Multi-layer defense algorithm against deep reinforcement learning-based intruders in smart grids.

• False data injection attacks pretend to mislead power scheduling and routing strategies in internet of energy-based smart grids besides monetary purposes. • The conventional cyber defense systems cannot detect well-developed false data injection attacks since intruders alter and enhance attack methods considering the dynamic nature of the smart grids. • A Deep Q-Learning-based attack generation algorithm is able to create a wide range of possible false data injection attacks, including dynamic attacks. • A two-layer defense framework is needed to detect passive and active attacks simultaneously. The Internet of Energy envisions the next generation of smart grids as a highly interconnected network, including advanced metering infrastructures, distributed energy resources, and bidirectional communication systems. The open architecture of IoE-based smart grid results in manifold security concerns, especially the risk of False Data Injection Attacks. The attack may target the technical aspects of a system since fabricating the network's data misleads power scheduling and routing strategies and interrupts the healthy operation of the power system. Additionally, monetary motivation for the intruder sometimes is the main motivation. The conventional cyber defense strategies are unable to detect well-developed False Data Injection Attacks, particularly once the intruder takes advantage of a Deep Reinforcement Learning-based attack development framework that analyzes the dynamic nature of the smart grids. This paper primarily outlines various possible passive attacks using statistical methods. Then, a reinforcement learning-based intruder as an active attack generator is developed, initialized by modeled passive attacks. The attack generator algorithm can simulate the network environment and subsequently creates unclassified attacks. After creating a dynamic attacker, a multilayer defense framework is developed using Snapshot Ensemble Deep Neural Network and an adoptable Deep Auto Encoder network to detect known and unknown threats. Performance evaluations and a real-world simulation prove that the proposed framework can successfully detect both passive and active, where the accuracy and false positive detection rate of the developed framework are 98.82% and 97.42%, respectively. [ABSTRACT FROM AUTHOR]