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<oembed><version>1.0</version><provider_name>Microsoft Research</provider_name><provider_url>https://www.microsoft.com/en-us/research</provider_url><author_name>William Blum</author_name><author_url>https://www.microsoft.com/en-us/research/people/wiblum/</author_url><title>CyberBattleSim - Microsoft Research</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="yKJ6fcWpOx"&gt;&lt;a href="https://www.microsoft.com/en-us/research/project/cyberbattlesim/"&gt;CyberBattleSim&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/project/cyberbattlesim/embed/#?secret=yKJ6fcWpOx" width="600" height="338" title="&#x201C;CyberBattleSim&#x201D; &#x2014; Microsoft Research" data-secret="yKJ6fcWpOx" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script type="text/javascript"&gt;
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</html><description>CyberBattleSim is an experimentation research platform to investigate the interaction of automated agents operating in a simulated abstract enterprise network environment. The simulation provides a high-level abstraction of computer networks and cyber security concepts. Its Python-based Open AI Gym interface allows for training of automated agents using reinforcement learning algorithms.  The simulation environment is parameterized by a fixed network topology and a set of vulnerabilities that agents can utilize to move laterally in the network. The goal of the attacker is to take ownership of a portion of the network by exploiting vulnerabilities that are planted in the computer nodes. While the attacker attempts to spread throughout the network, a defender agent watches the network activity and tries to detect any attack taking place and mitigate the impact on the system by evicting the attacker. We provide a basic stochastic defender that detects and mitigates ongoing attacks based on pre-defined probabilities of success. We implement mitigation by re-imaging the infected nodes, a process abstractly modeled as an operation spanning over multiple simulation steps.  To compare the performance of the agents we look at two metrics: the number of simulation steps taken to attain their goal and the cumulative rewards over simulation steps across training epochs.</description></oembed>
