Machine Learning for Cybersecurity : Innovative Deep Learning Solutions / by Marwan Omar

By:

Omar, Marwan [VerfasserIn]

Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: SpringerBriefs in Computer SciencePublisher: Cham : Springer International Publishing, 2022Publisher: Cham : Imprint: Springer, 2022Edition: 1st ed. 2022Description: 1 Online-Ressource(VIII, 48 p. 32 illus., 22 illus. in color.)ISBN:

9783031158933

Subject(s):

Additional physical formats: 9783031158926 | 9783031158940 | Erscheint auch als: 9783031158926 Druck-Ausgabe | Erscheint auch als: 9783031158940 Druck-AusgabeDOI: DOI: 10.1007/978-3-031-15893-3Online resources:

Summary: 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. .Summary: 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.PPN: PPN: 1817570765Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-SCS | ZDB-2-SXCS

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