UMBC is a member of the International Cybersecurity Center of Excellence, which will hold an online seminar on the Security Challenges for Medical Devices from 8-9:30 am on Wednesday, March 26, 2025 via Zoom.

The seminar will consist of three short presentations:

• A Security Review of Bluetooth Low Energy, Jorge Blasco Alís, Universidad Politécnica de Madrid

• Medical devices in 2050: An ideal use-case for medicine and cyber, Emil Lupu, Imperial College London 

• Smart Security: Protecting Embedded Medical Devices for a Safer Tomorrow, Phil Englert, Health-ISAC

Assessing DOGE's Impact on Federal Cybersecurity and Data

Dr. Richard Forno

Assistant Director, UMBC Cybersecurity Institute

12–1pm Friday, March 14, 2025, online

In just its first month of operation, that has disrupted large portions of the federal government, the activities of the so-called Department of Government Efficiency (DOGE) have worried cybersecurity, national security, and information technology experts both inside and outside the federal government. Armed with root-level access and presidential authorization---yet often lacking relevant operational expertise or any meaningful oversight---DOGE’s staff, activities, and the security culture of the current administration in which they operate often seem to violate many time-tested best practices and principles for effective cybersecurity and technology management. They may also violate longstanding federal guidelines and regulations. As such, it is likely that DOGE is creating conditions that can cause operational, cybersecurity, or privacy incidents across the federal IT landscape and subsequently placing government information systems and sensitive citizen data at risk. On the CIA (confidentiality-integrity-availability) triad, this talk will examine DOGE's activities in the context of cybersecurity and technology best practices. Is DOGE truly an “insider threat” to the federal government as some claim? And if so, what countermeasures, if any, are available to address the threat?

Dr. Richard Forno is a teaching professor in the UMBC Department of Computer Science and Electrical Engineering, where he directs the UMBC Graduate Cybersecurity Program, serves as the Assistant Director of UMBC's Cybersecurity Institute, and is an Affiliate Scholar at the Stanford Law School's Center for Internet and Society (CIS). Prior to academia, his twenty-year career includes helping build a formal cybersecurity program for the US House of Representatives, serving as the first Chief Security Officer for Network Solutions (then, the global center of Internet DNS), and other assorted roles with the government, military/defense entities, and private sector. In 2023, he was named a Distinguished Visiting Professor at the Universidad Autónoma del Estado de Hidalgo in Pachuca, Mexico. Along with regular public analysis and commentary, his most recent book is Cybersecurity for Local Governments (Wiley, 2022), coauthored with UMBC Public Policy colleagues Professor Emeritus Don Norris and Ph.D. candidate Laura Mateczun.

Securing Distributed Networks: Leveraging Reinforcement Learning and Game Theory for Attack Detection and Mitigation

Dr. Md Tariqul Islam, Syracuse University

10-11am January 30, 2025;  ITE 459, UMBC and online

Reinforcement learning (RL) has demonstrated remarkable success across diverse domains, from mastering complex games to optimizing real-time feedback systems in robotics and industrial control. However, its potential in cybersecurity, particularly for autonomous attack detection and mitigation in distributed systems, remains largely underexplored. Traditional single-agent RL approaches struggle in decentralized environments where multiple entities make independent decisions, necessitating multi-agent reinforcement learning (MARL). Our research explores blockchain networks as an ideal test case due to their decentralized architecture and trustless consensus mechanisms. We developed a novel MARL-based consensus mechanism for Proof-of-Stake blockchains, enabling nodes to collaboratively identify and penalize malicious behavior while preserving decentralization. This approach effectively mitigated six major blockchain attack types with minimal computational overhead. Building on these results, we propose integrating game-theoretic principles into the MARL framework to model adversarial strategies and enhance system resilience. The synergy between reinforcement learning and game theory establishes a robust foundation for dynamic and adaptive security in distributed systems, effectively addressing current vulnerabilities while anticipating and countering future threats. This integrated approach enables the design of resilient, scalable defense mechanisms tailored to the complex dynamics of decentralized architectures.