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Found 10 papers, 2 papers with code
Decision Theory-Guided Deep Reinforcement Learning for Fast Learning
Experimental results demonstrate that the integration of decision theory not only facilitates effective initial guidance for DRL agents but also promotes a more structured and informed exploration strategy, particularly in environments characterized by large and intricate state spaces.
IoTFlowGenerator: Crafting Synthetic IoT Device Traffic Flows for Cyber Deception
Over the years, honeypots emerged as an important security tool to understand attacker intent and deceive attackers to spend time and resources.
AIIPot: Adaptive Intelligent-Interaction Honeypot for IoT Devices
In this paper, we propose a honeypot for IoT devices that uses machine learning techniques to learn and interact with attackers automatically.
MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement Learning
The first algorithm, IVIPER, extends VIPER, a recent method for single-agent interpretable RL, to the multi-agent setting.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Learning Generative Deception Strategies in Combinatorial Masking Games
Next, we present a novel highly scalable approach for approximately solving such games by representing the strategies of both players as neural networks.
Understanding Adversarial Examples Through Deep Neural Network's Response Surface and Uncertainty Regions
Our study reveals the structural problem of DNN classification boundary that leads to the adversarial examples.
Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions
A Pareto GAN leverages extreme value theory and the functional properties of neural networks to learn a distribution that matches the asymptotic behavior of the marginal distributions of the features.
Game-Theoretic and Machine Learning-based Approaches for Defensive Deception: A Survey
Defensive deception is a promising approach for cyber defense.
Learning and Planning in the Feature Deception Problem
In order to formally reason about deception, we introduce the feature deception problem (FDP), a domain-independent model and present a learning and planning framework for finding the optimal deception strategy, taking into account the adversary's preferences which are initially unknown to the defender.
Exploring the Attack Surface of Blockchain: A Systematic Overview
In this paper, we systematically explore the attack surface of the Blockchain technology, with an emphasis on public Blockchains.
Cryptography and Security
