Machine Learning for Cybersecurity : Innovative Deep Learning Solutions / by Marwan Omar
(SpringerBriefs in Computer Science. ISSN:21915776)
データ種別 | 電子ブック |
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版 | 1st ed. 2022. |
出版者 | (Cham : Springer International Publishing : Imprint: Springer) |
出版年 | 2022 |
大きさ | VIII, 48 p. 32 illus., 22 illus. in color : online resource |
著者標目 | *Omar, Marwan author SpringerLink (Online service) |
書誌詳細を非表示
一般注記 | 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 HTTP:URL=https://doi.org/10.1007/978-3-031-15893-3 |
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件 名 | LCSH:Machine learning LCSH:Computer networks -- Security measures 全ての件名で検索 FREE:Machine Learning FREE:Mobile and Network Security |
分 類 | LCC:Q325.5-.7 DC23:006.31 |
書誌ID | EB00000765 |
ISBN | 9783031158933 |
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