STONewsArchive: Research Workshop (RWS) on Deep Machine Learning for Cyber Defense

Title: Research Workshop (RWS) on Deep Machine Learning for Cyber Defense
Start_Publishing: 30/06/2021
Panel_Page: IST
Page_ID: 3827
Main_Body_Multi: NATO Allies are responding with increasingly robust security and defence of the cyber landscape, especially as it intersects with military systems, platforms, and missions. Deep machine learning (ML) is a state-of-the-art technology that the military can implement to enhance its strategic cyber position and create a defense that not only addresses threats of today, but threats 10 and 20 years into the future.


With the aim of enhancing the Alliance’s Cyber Defense, a three-day event – planned to take place virtually from 19 to 21 October 2021 – intends to consolidate the NATO-wide knowledge in the field of deep ML applications of cyber defense. This includes identifying gaps between civilian solutions and military needs, and foster collaboration among NATO countries in order to identify and pursue the most promising technologies and applications to the military domain, including computational techniques, architectures, and data-sets.


Some of the high-level S&T direction from NATO Commands on future challenges will also be considered. This IST-183 Research Workshop should also serve as an information exchange arena between the research and defense communities whereby the research can be exploited, and inform options for future experimentation and technical specifications.

Page_Intro: NATO Allies are responding with increasingly robust security and defence of the cyber landscape, especially as it intersects with military systems, platforms, and missions. Deep machine learning (ML) is a state-of-the-art technology that the military can implement to enhance its strategic cyber position and create a defense that not only addresses threats of today, but threats 10 and 20 years into the future.


With the aim of enhancing the Alliance’s Cyber Defense, a three-day event – planned to take place virtually from 19 to 21 October 2021 – intends to consolidate the NATO-wide knowledge in the field of deep ML applications of cyber defense. This includes identifying gaps between civilian solutions and military needs, and foster collaboration among NATO countries in order to identify and pursue the most promising technologies and applications to the military domain, including computational techniques, architectures, and data-sets.


Some of the high-level S&T direction from NATO Commands on future challenges will also be considered. This IST-183 Research Workshop should also serve as an information exchange arena between the research and defense communities whereby the research can be exploited, and inform options for future experimentation and technical specifications.

HomePageImage: 2021-IST-183.jpg
HomePageBodyText: NATO Allies are responding with increasingly robust security and defence of the cyber landscape, especially as it intersects with military systems, platforms, and missions. Deep machine learning (ML) is a state-of-the-art technology that the military can implement to enhance its strategic cyber position and create a defense that not only addresses threats of today, but threats 10 and 20 years into the future.


With the aim of enhancing the Alliance’s Cyber Defense, a three-day event – planned to take place virtually from 19 to 21 October 2021 – intends to consolidate the NATO-wide knowledge in the field of deep ML applications of cyber defense. This includes identifying gaps between civilian solutions and military needs, and foster collaboration among NATO countries in order to identify and pursue the most promising technologies and applications to the military domain, including computational techniques, architectures, and data-sets.


Some of the high-level S&T direction from NATO Commands on future challenges will also be considered. This IST-183 Research Workshop should also serve as an information exchange arena between the research and defense communities whereby the research can be exploited, and inform options for future experimentation and technical specifications.


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Created at 30/06/2021 17:15 by ad.rodes
Last modified at 30/06/2021 17:15 by ad.rodes
 
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