DocumentCode :
1797385
Title :
Cognitive neural network for cybersecurity
Author :
Perlovsky, Leonid ; Shevchenko, Olexander
Author_Institution :
Athinoula Martinos Imaging Center, Harvard Univ., Charlestown, MA, USA
fYear :
2014
fDate :
6-11 July 2014
Firstpage :
4056
Lastpage :
4061
Abstract :
This chapter discusses the future of cybersecurity as warfare between machine learning techniques of attackers and defenders. As attackers will learn to evolve new camouflaging methods for evading better and better defenses, defense techniques will in turn learn new attacker´s tricks to defend against. The better technology will win. Here we discuss theory of machine learning based on dynamic logic that are mathematically provable to learn with the fastest possible speed. We also discuss cognitive functions of dynamic logic and related experimental proofs. This new mathematical theory, in addition to being provably fastest machine learning technique, is also an adequate model for several fundamental mechanisms of the mind.
Keywords :
cognitive systems; learning (artificial intelligence); neural nets; security of data; camouflaging methods; cognitive functions; cognitive neural network; cybersecurity; dynamic logic; experimental proofs; machine learning techniques; Abstracts; Cognition; Computers; Machine learning algorithms; Malware; Mathematical model;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), 2014 International Joint Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6627-1
Type :
conf
DOI :
10.1109/IJCNN.2014.6889430
Filename :
6889430
Link To Document :
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