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Machine learning for cybersecurityinnovative deep learning solutions /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning for cybersecurityby Marwan Omar.
Reminder of title:	innovative deep learning solutions /
Author:	Omar, Marwan.
Published:	Cham :Springer International Publishing :2022.
Description:	viii, 48 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-3-031-15893-3
ISBN:	9783031158933$q(electronic bk.)

Machine learning for cybersecurityinnovative deep learning solutions /
Omar, Marwan.

Machine learning for cybersecurityinnovative deep learning solutions /[electronic resource] :by Marwan Omar. - Cham :Springer International Publishing :2022. - viii, 48 p. :ill. (chiefly color), digital ;24 cm. - SpringerBriefs in computer science,2191-5776. - SpringerBriefs in computer science..

1. Application of Machine Learning (ML) to Address Cyber Security Threats -- 2. New Approach to Malware Detection Using Optimized Convolutional Neural Network -- 3. Malware Anomaly Detection Using Local Outlier Factor Technique.

This SpringerBrief presents the underlying principles of machine learning and how to deploy various deep learning tools and techniques to tackle and solve certain challenges facing the cybersecurity industry. By implementing innovative deep learning solutions, cybersecurity researchers, students and practitioners can analyze patterns and learn how to prevent cyber-attacks and respond to changing malware behavior. The knowledge and tools introduced in this brief can also assist cybersecurity teams to become more proactive in preventing threats and responding to active attacks in real time. It can reduce the amount of time spent on routine tasks and enable organizations to use their resources more strategically. In short, the knowledge and techniques provided in this brief can help make cybersecurity simpler, more proactive, less expensive and far more effective Advanced-level students in computer science studying machine learning with a cybersecurity focus will find this SpringerBrief useful as a study guide. Researchers and cybersecurity professionals focusing on the application of machine learning tools and techniques to the cybersecurity domain will also want to purchase this SpringerBrief.

ISBN: 9783031158933$q(electronic bk.)

Standard No.: 10.1007/978-3-031-15893-3doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .O53 2022

Dewey Class. No.: 006.31

Machine learning for cybersecurityinnovative deep learning solutions /
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505 0 $a 1. Application of Machine Learning (ML) to Address Cyber Security Threats -- 2. New Approach to Malware Detection Using Optimized Convolutional Neural Network -- 3. Malware Anomaly Detection Using Local Outlier Factor Technique.
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