A Practical Guide to AI in Cybersecurity: What You Need to Know, Download for Free

amy 09/12/2025

If you’ve ever looked at the growing role of AI in cybersecurity and thought, “Okay, but how do I actually use this?”, then this book is for you.

AI for Cybersecurity: A Handbook of Use Cases, by Peng Liu and the team from Penn State’s Cybersecurity Lab, isn’t another dense academic paper. It’s a hands-on guide that walks you through real-world problems, and shows exactly how to apply deep learning (DL) and reinforcement learning (RL) to solve them.

What makes this book stand out? It doesn’t just talk about theory. It gives you working code, real datasets, and clear steps to follow, so you can learn by doing.

Whether you’re a security analyst, a student taking an “AI for Cybersecurity” course, or a developer looking to build smarter tools, this handbook is designed to help you get practical results.

Content Overview

Here’s what you’ll find inside:

  • How AI helps with reverse engineering: From analyzing binary files to identifying hidden behaviors in software, the book shows how deep learning can automate parts of the process.
  • Detecting Android malware: Step-by-step walkthroughs on turning raw app data into features, training models, and deploying them safely.
  • Spotting abnormal behavior in network traffic: Using sequential data models to catch anomalies — think of it as teaching your system to recognize when something just feels off.
  • Fighting DNS cache poisoning attacks: The authors walk through collecting and labeling DNS sessions, building models, and evaluating performance.
  • Identifying PC malware and code similarity: Practical examples showing how AI can compare binaries or code snippets to spot malicious patterns.
  • Malware clustering: Grouping similar threats automatically, super useful for threat intelligence teams.

Table of Contents

  • Introduction
    • Why a Handbook?
    • The Use Cases Intend to Solve Various Cybersecurity Challenges through A Unified DL Pipeline
    • How to Properly Use This Handbook?
    • Organization of Rest of The Book
  • AI Conducts Two Reverse Engineering Tasks
    • The Security Problem
    • Related Work
    • DL Pipeline
    • Model Architecture
    • Model Training Issues
    • Model Performance
    • Deployed Model
    • Source Code and Dataset
    • Remaining Issues
  • AI Detects Android Malware
    • The Security Problem
    • Android Malware Example
    • Machine Learning Pipeline for the Use Case
    • Feature Engineering
    • Training Data
    • Machine Learning
    • Model Deployment
    • System Evolution
    • Code, Data, and Other Issues
  • AI Detects Abnormal Events in Sequential Data
    • The Security Problem
    • Dataset
    • Data Processing
    • Model Architecture
    • Hyperparameter Tuning
    • Model Deployment
    • Evaluation
    • Code, Data, and OtherIssues
  • AI Detects DNS Cache Poisoning Attack
    • The Security Problem
    • Raw Data Generation and Collection
    • Labeling DNS Sessions
    • Feature Extraction and Data Sample Representation
    • Data Set Construction
    • Model Architecture
    • Parameter Tuning
    • Evaluation results
    • Model Deployment
    • Remaining Issue
    • Code and Data Resources
  • AI Detects PC Malware
    • The Security Problem
    • Raw Data
    • Data Processing
    • Model Training
    • Model Deployment
    • Remaining Issues
    • Code and Data Resources
  • AI Detects Code Similarity
    • The Security Problem
    • Raw Data
    • Data Processing
    • Model
    • Code, Data and Other Issues
  • AI Conducts Malware Clustering
    • The Security Problem
    • Machine Learning Pipeline
    • Example Data
    • Feature Extraction
    • Scalable Clustering
    • Clusters Deployment
    • Concluding Remarks

Extra

One thing the authors really emphasize? Standardizing the machine learning pipeline. They argue that consistency across steps, from data prep to model deployment, makes a huge difference in real-world reliability. And honestly? That’s solid advice we all should take seriously.

The best part? Everything’s open. The full manuscript is available as a free PDF from Penn State’s lab, and all the code and datasets are hosted on GitHub. No paywalls, no gatekeeping, just good, usable content.

If you’re diving into AI-powered security tools, this handbook is a great place to start. It’s not flashy, but it’s thoughtful, clear, and built for people who want to roll up their sleeves and build something meaningful.

Want to try it yourself? Head over to the GitHub repo and grab the code. Or download the PDF and start exploring.

And if you spot a typo or have feedback? Just open an issue on GitHub, they welcome contributions.

In short: this book isn’t just about AI in cybersecurity. It’s about using AI in cybersecurity, in a smart, repeatable, and responsible way.

Happy learning, and stay secure!
Written with care by someone who believes good tools should be accessible to everyone.

Cite

@book{aisecurity,
  title={AI for Cybersecurity: A Handbook of Use Cases},
  author={Liu, Peng and Liu, Tao and Luo, Nanqing and Shang, Zitong and Wang, Haizhou and Wang, Zhilong and Zhang, Lan and Zou, Qingtian},
  year={2022},
  url = {https://www.amazon.com/gp/product/B09T3123RB/},
  note = {Kindle edition},
  publisher={Amazon}
}

Download

PDF of manuscript is posted by Penn State Cybersecurity Lab. Users could download a copy (Download) for personal use, educational purpose and etc.

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