moving Target\n Defense Open
Moving target defense (MTD) is a proactive defense approach that aims to\nthwart attacks by continuously changing the attack surface of a system (e.g.,\nchanging host or network configurations), thereby increasing the adversary's\nuncertai…