(opens in new tab)<\/span><\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"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.<\/p>\n
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.<\/p>\n
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.<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"research-area":[13558],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-739195","msr-project","type-msr-project","status-publish","hentry","msr-research-area-security-privacy-cryptography","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"2020-02-01","related-publications":[],"related-downloads":[741817],"related-videos":[],"related-groups":[],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[],"slides":[],"related-researchers":[{"type":"user_nicename","display_name":"William Blum","user_id":34834,"people_section":"People","alias":"wiblum"},{"type":"user_nicename","display_name":"James Bono","user_id":39387,"people_section":"People","alias":"jabono"},{"type":"user_nicename","display_name":"Jugal Parikh","user_id":40243,"people_section":"People","alias":"juparikh"}],"msr_research_lab":[],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/739195"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-project"}],"version-history":[{"count":9,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/739195\/revisions"}],"predecessor-version":[{"id":741814,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/739195\/revisions\/741814"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=739195"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=739195"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=739195"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=739195"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=739195"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}