| 書(shū)目名稱 | Data Science in Cybersecurity and Cyberthreat Intelligence |
| 編輯 | Leslie F. Sikos,Kim-Kwang Raymond Choo |
| 視頻video | http://file.papertrans.cn/264/263126/263126.mp4 |
| 概述 | Presents the state of the art in cybersecurity, and critically reviews existing approaches.Addresses the intersection of two hot topics: cybersecurity and data science.Includes an essential introducti |
| 叢書(shū)名稱 | Intelligent Systems Reference Library |
| 圖書(shū)封面 |  |
| 描述 | .This book presents a collection of state-of-the-art approaches to utilizing machine learning, formal knowledge bases and rule sets, and semantic reasoning to detect attacks on communication networks, including IoT infrastructures, to automate malicious code detection, to efficiently predict cyberattacks in enterprises, to identify malicious URLs and DGA-generated domain names, and to improve the security of mHealth wearables. This book details how analyzing the likelihood of vulnerability exploitation using machine learning classifiers can offer an alternative to traditional penetration testing solutions. In addition, the book describes a range of techniques that support data aggregation and data fusion to automate data-driven analytics in cyberthreat intelligence, allowing complex and previously unknown cyberthreats to be identified and classified, and countermeasures to be incorporated in novel incident response and intrusion detection mechanisms. . |
| 出版日期 | Book 2020 |
| 關(guān)鍵詞 | Cybersecurity; Cybersituational Awareness; Cyberthreat Intelligence; Data Science; Artificial Intelligen |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-38788-4 |
| isbn_softcover | 978-3-030-38790-7 |
| isbn_ebook | 978-3-030-38788-4Series ISSN 1868-4394 Series E-ISSN 1868-4408 |
| issn_series | 1868-4394 |
| copyright | Springer Nature Switzerland AG 2020 |
|Archiver|手機(jī)版|小黑屋|
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