Product details:
| ISBN13: | 9783031158926 |
| ISBN10: | 303115892X |
| Binding: | Paperback |
| No. of pages: | 48 pages |
| Size: | 235x155 mm |
| Weight: | 109 g |
| Language: | English |
| Illustrations: | 10 Illustrations, black & white; 22 Illustrations, color |
| 448 |
Category:
Machine Learning for Cybersecurity
Innovative Deep Learning Solutions
Series:
SpringerBriefs in Computer Science;
Edition number: 1st ed. 2022
Publisher: Springer
Date of Publication: 25 September 2022
Number of Volumes: 1 pieces, Book
Normal price:
Publisher's listprice:
EUR 58.84
EUR 58.84
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22 963 (21 869 HUF + 5% VAT )
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Short description:
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 computerscience 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.
Long description:
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.
Table of Contents:
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.
