// Comparison

The Android Malware Handbook vs The Art of Memory Forensics: Which Should You Read?

Two cybersecurity books on Malware, compared honestly: who each is for, what each does best, and which to read first.

Advanced

4/52023

Using Manual Analysis and ML-Based Detection

Qian Han, Salvador Mandujano, Sebastian Porst, V.S. Subrahmanian, Sai Deep Tetali, Yanhai Xiong

Machine-learning researchers and members of Meta's and Google's Android Security teams distill years of research into detecting banking trojans, ransomware, and SMS fraud on Android, combining manual analysis with classification models.

Advanced

5/52014

The Art of Memory Forensics

Detecting Malware and Threats in Windows, Linux, and Mac Memory

Michael Hale Ligh, Andrew Case, Jamie Levy, AAron Walters

Ligh, Case, Levy, and Walters' canonical reference on memory analysis with Volatility — the technique, the tooling, and the operating-system internals it depends on, across Windows, Linux, and macOS.

Read this if

Mobile security engineers and ML practitioners who need to go beyond signature-based Android AV into classification models and feature engineering for malware families. Written by people who actually built detection at Android-scale, not academics theorizing about it.

Incident responders, threat hunters, and malware analysts moving past disk forensics into the place where modern attackers actually live: in memory, in transit, and unbacked by files on disk. Also the textbook for the GCFA-and-beyond DFIR career path.

Skip this if

Readers who want iOS coverage (none here) or who need an introduction to machine learning itself; the book assumes ML fluency and applies it to Android malware specifically, rather than teaching ML from scratch.

Beginners with no OS-internals background; the book assumes you know what a process, a handle, and a kernel object are. Also dated on Volatility 3 — written for 2.x — though the conceptual material translates cleanly.

Key takeaways

Walks the history of Android malware in the wild since the OS launched, giving the classification models real evolutionary context instead of a static snapshot.
Covers both static and dynamic analysis of real specimens before getting to the ML layer, so detection models sit on top of sound manual analysis.
Breaks down ML strategies by malware category (banking trojans, ransomware, SMS fraud), each with the specific features that actually discriminate it.

Memory is the only place where modern post-exploitation tools are guaranteed to be honest; the book makes that argument by showing what you can recover that disk cannot.
Volatility plugins are an investigative grammar — once you know the verbs, you can construct the questions; the book is the dictionary for the grammar.
Cross-OS memory forensics is one workflow with three dialects; the unified Windows/Linux/macOS coverage is the book's underrated structural choice.

How they compare

We rate The Art of Memory Forensics higher (5/5 against 4/5 for The Android Malware Handbook). For most readers, that means The Art of Memory Forensics is the primary pick and The Android Malware Handbook is a useful follow-up.

Both books target advanced-level readers, so the choice is about topic, not difficulty.

The Android Malware Handbook and The Art of Memory Forensics both cover Malware, so reading them in sequence reinforces the same material from different angles.

Keep reading

The Android Malware Handbook

→ Alternatives to The Android Malware Handbook → What to read after The Android Malware Handbook