“Follow in the footsteps of professionals with a record of discovering advanced malware.”
— Rodrigo Rubira Branco
Rootkits and Bootkits will teach you how to understand and counter sophisticated, advanced threats buried deep in a machine’s boot process or UEFI firmware.
With the aid of numerous case studies and professional research from three of the world’s leading security experts, you’ll trace malware development over time from rootkits like TDL3 to present-day UEFI implants and examine how they infect a system, persist through reboot, and evade security software. As you inspect and dissect real malware, you’ll learn:
How Windows boots—including 32-bit, 64-bit, and UEFI mode—and where to find vulnerabilities
The details of boot process security mechanisms like Secure Boot, including an overview of Virtual Secure Mode (VSM) and Device Guard
Reverse engineering and forensic techniques for analyzing real malware, including bootkits like Rovnix/Carberp, Gapz, TDL4, and the infamous rootkits TDL3 and Festi
How to perform static and dynamic analysis using emulation and tools like Bochs and IDA Pro
How to better understand the delivery stage of threats against BIOS and UEFI firmware in order to create detection capabilities
How to use virtualization tools like VMware Workstation to reverse engineer bootkits and the Intel Chipsec tool to dig into forensic analysis
Cybercrime syndicates and malicious actors will continue to write ever more persistent and covert attacks, but the game is not lost. Explore the cutting edge of malware analysis with Rootkits and Bootkits.
About the Authors
ALEX MATROSOV is an Offensive Security Research Lead at NVIDIA with over 20 years of experience in reverse engineering, advanced malware analysis, firmware security, and exploitation techniques. EUGENE RODIONOV, PhD, is a Security Researcher at Intel working in BIOS security for Client Platforms. SERGEY BRATUS is a Research Associate Professor in the Computer Science Department at Dartmouth College. He has previously worked at BBN Technologies on natural language processing research.
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