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Название: Adversarial Deep Learning in Cybersecurity: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence
Автор: Aneesh Sreevallabh Chivukula, Xinghao Yang, Bo Liu
Издательство: Springer
Год: 2023
Страниц: 314
Язык: английский
Формат: pdf
Размер: 10.2 MB

A critical challenge in Deep Learning is the vulnerability of Deep Learning networks to security attacks from intelligent cyber adversaries. Even innocuous perturbations to the training data can be used to manipulate the behaviour of deep networks in unintended ways. In this book, we review the latest developments in adversarial attack technologies in Computer vision; Natural Language Processing (NLP); and cybersecurity with regard to multidimensional, textual and image data, sequence data, and temporal data. In turn, we assess the robustness properties of Deep Learning networks to produce a taxonomy of adversarial examples that characterises the security of learning systems using game theoretical Adversarial Deep Learning algorithms. The state-of-the-art in adversarial perturbation-based privacy protection mechanisms is also reviewed.

We propose new adversary types for game theoretical objectives in non-stationary computational learning environments. Proper quantification of the hypothesis set in the decision problems of our research leads to various functional problems, oracular problems, sampling tasks, and optimization problems. We also address the defence mechanisms currently available for Deep Learning models deployed in real-world environments. The learning theories used in these defence mechanisms concern data representations, feature manipulations, misclassifications costs, sensitivity landscapes, distributional robustness, and complexity classes of the Adversarial Deep Learning algorithms and their applications.

In closing, we propose future research directions in Adversarial Deep Learning applications for resilient learning system design and review formalized learning assumptions concerning the attack surfaces and robustness characteristics of Artificial Intelligence applications so as to deconstruct the contemporary Adversarial Deep Learning designs.

This book is relevant for Adversarial Machine Learning practitioners and Adversarial Artificial Intelligence researchers working in the design and application of Adversarial Deep Learning theories in Machine Learning, Deep Learning, data mining, and knowledge discovery algorithms design. Particular emphasis is placed on the real-world application domains of Adversarial Deep Learning in the development of Data Science, Big Data analytics, and cybersecurity solutions. The Adversarial Deep Learning theories are summarized with reference to capabilities of computational algorithms in pattern recognition, game theory, computational mathematics, and numerical analysis. The resultant analytics algorithmics, deep neural networks, and adversarial loss functions review the state of the art in the implementation of adversarial algorithms, their attack surfaces, concepts, and methods from the perspective of game theoretical Machine Learning. The book explores the systems theoretic dependence between randomization in adversarial manipulations and generalizability in blackbox optimizations of the game theoretical Adversarial Deep Learning. It aids future research, design, development, and innovations in the game theoretical Adversarial Deep Learning algorithms applicable to cyberspace security data mining problems.

The book also serves as a reference on the existing literature that can be implemented by researchers as baseline models to empirically compare the relevant attack scenarios and defense mechanisms for Adversarial Deep Learning. The known invasive techniques and their countermeasures to develop future cybersecurity capabilities are reviewed. The security issues and vulnerabilities in the machine/deep learning solutions are mainly located within the deep layers of mathematical formulation and mechanism of the learning methods. The game theoretical formulations of the adversarial learning in the book leverage Deep Learning and Big Data to solve for adversarial samples that effect data manipulation on the learnt discriminative learning baselines. Several such learning baselines must be built to generate an adversary’s attack hypothesis and consequent defense mechanisms available for adjusting the decision boundaries in discriminative learning. Thus the research questions covered in the book can set the stage for strategies and expectations in the Adversarial Deep Learning capabilities offered around cyber adversaries’ Tools, Tactics, Techniques, and Procedures (TTPs) in the cyber kill chain. They can assess, prioritize, and select the high-risk use case scenarios of cyber threats targeting Deep Learning models in security detection/prevention layers.









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