Machine Learning for Cybersecurity Cookbook

Tsukerman, Emmanuel

  • 出版商: Packt Publishing
  • 出版日期: 2019-11-22
  • 售價: $1,420
  • 貴賓價: 9.5$1,349
  • 語言: 英文
  • 頁數: 346
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1789614678
  • ISBN-13: 9781789614671
  • 相關分類: Machine Learning 機器學習 資訊安全
  • 下單後立即進貨 (約3~4週)

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商品描述

Organizations today face a major threat in terms of cybersecurity, from malicious URLs to credential reuse, and having robust security systems can make all the difference. With this book, you'll learn how to use Python libraries such as TensorFlow and scikit-learn to implement the latest artificial intelligence (AI) techniques and handle challenges faced by cybersecurity researchers.

You'll begin by exploring various machine learning (ML) techniques and tips for setting up a secure lab environment. Next, you'll implement key ML algorithms such as clustering, gradient boosting, random forest, and XGBoost. The book will guide you through constructing classifiers and features for malware, which you'll train and test on real samples. As you progress, you'll build self-learning, reliant systems to handle cybersecurity tasks such as identifying malicious URLs, spam email detection, intrusion detection, network protection, and tracking user and process behavior. Later, you'll apply generative adversarial networks (GANs) and autoencoders to advanced security tasks. Finally, you'll delve into secure and private AI to protect the privacy rights of consumers using your ML models.

By the end of this book, you'll have the skills you need to tackle real-world problems faced in the cybersecurity domain using a recipe-based approach.

  • Manage data of varying complexity to protect your system using the Python ecosystem
  • Apply ML to pentesting, malware, data privacy, intrusion detection system(IDS) and social engineering
  • Automate your daily workflow by addressing various security challenges using the recipes covered in the book