Security and Artificial Intelligence
Springer International Publishing (Verlag)
978-3-030-98794-7 (ISBN)
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-blindmode and revised.
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.
| Erscheinungsdatum | 10.04.2022 |
|---|---|
| Reihe/Serie | Lecture Notes in Computer Science |
| Zusatzinfo | X, 361 p. 43 illus., 28 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 569 g |
| Themenwelt | Informatik ► Netzwerke ► Sicherheit / Firewall |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Adversarial Machine Learning • Applications • Artificial Intelligence • authentication • Biometric Authentication Systems • computer crime • Computer Science • conference proceedings • cryptography • Data Security • Deep Learning Backdoors • fault injection • Hardware Fingerprinting • Informatics • machine learning • Malware • Network Intrusion Detection • Network Protocols • Network Security • Physically unclonable functions • privacy • Privacy Enhancement • Privacy-Preserving Machine Learning • Research • Side-Channel Analysis |
| ISBN-10 | 3-030-98794-9 / 3030987949 |
| ISBN-13 | 978-3-030-98794-7 / 9783030987947 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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