WSM Fuel Filter Honda 115 / 130 Hp 99-02 - 18-7785, 16010-ST5-992, 16010-ST5-993

WSM Fuel Filter Honda 115 / 130 Hp 99-02 - 18-7785, 16010-ST5-992, 16010-ST5-993

Marsoni

M251S

$44.97

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WSM Fuel Filter Honda 115 / 130 Hp 99-02 - 18-7785, 16010-ST5-992, 16010-ST5-993Item#: 18 7785 Fuel Filter Honda 115 130 Hp 99 02 OEM#: 16010 ST5 992, 16010 ST5 993, 16010 ST5 E01, 16010 ST5 E02 Brand: Sierra FITMENTS: Make Model Year(s) Honda ALL Honda 4 Cyl 3. 386 Bore BF 115 1999 2002 Honda 4 Cyl 3. 386 Bore BF 130 1999 2002

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MAoS - Malware Analysis on Steroids: Fighting Malware to the Death - Real-World Threats and Reverse Engineering Tactics by Uriel Kosayev is an outstanding and highly informative book. This is the second book I’ve read from him, after Antivirus Bypass Techniques, and once again I found the content to be extremely practical, well-structured, and insightful. Uriel has a way of explaining complex malware analysis and reverse engineering concepts in a way that is both approachable and deeply technical. The real-world tactics and examples make it not just a book you read once, but a reference you’ll return to often. Without a doubt, another excellent resource—I’m giving it 5 stars.

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Reviewed in the United States on September 10, 2025

Verified Purchase

Arthur Donkers

Los Angeles, US

★★★★★ 4

good

Format: Paperback

Good practical content, quality of images could be improved

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Reviewed in the United States on October 13, 2025

Zachi

Birmingham, US

★★★★★ 5

Essential for Malware Hunters

Format: Hardcover

MAoS - Malware Analysis on Steroids is a clear, practical, and engaging guide to real-world malware threats. It blends hands-on reverse engineering tactics with modern case studies, making it both highly technical and easy to follow. Whether you’re new to malware analysis or a seasoned security pro, this book sharpens your skills and mindset for the fight against today’s threats.

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Reviewed in the United States on September 20, 2025

Verified Purchase

Cliente Amazon

San Leandro, US

★★★★★ 5

All OK.

Format: Paperback

All OK.

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Reviewed in the United States on August 23, 2024

Eugene

Los Angeles, US

★★★★★ 5

Great book on Android malware reverse engineering and detection!

Format: Paperback

This book offers a holistic approach for learning how to deal with malware targeting Android platform. I find this book to be equally interesting to read for both people with little experience in the domain of malware analysis as quick-start guide as well as for experienced reverse engineers who would want to translate their knowledge from malware analysis in other platforms to Android. This book is definitely worth the time reading it. The first half of the book is a great source of information on reverse engineering Android malware. Chapters on categories of Android Malware and examples of the malware in the wild set up the necessary context for the readers and help to better understand why Android malware focuses on certain infection/monetization techniques. It also bring context in the evolution of mitigation and hardening in Android platform. Chapters on static and dynamic analysis are very practical and engaging to read which I probably enjoyed the most in the book. They teach readers how to do reverse engineering of Android malware -- covers tooling, methodologies, hints to look for in the code or binary artifacts when doing reverse engineering. The second half of the book is dedicated to using machine learning techniques for detecting malware. It starts with a concise yet self-contained necessary theoretical background. In my point of view the authors hit a very good balance for making material accurate and comprehensive yet easy to read and follow along for people without extensive background in the field (which is not a trivial thing for math texts). As it is probably impossible to fit all the necessary theoretical material in a single (or even couple of chapters) they text offers references to sources which cover certain topics in-depth -- for those readers who would like to dive deeper in machine learning. Finally, the book wraps with application of the discussed machine learning techniques to certain malware families.

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Reviewed in the United States on October 14, 2024