001461497 000__ 05065cam\a22006377i\4500
001461497 001__ 1461497
001461497 003__ OCoLC
001461497 005__ 20230503003356.0
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001461497 007__ cr\cn\nnnunnun
001461497 008__ 230317s2023\\\\sz\\\\\\ob\\\\000\0\eng\d
001461497 019__ $$a1372015985$$a1372396305
001461497 020__ $$a9783030997724$$qelectronic book
001461497 020__ $$a3030997723$$qelectronic book
001461497 020__ $$z9783030997717
001461497 020__ $$z3030997715
001461497 0247_ $$a10.1007/978-3-030-99772-4$$2doi
001461497 035__ $$aSP(OCoLC)1373259870
001461497 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dYDX$$dN$T$$dUKAHL$$dOCLCF$$dYDX
001461497 049__ $$aISEA
001461497 050_4 $$aQA76.9.A25$$bC55 2023
001461497 08204 $$a005.8$$223/eng/20230317
001461497 1001_ $$aChivukula, Aneesh Sreevallabh,$$eauthor.
001461497 24510 $$aAdversarial machine learning :$$battack surfaces, defence mechanisms, learning theories in artificial intelligence /$$cAneesh Sreevallabh Chivukula, Xinghao Yang, Bo Liu, Wei Liu, Wanlei Zhou.
001461497 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2023]
001461497 300__ $$a1 online resource (1 volume)
001461497 336__ $$atext$$btxt$$2rdacontent
001461497 337__ $$acomputer$$bc$$2rdamedia
001461497 338__ $$aonline resource$$bcr$$2rdacarrier
001461497 504__ $$aIncludes bibliographical references.
001461497 5050_ $$aAdversarial Machine Learning -- Adversarial Deep Learning -- Security and Privacy in Adversarial Learning -- Game-Theoretical Attacks with Adversarial Deep Learning Models -- Physical Attacks in the Real World -- Adversarial Defense Mechanisms -- Adversarial Learning for Privacy Preservation.
001461497 506__ $$aAccess limited to authorized users.
001461497 520__ $$aA 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; 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. Given its scope, the book will be of interest to Adversarial Machine Learning practitioners and Adversarial Artificial Intelligence researchers whose work involves the design and application of Adversarial Deep Learning.
001461497 588__ $$aDescription based on online resource; title from digital title page (viewed on April 27, 2023).
001461497 650_0 $$aComputer security.
001461497 650_0 $$aDeep learning (Machine learning)
001461497 655_0 $$aElectronic books.
001461497 7001_ $$aYang, Xinghao,$$eauthor.
001461497 7001_ $$aLiu, Bo,$$eauthor.
001461497 7001_ $$aLiu, Wei$$c(Chemical engineer),$$eauthor.
001461497 7001_ $$aZhou, Wanlei,$$eauthor.$$1https://isni.org/isni/0000000117462496
001461497 77608 $$iPrint version:$$aChivukula, Aneesh Sreevallabh.$$tAdversarial deep learning in cybersecurity.$$dCham : Springer, 2022$$z9783030997717$$w(OCoLC)1338684528
001461497 852__ $$bebk
001461497 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-99772-4$$zOnline Access$$91397441.1
001461497 909CO $$ooai:library.usi.edu:1461497$$pGLOBAL_SET
001461497 980__ $$aBIB
001461497 980__ $$aEBOOK
001461497 982__ $$aEbook
001461497 983__ $$aOnline
001461497 994__ $$a92$$bISE