Overview

This book will focus on two specific aspects, namely deep learning vulnerabilities and cyber security. As for deep learning, deep neural network architectures are considered to be robust to random perturbations. Nevertheless, it is shown that they could be severely vulnerable to slight but carefully crafted perturbations of the input, termed as adversarial samples. In recent years, numerous studies have been conducted in this new area called """"Adversarial Machine Learning"""" to devise new adversarial attacks and to defend against these attacks with more robust DNN architectures. As for cyber security, the protection and processing of sensitive data in big data systems is a common problem as the increase in data size increases the need for high processing power. Protection of the sensitive data on a system that contains multiple connections with different privacy policies also brings the need for proper cryptographic key exchange methods for each party, as extra work. This book gives detail on the new threats and mitigation methods in the cyber security domain. It provides information on the new threats in new technologies such as vulnerabilities in deep learning, data privacy problems with GDPR, and new solutions.

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9781799890638

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Ferhat Ozgur Catak, Simula Research Laboratory, Oslo, Norway

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