Free shipping on orders over $99
Deployable Machine Learning for Security Defense

Deployable Machine Learning for Security Defense

Second International Workshop, MLHat 2021, Virtual Event, August 15, 2021, Proceedings

by Gang WangArridhana Ciptadi and Ali Ahmadzadeh
Paperback
Publication Date: 25/09/2021

Share This Book:

  $109.00
or 4 easy payments of $27.25 with
afterpay
This item qualifies your order for FREE DELIVERY
This book constitutes selected and extended papers from the Second International Workshop on Deployable Machine Learning for Security Defense, MLHat 2021, held in August 2021. Due to the COVID-19 pandemic the conference was held online.
The 6 full papers were thoroughly reviewed and selected from 7 qualified submissions. The papers are organized in topical sections on machine learning for security, and malware attack and defense.
ISBN:
9783030878382
9783030878382
Category:
Machine learning
Format:
Paperback
Publication Date:
25-09-2021
Publisher:
Springer Nature Switzerland AG
Country of origin:
Switzerland
Pages:
157
Dimensions (mm):
235x155mm
Weight:
0.27kg

This title is in stock with our Australian supplier and should arrive at our Sydney warehouse within 2 - 3 weeks of you placing an order.

Once received into our warehouse we will despatch it to you with a Shipping Notification which includes online tracking.

Please check the estimated delivery times below for your region, for after your order is despatched from our warehouse:

ACT Metro: 2 working days
NSW Metro: 2 working days
NSW Rural: 2-3 working days
NSW Remote: 2-5 working days
NT Metro: 3-6 working days
NT Remote: 4-10 working days
QLD Metro: 2-4 working days
QLD Rural: 2-5 working days
QLD Remote: 2-7 working days
SA Metro: 2-5 working days
SA Rural: 3-6 working days
SA Remote: 3-7 working days
TAS Metro: 3-6 working days
TAS Rural: 3-6 working days
VIC Metro: 2-3 working days
VIC Rural: 2-4 working days
VIC Remote: 2-5 working days
WA Metro: 3-6 working days
WA Rural: 4-8 working days
WA Remote: 4-12 working days

Reviews

Be the first to review Deployable Machine Learning for Security Defense.