Deployable Machine Learning for Security Defense (eBook, PDF)

First International Workshop, MLHat 2020, San Diego, CA, USA, August 24, 2020, Proceedings
Redaktion: Wang, Gang; Ahmadzadeh, Ali; Ciptadi, Arridhana

• Format: PDF

Produktbeschreibung

This book constitutes selected papers from the First International Workshop on Deployable Machine Learning for Security Defense, MLHat 2020, held in August 2020. Due to the COVID-19 pandemic the conference was held online.

The 8 full papers were thoroughly reviewed and selected from 13 qualified submissions. The papers are organized in the following topical sections: understanding the adversaries; adversarial ML for better security; threats on networks.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 165
• Erscheinungstermin: 17. Oktober 2020
• Englisch
• ISBN-13: 9783030596217
• Artikelnr.: 62299077

Inhaltsangabe

Understanding the Adversaries.- Adversarial ML for Better Security.- Threats on Networks.