Adversarial Resilience Learning - Towards Systemic Vulnerability Analysis for Large and Complex Systems

2018/11/15 by Lars Fischer, Jan-Menno Memmen, Fischer, Lars +5 · 13 citations
Computer Science · Engineering · Psychology · #Adversarial Robustness in Machine Learning #Adversarial system #Anomaly Detection Techniques and Applications #Artificial Intelligence (cs.AI) #Artificial intelligence #Computer science #Computer security #Economics #FOS: Computer and information sciences #FOS: Electrical engineering #Psychological resilience #Psychology #Resilience (materials science) #Smart Grid Security and Resilience #Social psychology #Sociology #Systemic risk #Systems and Control (eess.SY) #Vulnerability (computing) #Vulnerability assessment #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1811.06447

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2018/11/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

This paper introduces Adversarial Resilience Learning (ARL), a concept to model, train, and analyze artificial neural networks as representations of competitive agents in highly complex systems. In our examples, the agents normally take the roles of attackers or defenders that aim at worsening or improving-or keeping, respectively-defined performance indicators of the system. Our concept provides adaptive, repeatable, actor-based testing with a chance of detecting previously unknown attack vectors. We provide the constitutive nomenclature of ARL and, based on it, the description of experimental setups and results of a preliminary implementation of ARL in simulated power systems.

Citations

• Adam: A Method for Stochastic Optimization
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• Long Short-Term Memory
• On the Computational Power of Neural Nets
• Finding Structure in Time
• An overview of gradient descent optimization algorithms
• Robust Physical-World Attacks on Deep Learning Models
• On the Definition of Cyber-Physical Resilience in Power Systems
• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Cited by

• Vulnerability-Based Optimal Grid Defense Strategies for Enhancing Cyber-Physical Energy System Resilience

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