Markov Game Modeling of Moving Target Defense for Strategic Detection of Threats in Cloud Networks

2018-12-23 · Ankur Chowdhary, Sailik Sengupta, Dijiang Huang, Subbarao Kambhampati

The processing and storage of critical data in large-scale cloud networks necessitate the need for scalable security solutions. It has been shown that deploying all possible security measures incurs a cost on performance by using up valuable computing and networking resources which are the primary selling points for cloud service providers. Thus, there has been a recent interest in developing Moving Target Defense (MTD) mechanisms that helps one optimize the joint objective of maximizing security while ensuring that the impact on performance is minimized. Often, these techniques model the problem of multi-stage attacks by stealthy adversaries as a single-step attack detection game using graph connectivity measures as a heuristic to measure performance, thereby (1) losing out on valuable information that is inherently present in graph-theoretic models designed for large cloud networks, and (2) coming up with certain strategies that have asymmetric impacts on performance. In this work, we leverage knowledge in attack graphs of a cloud network in formulating a zero-sum Markov Game and use the Common Vulnerability Scoring System (CVSS) to come up with meaningful utility values for this game. Then, we show that the optimal strategy of placing detecting mechanisms against an adversary is equivalent to computing the mixed Min-max Equilibrium of the Markov Game. We compare the gains obtained by using our method to other techniques presently used in cloud network security, thereby showing its effectiveness. Finally, we highlight how the method was used for a small real-world cloud system.

📄 PDF Abstract BibTeX arXiv:1812.09660

Code (2)

sailik1991/MarkovGameSolvers 공식 구현

facundo-p/Markov-Game-Model

Multi-agent Reinforcement Learning in Bayesian Stackelberg Markov Games for Adaptive Moving Target Defense

2020-07-20 · Sailik Sengupta, Subbarao Kambhampati

The field of cybersecurity has mostly been a cat-and-mouse game with the discovery of new attacks leading the way. To take away an attacker's advantage of reconnaissance, researchers have proposed proactive defense metho…

Multi-agent Reinforcement Learning Q-Learning Reinforcement Learning (RL)

Learning Movement Strategies for Moving Target Defense

2021-01-01 · Sailik Sengupta, Subbarao Kambhampati

Q-Learning

Adversarial Deep Reinforcement Learning based Adaptive Moving Target Defense

2019-11-27 · Taha Eghtesad, Yevgeniy Vorobeychik, Aron Laszka

Moving target defense (MTD) is a proactive defense approach that aims to thwart attacks by continuously changing the attack surface of a system (e.g., changing host or network configurations), thereby increasing the adve…

Deep Reinforcement Learning Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning+1

Moving Target Defense for Web Applications using Bayesian Stackelberg Games

2016-02-23 · Sailik Sengupta, Satya Gautam Vadlamudi, Subbarao Kambhampati, Marthony Taguinod 외

The present complexity in designing web applications makes software security a difficult goal to achieve. An attacker can explore a deployed service on the web and attack at his/her own leisure. Moving Target Defense (MT…

A Factored MDP Approach To Moving Target Defense With Dynamic Threat Modeling and Cost Efficiency

2024-08-16 · Megha Bose, Praveen Paruchuri, Akshat Kumar

Moving Target Defense (MTD) has emerged as a proactive and dynamic framework to counteract evolving cyber threats. Traditional MTD approaches often rely on assumptions about the attackers knowledge and behavior. However,…