| 書目名稱 | Smart Log Data Analytics | | 副標題 | Techniques for Advan | | 編輯 | Florian Skopik,Markus Wurzenberger,Max Landauer | | 視頻video | http://file.papertrans.cn/869/868860/868860.mp4 | | 概述 | Contains a comprehensive presentation of novel methods to parse, process and analyze log data.Provides insights into the inner mechanisms of novel machine learning approaches.Presents step-by-step exa | | 圖書封面 |  | | 描述 | This book provides insights into smart ways of computer log data analysis, with the goal of spotting adversarial actions. It is organized into 3 major parts with a total of 8 chapters that include a detailed view on existing solutions, as well as novel techniques that go far beyond state of the art. The first part of this book motivates the entire topic and highlights major challenges, trends and design criteria for log data analysis approaches, and further surveys and compares the state of the art. The second part of this book introduces concepts that apply character-based, rather than token-based, approaches and thus work on a more fine-grained level. Furthermore, these solutions were designed for “online use”, not only forensic analysis, but also process new log lines as they arrive in an efficient single pass manner. An advanced method for time series analysis aims at detecting changes in the overall behavior profile of an observed system and spotting trends and periodicitiesthrough log analysis. The third part of this book introduces the design of the AMiner, which is an advanced open source component for log data anomaly mining. The AMiner comes with several detectors to spot | | 出版日期 | Book 2021 | | 關(guān)鍵詞 | Cyber security; computer security; log data analysis; anomaly detection; machine learning; system behavio | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-74450-2 | | isbn_softcover | 978-3-030-74452-6 | | isbn_ebook | 978-3-030-74450-2 | | copyright | Springer Nature Switzerland AG 2021 |
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