In the past few years, with the evolution of advanced persistent threats and mutation techniques, sensitive and damaging information from a variety of sources have been exposed to possible corruption and hacking. Machine learning, artificial intelligence, predictive analytics, and similar disciplines of cognitive science applications have been found to have significant applications in the domain of cyber security. Machine Learning and Cognitive Science Applications in Cyber Security examines different applications of cognition that can be used to detect threats and analyze data to capture malware. Highlighting such topics as anomaly detection, intelligent platforms, and triangle scheme, this publication is designed for IT specialists, computer engineers, researchers, academicians, and industry professionals interested in the impact of machine learning in cyber security and the methodologies that can help improve the performance and reliability of machine learning applications.
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Muhammad Salman Khan is a senior research scientist in cognitive machine intelligence applications in cyber threat hunting and is leading projects in advanced machine learning application in cyber security. Salman has more than a decade of industrial experience in leading research and development projects in cyber security, enterprise networking products and real time data acquisition systems. Salman’s Ph.D. research was focussed on improving cognitive performance of machine intelligence algorithms to address the growing challenges of advanced and persistent malware. Salman did his M.S. in Electrical Engineering from Rutgers University USA. Salman has been a recipient of notable national and international awards including Fulbright scholarship, Mitacs Canada fellowship, NSERC PGS (D) scholarship as well as receiving nomination for Canadian Vanier scholarship. He authored and co-authored more than 25 research publications including conference papers, journals and book chapters. Salman is a founder of a technology start-up and has been granted various multi-year research and development funds in technological innovation and development which subsequently led to successful commercialization.
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