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The Art of Mac Malware, Volume 2 vs Malware Data Science: 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

4/52025

Detecting Malicious Software

Patrick Wardle

The detection-focused sequel to Volume 1, Wardle turns from analyzing Mac malware to building the heuristics and tooling that catch it, using macOS's security frameworks and real-world malware samples throughout.

Intermediate

4/52018

Malware Data Science

Attack Detection and Attribution

Joshua Saxe, Hillary Sanders

Saxe and Sanders apply machine-learning techniques (classification, clustering, deep learning) to malware detection and attribution, with working Python code and real corpora.

Read this if

Detection engineers and blue teamers who already have Volume 1's analysis grounding and now need to build detections, not just read samples. Shows how to program against macOS's own security-centric frameworks (Endpoint Security, ESF) rather than relying only on third-party AV.

Malware analysts and detection engineers who want to scale beyond manual triage. Saxe and Sanders apply classification, clustering, similarity analysis, and deep learning to the malware corpus, with working Python code throughout.

Skip this if

Readers who haven't worked through Volume 1's foundations first (Mach-O, code signing, persistence taxonomy); this book assumes that vocabulary and builds detection logic on top of it rather than re-teaching it.

Analysts whose work is one-sample-at-a-time, or readers without basic Python and statistics comfort. The book is for telemetry-rich environments where ML scales matter.

Key takeaways

Detection, not just analysis, is the book's whole focus — moving from "what is this sample doing" to "how do I catch the next one like it."
Builds heuristics directly on top of macOS's own security frameworks (Endpoint Security) instead of treating the OS as a black box.
Every technique is grounded in real malware families Wardle has tracked through Objective-See, not synthetic examples.

Static-feature classifiers can route a triage queue effectively even at scale; the book's chapters on feature engineering pay back the cost.
Similarity analysis (locality-sensitive hashing, ssdeep, imphash, function-level fuzzy hashing) is the analyst's lever for clustering campaigns and tracking actor evolution.
Deep learning is overhyped for malware in many contexts and exactly the right tool in others; the book is honest about the trade-offs in a way most ML/security books aren't.

How they compare

The Art of Mac Malware, Volume 2 and Malware Data Science are both rated 4/5 in our catalog. Pick by topic preference and reading style rather than by rating.

The Art of Mac Malware, Volume 2 is pitched at advanced level. Malware Data Science is pitched at intermediate level. Read the easier one first if you're not yet comfortable with the topic.

The Art of Mac Malware, Volume 2 and Malware Data Science both cover Malware, Detection, so reading them in sequence reinforces the same material from different angles.

Keep reading

The Art of Mac Malware, Volume 2

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