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Description
Fabtech 07-15 GM 1500 4in Front Dirt Logic 2.5 N/R Coilovers - PairFrom our desert racing experience the Dirt Logic 2. 5 has been developed for exceptional performance in a variety of Coilover applications. The Dirt Logic 2. 5 will provide incredible dampening capacity for aggressive offroad driving. This Part Fits: Year Make Model Submodel 2013 Chevrolet Avalanche Black Diamond LS 2013 Chevrolet Avalanche Black Diamond LT 2013 Chevrolet Avalanche Black Diamond LTZ 2007 2012 Chevrolet Avalanche LS 2007 2012 Chevrolet
From our desert racing experience the Dirt Logic 2.5 has been developed for exceptional performance in a variety of Coilover applications. The Dirt Logic 2.5 will provide incredible dampening capacity for aggressive offroad driving.This Part Fits:
| Year | Make | Model | Submodel |
|---|---|---|---|
| 2013 | Chevrolet | Avalanche | Black Diamond LS |
| 2013 | Chevrolet | Avalanche | Black Diamond LT |
| 2013 | Chevrolet | Avalanche | Black Diamond LTZ |
| 2007-2012 | Chevrolet | Avalanche | LS |
| 2007-2012 | Chevrolet | Avalanche | LT |
| 2007-2012 | Chevrolet | Avalanche | LTZ |
| 2016-2017 | Chevrolet | Silverado 1500 | Custom |
| 2014-2017 | Chevrolet | Silverado 1500 | High Country |
| 2009-2013 | Chevrolet | Silverado 1500 | Hybrid |
| 2008-2013,2015-2017 | Chevrolet | Silverado 1500 | LS |
| 2007-2017 | Chevrolet | Silverado 1500 | LT |
| 2007-2017 | Chevrolet | Silverado 1500 | LTZ |
| 2015-2017 | Chevrolet | Silverado 1500 | SSV |
| 2007-2017 | Chevrolet | Silverado 1500 | WT |
| 2010-2013 | Chevrolet | Silverado 1500 | XFE |
| 2007-2014 | Chevrolet | Suburban 1500 | LS |
| 2007-2014 | Chevrolet | Suburban 1500 | LT |
| 2007-2014 | Chevrolet | Suburban 1500 | LTZ |
| 2008-2013 | Chevrolet | Tahoe | Hybrid |
| 2007-2014 | Chevrolet | Tahoe | LS |
| 2007-2014 | Chevrolet | Tahoe | LT |
| 2007-2014 | Chevrolet | Tahoe | LTZ |
| 2012-2014 | Chevrolet | Tahoe | PPV |
| 2012-2014 | Chevrolet | Tahoe | SSV |
| 2014-2017 | GMC | Sierra 1500 | Base |
| 2007-2017 | GMC | Sierra 1500 | Denali |
| 2009-2013 | GMC | Sierra 1500 | Hybrid |
| 2008-2013 | GMC | Sierra 1500 | SL |
| 2007-2017 | GMC | Sierra 1500 | SLE |
| 2007-2017 | GMC | Sierra 1500 | SLT |
| 2007-2013 | GMC | Sierra 1500 | WT |
| 2010-2013 | GMC | Sierra 1500 | XFE |
| 2007-2014 | GMC | Yukon | Denali |
| 2010-2013 | GMC | Yukon | Denali Hybrid |
| 2008-2013 | GMC | Yukon | Hybrid |
| 2007-2014 | GMC | Yukon | SLE |
| 2007-2014 | GMC | Yukon | SLT |
| 2007-2014 | GMC | Yukon XL 1500 | Denali |
| 2007-2014 | GMC | Yukon XL 1500 | SLE |
| 2007-2014 | GMC | Yukon XL 1500 | SLT |
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4.8 ★★★★★
Based on 721 reviews
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Product Reviews
★★★★★ 5
outstanding and highly informative book
Format: Paperback
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
★★★★★ 4
good
Format: Paperback
Good practical content, quality of images could be improved
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on October 13, 2025
★★★★★ 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
★★★★★ 5
All OK.
Format: Paperback
All OK.
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Reviewed in the United States on August 23, 2024
★★★★★ 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