Machine Learning and Security Protecting Systems with Data and Algorithms
Langue : Anglais
Auteurs : Chio Clarence, Freeman David
Can machine learning techniques solve our computer security problems and
finally put an end to the cat-and-mouse game between attackers and
defenders? Or is this hope merely hype? Now you can dive into the science
and answer this question for yourself. With this practical guide, you’ll
explore ways to apply machine learning to security issues such as
intrusion detection, malware classification, and network analysis.
Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike.
- Learn how machine learning has contributed to the success of modern spam filters
- Quickly detect anomalies, including breaches, fraud, and impending system failure
- Conduct malware analysis by extracting useful information from computer binaries
- Uncover attackers within the network by finding patterns inside datasets
- Examine how attackers exploit consumer-facing websites and app functionality
- Translate your machine learning algorithms from the lab to production
- Understand the threat attackers pose to machine learning solutions
Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike.
- Learn how machine learning has contributed to the success of modern spam filters
- Quickly detect anomalies, including breaches, fraud, and impending system failure
- Conduct malware analysis by extracting useful information from computer binaries
- Uncover attackers within the network by finding patterns inside datasets
- Examine how attackers exploit consumer-facing websites and app functionality
- Translate your machine learning algorithms from the lab to production
- Understand the threat attackers pose to machine learning solutions
Date de parution : 02-2018
Ouvrage de 370 p.
18.1x23.3 cm
Disponible chez l'éditeur (délai d'approvisionnement : 12 jours).
Prix indicatif 67,11 €
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