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  <NewsItem contentIssues="false" id="153133" important="false" status="posted" url="https://dev.my.umbc.edu/groups/umbc-ai/posts/153133">
  <Title>talk:  Self-Defending Ledgers: Automating Distributed Ledger Security Using Multi-Agent Reinforcement Learning and Game Theory</Title>
  <Tagline>12-1 EDT Friday, October3, 2025, online</Tagline>
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    <div class="html-content"><p>The UMBC Cyber Defense Lab presents</p><p><strong>Self-Defending Ledgers: Automating Distributed Ledger Security Using Multi-Agent Reinforcement Learning and Game Theory</strong></p><p><a href="https://paveltariq.com/" rel="nofollow external" class="bo"><strong>Md Tariqul Islam 'Pavel'</strong></a><br>Assistant Professor of Cybersecurity<br>UMBC Department of Information Systems</p><p>12–1pm EDT, Friday, October 3, 2025 via <a href="https://umbc.webex.com/meet/sherman" rel="nofollow external" class="bo"><strong>Webex</strong></a></p><p><a href="https://en.wikipedia.org/wiki/Distributed_ledger" rel="nofollow external" class="bo">Distributed ledger technologies</a> (DLTs) continue to face significant security challenges. While attackers constantly adapt their strategies, governance mechanisms often remain static. Our work addresses this critical gap by introducing a framework for self-defending ledgers, where nodes enforce ledger security through adaptive governance driven by <a href="https://en.wikipedia.org/wiki/Multi-agent_reinforcement_learning" rel="nofollow external" class="bo">multi-agent reinforcement learning </a>(MARL) grounded in game-theoretic principles. We model DLT consensus as a repeated Bayesian game, in which participants hold probabilistic beliefs about peer behavior, allowing agents to make strategic decisions under partial observability of adversarial actions. Our framework enables nodes to model, detect, and respond to a wide range of malicious behaviors, including bribery, selfish mining, equivocation, Sybil attacks, and collusive voting, by continuously updating Bayesian trust beliefs and governance policies based on network observations. We formally prove that networks with an honest majority reach stable equilibria and provide bounds on adversarial influence. Experiments across five major protocols show that agents effectively identify attacks with high accuracy while substantially reducing adversarial success. This work demonstrates the potential of game-theoretic MARL to provide robust, self-adaptive security in varied DLT environments, paving the way for resilient and autonomous ledger governance.</p><p><a href="https://paveltariq.com/" rel="nofollow external" class="bo"><strong>Md Tariqul Islam 'Pavel'</strong></a> is an assistant professor of cybersecurity in UMBC's Department of Information Systems. His research centers on the security, efficiency, and fault-tolerance of distributed computing systems, with a strong emphasis on blockchain, cloud, and vehicular networks. He develops formal models, algorithms, and protocols that address critical vulnerabilities in decentralized ecosystems, spanning inter-blockchain communication, smart contract migration, and trustworthy governance. His work combines cryptography, game theory, and system design to build scalable, resilient infrastructures. He earned his PhD and MS from the University of Kentucky and BS from the University of Dhaka, Bangladesh.</p><div><a href="https://paveltariq.com/" rel="nofollow external" class="bo"><br></a>Support for this event was provided in part by NSF SFS grant DGE-1753681.</div></div>
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  <Summary>The UMBC Cyber Defense Lab presents  Self-Defending Ledgers: Automating Distributed Ledger Security Using Multi-Agent Reinforcement Learning and Game Theory  Md Tariqul Islam 'Pavel' Assistant...</Summary>
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  <Tag>ai</Tag>
  <Tag>distributed</Tag>
  <Tag>game-theory</Tag>
  <Tag>ledger</Tag>
  <Tag>multi-agent</Tag>
  <Tag>reinforcement-learning</Tag>
  <Group token="umbc-ai">UMBC AI</Group>
  <GroupUrl>https://dev.my.umbc.edu/groups/umbc-ai</GroupUrl>
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  <Sponsor>UMBC Cyber Defense Laboratory</Sponsor>
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  <ThumbnailAltText>Distributed ledger system</ThumbnailAltText>
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  <PostedAt>Wed, 01 Oct 2025 18:49:30 -0400</PostedAt>
  <EditAt>Wed, 01 Oct 2025 19:03:07 -0400</EditAt>
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