Machine Learning and Security: Protecting Systems with Data and Algorithms

Clarence Chio, David Freeman

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商品描述

Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? The best way to find out is to dive into the science and do lots of testing and experimentation. With this practical guide, you’ll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis.

Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike.

  • Learn how machine learning has contributed to the success of modern spam filters
  • Quickly detect anomalies, including breaches, fraud, and impending system failure
  • Conduct malware analysis by extracting useful information from computer binaries
  • Uncover attackers within the network by finding patterns inside datasets
  • Examine how attackers exploit consumer-facing websites and app functionality
  • Translate your machine learning algorithms from the lab to production
  • Understand the threat attackers pose to machine learning solutions

商品描述(中文翻譯)

機器學習技術能否解決我們的電腦安全問題,並最終結束攻擊者和防禦者之間的貓鼠遊戲?還是這只是一種炒作?要找出答案,最好的方法就是深入研究並進行大量的測試和實驗。在這本實用指南中,您將探索如何將機器學習應用於入侵檢測、惡意軟件分類和網絡分析等安全問題。

機器學習和安全專家Clarence Chio和David Freeman提供了一個討論這兩個領域結合的框架,以及一個機器學習算法工具包,您可以應用於各種安全問題。這本書非常適合安全工程師和數據科學家。

- 學習機器學習如何為現代垃圾郵件過濾器的成功做出貢獻
- 快速檢測異常,包括入侵、欺詐和即將系統故障
- 通過從計算機二進制文件中提取有用信息進行惡意軟件分析
- 通過在數據集中尋找模式來發現網絡內的攻擊者
- 檢查攻擊者如何利用面向消費者的網站和應用功能
- 將您的機器學習算法從實驗室應用到生產環境
- 了解攻擊者對機器學習解決方案的威脅