Conference on Decision and Game Theory for Security (GameSec)

October 28th, 2020
Cyber Security Summit DC

 October 28th, 2020 - October 30th, 2020 

Conference on Decision and Game Theory for Security (GameSec)

Event submitted on Thursday, August 13th 2020, approved by Charles Villanueva

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37 Days Until The Event
October 28th, 2020 - October 30th, 2020
United States  »  South, USA  »  Maryland » 
Event Website

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 Our Review

An academic event aimed at exploring the latest progress and challenges facing information, automation, and communication technologies. Highly recommended if you’re in this space. I understand that this event is a physical conference, but please double-check that is still the case in the end of October when this conference takes place in Maryland.

- Review written by Henry Dalziel on Thursday, August 13th 2020.
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Event Summary

The following description was either submitted by the Conference Organizer on Thursday, August 13th 2020, or created by us.

Modern societies are becoming dependent on information, automation, and communication technologies more than ever. Managing the security of the emerging systems, many of them safety-critical poses significant challenges.

The 11th Conference on Decision and Game Theory for Security (GameSec 2020) focuses on the protection of heterogeneous, large-scale, and dynamic cyber-physical systems as well as managing security risks faced by critical infrastructures through rigorous and practically-relevant analytical methods.

GameSec 2020 invites novel, high-quality theoretical and practically-relevant contributions, which apply decision and game theory, as well as related techniques such as optimization, machine learning, dynamic control, and mechanism design, to build resilient, secure, and dependable networked systems. The goal of GameSec 2020 is to bring together academic and industrial researchers in an effort to identify and discuss the major technical challenges and recent results that highlight the connections between game theory, control, distributed optimization, machine learning, economic incentives, and real-world security, reputation, trust, and privacy problems.

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