UID:
almahu_9949286387302882
Format:
X, 361 p. 43 illus., 28 illus. in color.
,
online resource.
Edition:
1st ed. 2022.
ISBN:
9783030987954
Series Statement:
Lecture Notes in Computer Science, 13049
Content:
AI has become an emerging technology to assess security and privacy, with many challenges and potential solutions at the algorithm, architecture, and implementation levels. So far, research on AI and security has looked at subproblems in isolation but future solutions will require sharing of experience and best practice in these domains. The editors of this State-of-the-Art Survey invited a cross-disciplinary team of researchers to a Lorentz workshop in 2019 to improve collaboration in these areas. Some contributions were initiated at the event, others were developed since through further invitations, editing, and cross-reviewing. This contributed book contains 14 invited chapters that address side-channel attacks and fault injection, cryptographic primitives, adversarial machine learning, and intrusion detection. The chapters were evaluated based on their significance, technical quality, and relevance to the topics of security and AI, and each submission was reviewed in single-blind mode and revised. .
Note:
AI for Cryptography -- Artificial Intelligence for the Design of Symmetric Cryptographic Primitives -- Traditional Machine Learning Methods for Side-Channel Analysis -- Deep Learning on Side-Channel Analysis -- Artificial Neural Networks and Fault Injection Attacks -- Physically Unclonable Functions and AI: Two Decades of Marriage -- AI for Authentication and Privacy -- Privacy-Preserving Machine Learning using Cryptography -- Machine Learning Meets Data Modification: the Potential of Pre-processing for Privacy Enhancement -- AI for Biometric Authentication Systems -- Machine Learning and Deep Learning for Hardware Fingerprinting. - AI for Intrusion Detection -- Intelligent Malware Defenses -- Open-World Network Intrusion Detection -- Security of AI -- Adversarial Machine Learning -- Deep Learning Backdoors. - On Implementation-level Security of Edge-based Machine Learning Models.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9783030987947
Additional Edition:
Printed edition: ISBN 9783030987961
Language:
English
DOI:
10.1007/978-3-030-98795-4
URL:
https://doi.org/10.1007/978-3-030-98795-4
