Adaptive Cybersecurity for Distributed Energy Resources (AdCyDER): Online Reinforcement Learning with Stackelberg-Optimized Defenses — Pipeline Architecture, Evaluation Methodology, and Findings from a Synthetic-Data Evaluation

Blakely, Benjamin, Ramchandra, Akshay Ram, Swindall, Matthew et al. · 2026

Read Adaptive Cybersecurity for Distributed Energy Resources (AdCyDER): Online Reinforcement Learning with Stackelberg-Optimized Defenses — Pipeline Architecture, Evaluation Methodology, and Findings from a Synthetic-Data Evaluation by Blakely, Benjamin, Ramchandra, Akshay Ram, Swindall, Matthew et al. (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/doe-osti-3377852-adaptive-cybersecurity-for-distributed-e.txt.

Description

Argonne National Laboratory report ANL-26/18, from the Strategic Security Sciences Division, presents AdCyDER, a defense system for distributed energy resources. The evidence record names Benjamin Blakely, Akshay Ram Ramchandra, and Matthew Swindall, among others. AdCyDER uses online reinforcement learning, tuned with Stackelberg game methods. Defenses therefore adapt as attacks unfold.

The long report details the pipeline architecture and the evaluation method. It gives findings from an evaluation run on synthetic data rather than live grid data. Engineering choices are documented closely. The infrastructure is defined once in CDKTF and rendered for whichever backend a stack targets. One configuration value switches deployment between Amazon Web Services and a local LocalStack copy. Python was chosen so infrastructure code shares validation logic with the service code.

Catalog record

CREATORBlakely, Benjamin, Ramchandra, Akshay Ram, Swindall, Matthew et al.
YEAR2026
COLLECTIONPAPERS
FORMATTXT
FILE SIZE181 KB
LICENSEPublic Domain
RIGHTSPUBLIC DOMAIN
SOURCEDOE OSTI

Source: https://www.osti.gov/biblio/3377852

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