| 书目名称 | Handbook of Big Data Analytics and Forensics |
| 编辑 | Kim-Kwang Raymond Choo,Ali Dehghantanha |
| 视频video | http://file.papertrans.cn/421/420871/420871.mp4 |
| 概述 | Covers advances in big data analytics and digital forensics from an interdisciplinary lens.Provides a comprehensive review and bibliometric analysis of big data and IoT applications, as well as future |
| 图书封面 |  |
| 描述 | .This handbook discusses challenges and limitations in existing solutions, and presents state-of-the-art advances from both academia and industry, in big data analytics and digital forensics. The second chapter comprehensively reviews IoT security, privacy, and forensics literature, focusing on IoT and unmanned aerial vehicles (UAVs). The authors propose a deep learning-based approach to process cloud’s log data and mitigate enumeration attacks in the third chapter. The fourth chapter proposes a robust fuzzy learning model to protect IT-based infrastructure against advanced persistent threat (APT) campaigns. Advanced and fair clustering approach for industrial data, which is capable of training with huge volume of data in a close to linear time is introduced in the fifth chapter, as well as offering an adaptive deep learning model to detect cyberattacks targeting cyber physical systems (CPS) covered in the sixth chapter. .The authors evaluate the performance of unsupervised machine learning for detecting cyberattacks against industrial control systems (ICS) in chapter 7, and the next chapter presents a robust fuzzy Bayesian approach for ICS’s cyber threat hunting. This handbook a |
| 出版日期 | Book 2022 |
| 关键词 | cyber threat; cyber security; privacy; big data; threat intelligence; machine learning; cyber forensics; in |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-74753-4 |
| isbn_softcover | 978-3-030-74755-8 |
| isbn_ebook | 978-3-030-74753-4 |
| copyright | Springer Nature Switzerland AG 2022 |
|Archiver|手机版|小黑屋|
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