malware analysis

Most books in this set focus on Android and Windows malware, with material on analyzing threats that target the Android operating system and on analyzing malicious Windows software.

Several books discuss machine-learning methods, ranging from ML techniques to detect malicious Android apps to applying machine learning and statistical analysis for anomaly detection in threat hunting.

At least one book frames malware development in an ethical context, presenting malware programming, evasion strategies, and practical examples intended for ethical hackers and red teamers.

They emphasize static and dynamic analysis of real malware specimens and also describe feature extraction and machine-learning techniques used to detect malicious apps.

Some titles offer tips for building a malware analysis lab and show how to construct test environments to run adversarial attacks; others provide downloadable datasets and scenario templates. Descriptions generally do not promise fully prescriptive, step-by-step lab procedures.

Books target a range of readers: software developers, architects, team leaders, and project managers; security, network, and systems professionals (often expecting some Python experience); and offensive roles such as penetration testers, exploit developers, ethical hackers, and red teamers.

They cover the Windows auditing subsystem and security logging and monitoring policy, and describe common event patterns such as user account authentication, Active Directory object modifications, and local security policy changes.