An adversarial reinforcement learning based system for cyber security

2019 ◽  
Author(s):  
Song Xia ◽  
Meikang Qiu ◽  
Hao Jiang
Keyword(s):  
Reinforcement Learning ◽  
Cyber Security

scholarly journals A Game-Theoretical Approach to Cyber-Security of Critical Infrastructures Based on Multi-Agent Reinforcement Learning

2018 ◽  
Author(s):  
Martina Panfili ◽  
Alessandro Giuseppi ◽  
Andrea Fiaschetti ◽  
Homoud B. Al-Jibreen ◽  
Antonio Pietrabissa ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Theoretical Approach ◽  
Cyber Security ◽  
Critical Infrastructures ◽  
Game Theoretical Approach ◽  
Multi Agent

Reinforcement Learning's Contribution to the Cyber Security of Distributed Systems

2020 ◽  
Vol 12 (2) ◽  
pp. 35-55
Author(s):  
Christophe Feltus
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Information System ◽  
Reinforcement Learning ◽  
Distributed Systems ◽  
Unsupervised Learning ◽  
Cyber Security ◽  
Analytic Framework ◽  
Learning Paradigm

Reinforcement learning (RL) is a machine learning paradigm, like supervised or unsupervised learning, which learns the best actions an agent needs to perform to maximize its rewards in a particular environment. Research into RL has been proven to have made a real contribution to the protection of cyberphysical distributed systems. In this paper, the authors propose an analytic framework constituted of five security fields and eight industrial areas. This framework allows structuring a systematic review of the research in artificial intelligence that contributes to cybersecurity. In this contribution, the framework is used to analyse the trends and future fields of interest for the RL-based research in information system security.

scholarly journals Reinforcement Learning vs Genetic Algorithms in Game-Theoretic Cyber-Security

2018 ◽  
Author(s):  
Stefan Niculae
Keyword(s):  
Reinforcement Learning ◽  
Cyber Security ◽  
Large Scale ◽  
Human Performance ◽  
Learning Approaches ◽  
Classifier Systems ◽  
Penetration Testing ◽  
Q Learning ◽  
Game Theoretic ◽  
Security Game

Penetration testing is the practice of performing a simulated attack on a computer system in order to reveal its vulnerabilities. The most common approach is to gain information and then plan and execute the attack manually, by a security expert. This manual method cannot meet the speed and frequency required for efficient, large-scale secu- rity solutions development. To address this, we formalize penetration testing as a security game between an attacker who tries to compro- mise a network and a defending adversary actively protecting it. We compare multiple algorithms for finding the attacker’s strategy, from fixed-strategy to Reinforcement Learning, namely Q-Learning (QL), Extended Classifier Systems (XCS) and Deep Q-Networks (DQN). The attacker’s strength is measured in terms of speed and stealthi- ness, in the specific environment used in our simulations. The results show that QL surpasses human performance, XCS yields worse than human performance but is more stable, and the slow convergence of DQN keeps it from achieving exceptional performance, in addition, we find that all of these Machine Learning approaches outperform fixed-strategy attackers.

scholarly journals Deep Learning Cluster Structures for Management Decisions: The Digital CEO

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3327
Author(s):  
Will Serrano
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Cyber Security ◽  
Learning Algorithm ◽  
Cluster Structure ◽  
Long Term Memory ◽  
Management Decisions ◽  
Proposed Model ◽  
The Brain ◽  
The Way

This paper presents a Deep Learning (DL) Cluster Structure for Management Decisions that emulates the way the brain learns and makes choices by combining different learning algorithms. The proposed model is based on the Random Neural Network (RNN) Reinforcement Learning for fast local decisions and Deep Learning for long-term memory. The Deep Learning Cluster Structure has been applied in the Cognitive Packet Network (CPN) for routing decisions based on Quality of Service (QoS) metrics (Delay, Loss and Bandwidth) and Cyber Security keys (User, Packet and Node) which includes a layer of DL management clusters (QoS, Cyber and CEO) that take the final routing decision based on the inputs from the DL QoS clusters and RNN Reinforcement Learning algorithm. The model has been validated under different network sizes and scenarios. The simulation results are promising; the presented DL Cluster management structure as a mechanism to transmit, learn and make packet routing decisions is a step closer to emulate the way the brain transmits information, learns the environment and takes decisions.

scholarly journals Adversarial Reinforcement Learning in a Cyber Security Simulation

2017 ◽  
Author(s):  
Richard Elderman ◽  
Leon J. J. Pater ◽  
Albert S. Thie ◽  
Madalina M. Drugan ◽  
Marco M. Wiering
Keyword(s):  
Reinforcement Learning ◽  
Cyber Security

scholarly journals Deep Reinforcement Learning for Cyber Security

2021 ◽  
pp. 1-17
Author(s):  
Thanh Thi Nguyen ◽  
Vijay Janapa Reddi
Keyword(s):  
Reinforcement Learning ◽  
Cyber Security

Bayes factors for reinforcement-learning models of the Iowa gambling task.

Decision ◽  
2016 ◽  
Vol 3 (2) ◽  
pp. 115-131 ◽  
Author(s):  
Helen Steingroever ◽  
Ruud Wetzels ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Reinforcement Learning ◽  
Iowa Gambling Task ◽  
Bayes Factors ◽  
Gambling Task ◽  
Learning Models ◽  
Reinforcement Learning Models

Analogical Reinforcement Learning With Two-Stage Memory Retrieval

2014 ◽  
Author(s):  
James Foster ◽  
Matt Jones
Keyword(s):  
Reinforcement Learning ◽  
Memory Retrieval ◽  
Two Stage
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