Adversarial Resilience Learning - Towards Systemic Vulnerability Analysis for Large and Complex Systems
arXiv:1811.06447
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.
10 pages
References in corpus (6)
- Adam: A Method for Stochastic Optimization
- Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
- An overview of gradient descent optimization algorithms
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- Robust Physical-World Attacks on Deep Learning Models
- On the Definition of Cyber-Physical Resilience in Power Systems
Cited by in corpus (3)
- Large-Scale Co-Simulation of Power Grid and Communication Network Models with Software in the Loop
- The Adversarial Resilience Learning Architecture for AI-based Modelling, Exploration, and Operation of Complex Cyber-Physical Systems
- ANALYSE -- Learning to Attack Cyber-Physical Energy Systems With Intelligent Agents