SKU: 90592583550

DeWalt DCF 961 H1T Akku Schlagschrauber 18 V 1632 Nm 1/2" Brushless + 1x Powerstack Akku 5,0 Ah + Ladegerät + TSTAK

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Description

DeWalt DCF 961 H1T Akku Schlagschrauber 18 V 1632 Nm 1/2" Brushless + 1x Powerstack Akku 5,0 Ah + Ladegerät + TSTAKLieferumfang: 1x DeWalt DCF 961 NT 1x DeWalt DCBP 518 18 V 5,0 Ah Powerstack Akku 1x Dewalt DCB XR Ladegert fr 10,8 18 V Li Ion Akkus 1x TSTAK Box Werkzeugkoffer Produktbeschreibung: Erfahren Sie mehr ber den DeWalt DCF 961 NT Akku Schlagschrauber! Diese Maschine der neuesten Generation bietet ein beeindruckendes Anzugsmoment von bis zu 1. 632 Nm und ein Lsemoment von bis zu 2. 373 Nm. Durch die innovative brstenlose Motor Technologie verfgt sie ber

Lieferumfang:

- 1x DeWalt DCF 961 NT
- 1x DeWalt DCBP 518 18 V 5,0 Ah Powerstack Akku
- 1x Dewalt DCB XR Ladegerät für 10,8 - 18 V Li-Ion Akkus
- 1x TSTAK Box Werkzeugkoffer

Produktbeschreibung:

Erfahren Sie mehr über den DeWalt DCF 961 NT Akku Schlagschrauber! Diese Maschine der neuesten Generation bietet ein beeindruckendes Anzugsmoment von bis zu 1.632 Nm und ein Lösemoment von bis zu 2.373 Nm. Durch die innovative bürstenlose Motor-Technologie verfügt sie über eine längere Laufzeit pro Akku-Ladung und eine höhere Lebensdauer. Mit einem elektronisch regulierbaren Gasgeberschalter können Dreh- und Schlagzahl sowie das Drehmoment in 3 Stufen präzise kontrolliert werden. Die zusätzliche elektronische Power-Stufe ermöglicht eine temporäre Reduzierung der Drehzahl bei gleichbleibendem Drehmoment für anspruchsvolle Anwendungen. Sicherheitselektronik sorgt für problemlosen Langzeiteinsatz, während das robuste Aluminium-Schlagwerkgehäuse die Lebensdauer des Gerätes erhöht. Trotz seiner Leistungsfähigkeit ist der Schlagschrauber kompakt und leicht, was eine problemlose Handhabung gewährleistet. Die leistungsstarke LED sorgt zudem für optimale Sichtverhältnisse am Arbeitsplatz.

Technische Daten:

Hersteller: DeWalt
Herstellerbezeichnung: DCF 961 NT
Spannung: 18 V
Leerlaufdrehzahl: 0 - 440 / 800 / 1200 min−1
Schlagzahl: 0 - 1890 min−1
Anzugsmoment:
Precision-Wrench Modus: 1496 Nm
Low Modus: 135 Nm
Medium Modus: 1220 Nm
High Modus: 1626 Nm
Max. Losbrechmoment: 2372 Nm
Gewicht (ohne Akku): 3,5 kg


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SKU: 90592583550

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4.4 ★★★★★
Based on 11 reviews
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Product Reviews
E
eyal Fishman
Fort Morgan, US
★★★★★ 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on September 10, 2025
A
Verified Purchase
Arthur Donkers
Fort Morgan, US
★★★★★ 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
Z
Zachi
Los Angeles, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on September 20, 2025
C
Verified Purchase
Cliente Amazon
Natrona Heights, US
★★★★★ 5
All OK.
Format: Paperback
All OK.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on August 23, 2024
E
Eugene
Whiting, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on October 14, 2024

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