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Pages
About Me
| Software Engineer at Google | Ph.D. in Informatics from Penn State |
Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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Blog Post number 1
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portfolio
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projects
Artificial Intelligence for Computer Security
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Designing safe and attack resilient software with the help of artificial intelligence and machine learning. This includes modeling the real world systems as an adversarial game, and using multi-agent reinforcement learning for solving the proposed game.
publications
Cyber-Attacks and Mitigation in Blockchain Based Transactive Energy Systems
Published in IEEE Conference on Industrial Cyberphysical Systems, 2020
We introduce a novel class of attacks against blockchain-based Transactive Energy Systems targeting the gateways connecting market participants, study multiple threat models, and demonstrate mitigation strategies on a testbed with GridLAB-D and a private Ethereum network.
Recommended citation: Barreto, C., Eghtesad, T., Eisele, S., Laszka, A., Dubey, A. and Koutsoukos, X., 2020. Cyber-Attacks and Mitigation in Blockchain Based Transactive Energy Systems. In 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS), pp. 129–136.
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Mechanisms for Outsourcing Computation via a Decentralized Market
Published in 14th ACM International Conference on Distributed and Event-Based Systems, 2020
We introduce MODiCuM, a decentralized system for outsourcing computation that deters misbehavior by resolving disputes via dedicated mediators and enforceable fines, while minimizing computational overhead.
Recommended citation: Eisele, S., Eghtesad, T., Troutman, N., Laszka, A. and Dubey, A., 2020. Mechanisms for Outsourcing Computation via a Decentralized Market. In Proceedings of the 14th ACM International Conference on Distributed and Event-Based Systems (DEBS 2020), pp. 61–72.
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Blockchains for Transactive Energy Systems: Opportunities, Challenges, and Approaches
Published in IEEE Computer, Vol. 53, No. 9, pp. 66-76, 2020
The emergence of blockchains and smart contracts has renewed interest in electrical cyberphysical systems, especially transactive energy systems. To address the associated challenges, we present TRANSAX, a blockchain-based transactive energy system providing an efficient, safe, and privacy-preserving market.
Recommended citation: Eisele, S., Barreto, C., Dubey, A., Koutsoukos, X., Eghtesad, T., Laszka, A. and Mavridou, A., 2020. Blockchains for Transactive Energy Systems: Opportunities, Challenges, and Approaches. Computer, 53(9), pp. 66–76.
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The Hackers’ Viewpoint: Exploring Challenges and Benefits of Bug-Bounty Programs
Published in 6th Workshop on Security Information Workers, 2020
Bug-bounty programs enable organizations to improve their security posture by harnessing the outside perspective of security experts. We present survey results from 56 bug hunters exploring their motivations, decision-making, and challenges with bug-bounty platforms.
Recommended citation: Akgul, O., Eghtesad, T., Elazari, A., Gnawali, O., Grossklags, J., Votipka, D. and Laszka, A., 2020. The Hackers’ Viewpoint: Exploring Challenges and Benefits of Bug-Bounty Programs. In Proceedings of the 2020 Workshop on Security Information Workers (WSIW 2020).
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Adversarial Deep Reinforcement Learning based Adaptive Moving Target Defense
Published in 11th Conference on Decision and Game Theory for Security, 2020
Moving target defense (MTD) aims to thwart attacks by continuously changing the attack surface of a system. We propose a multi-agent partially-observable Markov Decision Process model of MTD, formulate a two-player general-sum game between adversary and defender, and solve it using multi-agent RL based on the double oracle algorithm.
Recommended citation: Eghtesad, T., Vorobeychik, Y. and Laszka, A., 2020. Adversarial Deep Reinforcement Learning Based Adaptive Moving Target Defense. In Decision and Game Theory for Security (GameSec 2020), LNCS vol. 12513, pp. 58–79. Springer, Cham.
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Safe and Private Forward-Trading Platform for Transactive Microgrids
Published in ACM Transactions on Cyber-Physical Systems (TCPS), Vol. 5, No. 1, Article 8, 2020
We present TRANSAX, a platform that enables participants to trade in an energy futures market for transactive microgrids. TRANSAX balances safety, efficiency, and privacy by anonymizing trading activity via a distributed mixing service while enforcing physical line capacity constraints.
Recommended citation: Eisele, S., Eghtesad, T., Campanelli, K., Agrawal, P., Laszka, A. and Dubey, A., 2021. Safe and Private Forward-Trading Platform for Transactive Microgrids. ACM Transactions on Cyber-Physical Systems, 5(1), Article 8, 29 pages.
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Decentralized Computation Market for Stream Processing Applications
Published in 10th IEEE International Conference on Cloud Engineering, 2022
While cloud computing is the standard for outsourcing computation, it can be prohibitively expensive for streaming applications. We introduce an architecture combining a distributed trusted computing base (blockchain) with Apache Pulsar to create a decentralized computation market for hosting stream processing applications.
Recommended citation: Eisele, S., Wilbur, M., Eghtesad, T., Silvergold, K., Eisele, F., Mukhopadhyay, A., Laszka, A. and Dubey, A., 2022. Decentralized Computation Market for Stream Processing Applications. In 2022 IEEE International Conference on Cloud Engineering (IC2E), pp. 36–46.
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Bug Hunters’ Perspectives on the Challenges and Benefits of the Bug Bounty Ecosystem
Published in 32nd USENIX Security Symposium, 2023
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 bug hunters. We address this research gap with three studies: identifying key factors through a free listing survey, a larger-scale factor-rating survey (n=159), and semi-structured interviews (n=24).
Recommended citation: Akgul, O., Eghtesad, T., Elazari, A., Gnawali, O., Grossklags, J., Mazurek, M.L., Votipka, D. and Laszka, A., 2023. Bug Hunters' Perspectives on the Challenges and Benefits of the Bug Bounty Ecosystem. In 32nd USENIX Security Symposium (USENIX Security 23), pp. 2275–2291.
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Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks
Published in 23rd International Conference on Autonomous Agents and Multiagent Systems, 2024
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 data collection or processing to inject false information and interfere with drivers' route selection. We introduce a computational framework using hierarchical multi-agent reinforcement learning to find worst-case data-injection attacks against transportation networks.
Recommended citation: Eghtesad, T., Li, S., Vorobeychik, Y. and Laszka, A., 2024. Hierarchical Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks. In Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024), pp. 508–515.
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Adversarial Reinforcement Learning Applications in Cyber-Physical Systems Security
Published in Pennsylvania State University, 2026
This doctoral dissertation establishes a comprehensive framework that integrates proactive prevention, effective detection, and adaptive mitigation strategies across cyber-physical systems using adversarial and multi-agent reinforcement learning.
Recommended citation: Eghtesad, T., 2026. Adversarial Reinforcement Learning Applications in Cyber-Physical Systems Security. Ph.D. Dissertation, Pennsylvania State University.
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Adversarial Reinforcement Learning for Detecting False Data Injection Attacks in Vehicular Routing
Published in 17th ACM/IEEE International Conference on Cyber-Physical Systems, 2026
In modern transportation networks, adversaries can manipulate routing algorithms using false data injection attacks to mislead vehicles toward suboptimal routes and increase congestion. We formulate a strategic zero-sum game between an attacker and a defender and propose a multi-agent reinforcement learning approach using policy space response oracles to compute a Nash equilibrium detection strategy.
Recommended citation: Eghtesad, T., Vorobeychik, Y. and Laszka, A., 2026. Adversarial Reinforcement Learning for Detecting False Data Injection Attacks in Vehicular Routing. In Proceedings of the 17th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS 2026).
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talks
Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks Permalink
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Adversarial Reinforcement Learning Applications in Cyber-Physical Systems Security Permalink
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Prevention of Threats using Moving Target Defense
Traditional security measures are augmented by a proactive strategy known as Moving Target Defense (MTD). MTD introduces continuous and random alterations to system configurations, making reconnaissance computationally expensive for adversaries or trapping them in exploration loops. Manual deployment of MTD configurations poses challenges, necessitating automated approaches that balance security benefits and system efficiency. The goal is to render cyber-attacks economically and logistically infeasible for adversaries.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
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