BIBLIO is the largest independent book marketplace in the world, with over 100 million books.

Skip to content

The Android Malware Handbook: Detection and Analysis by Human and Machine

The Android Malware Handbook: Detection and Analysis by Human and Machine

The Android Malware Handbook: Detection and Analysis by Human and Machine
Stock photo: cover may vary

The Android Malware Handbook: Detection and Analysis by Human and Machine Paperback - 2023

by Han, Qian,Mandujano, Salvador,Porst, Sebastian,Subrahmanian, V.S.,Tetali, Sai Deep,Xiong, Yanhai

Add to wish list
  • Used
  • very good
  • Paperback
Used - Very good

Description

No Starch Press, 11/7/2023 12:00:01 A. paperback. Very Good. 0.8661 in x 9.1732 in x 7.0079 in. Very Good Condition - May show some limited signs of wear and may have a remainder mark. Pages and dust cover are intact and not marred by notes or highlighting.
Ask the seller a question Add to wish list
On sale A$21.80 (was A$24.22 )
Free Delivery within USA
Standard delivery: 2 to 7 days
More delivery options
Ships from Spellbound (Pennsylvania, United States)
The BIBLIO Guarantee

On sale

More books like this are on offer from Spellbound at 10% off.

Shop now

Details

  • Title The Android Malware Handbook: Detection and Analysis by Human and Machine
  • Author Han, Qian,Mandujano, Salvador,Porst, Sebastian,Subrahmanian, V.S.,Tetali, Sai Deep,Xiong, Yanhai
  • Binding Paperback
  • Condition Used - Very good
  • Pages 328
  • Volumes 1
  • Language ENG
  • Publisher No Starch Press
  • Publication date 11/7/2023 12:00:01 A
  • Illustrated Yes
  • Features Illustrated, Index
  • Bookseller's Inventory # mon0000010117
  • ISBN 9781718503304 / 171850330X
  • Weight 1.4 lbs (0.64 kg)
  • Dimensions 9.2 x 6.9 x 0.9 in (23.37 x 17.53 x 2.29 cm)
  • Size 0.8661 in x 9.1732 in x 7.0079 i
  • Category Computers - General Information
  • Library of Congress subjects Machine learning, Mobile computing - Security measures
  • Library of Congress Catalogue Number 2023024979
  • Dewey Decimal Code 005.88
  • Quantity available 1

About Spellbound Pennsylvania, United States

Biblio member since 2012

We are an internet retailer in business since 2004. Our catalog currently contains more than 40,000 different titles. We ship internationally and have sold items to 60+ countries within the past year.

Terms of Sale:

30 day return guarantee, with full refund including shipping costs for up to 30 days after delivery if an item arrives misdescribed or damaged.

Browse books from Spellbound

Reader reviews for The Android Malware Handbook: Detection and Analysis by Human and Machine

Write a review for this book

There are no reviews for this book yet. Be the first to write a review!

Your review will be anonymous unless you login or create an account first.

Overall rating
Remember: You’re rating the book as a work, not the seller or the condition of the specific copy you purchased!

Important Terms and Guidelines

  • Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
  • Reviews must be at least 140 characters in length.
  • Please do not reveal critical plot elements.
  • This is not a help line. Contact customer support if you need help.
Your review must not include:
  • Obscenities, discriminatory language, or other insulting language not suitable for public domain
  • Advertisements, “spam” content, or references to other products, offers or websites.
  • Email addresses, URLs, phone numbers, physical addresses or other contact information.
  • Overly critical comments about other reviews or reviewers
  • Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
  • Availability, price, or alternative ordering/shipping information

From the publisher

Written by machine-learning researchers and members of the Android Security team, this all-star guide tackles the analysis and detection of malware that targets the Android operating system.

This groundbreaking guide to Android malware distills years of research by machine learning experts in academia and members of Meta and Google's Android Security teams into a comprehensive introduction to detecting common threats facing the Android eco-system today.

Explore the history of Android malware in the wild since the operating system first launched and then practice static and dynamic approaches to analyzing real malware specimens. Next, examine machine learning techniques that can be used to detect malicious apps, the types of classification models that defenders can implement to achieve these detections, and the various malware features that can be used as input to these models. Adapt these machine learning strategies to the identifica-tion of malware categories like banking trojans, ransomware, and SMS fraud.

You'll:

  • Dive deep into the source code of real malware
  • Explore the static, dynamic, and complex features you can extract from malware for analysis
  • Master the machine learning algorithms useful for malware detection
  • Survey the efficacy of machine learning techniques at detecting common Android malware categories

The Android Malware Handbook's team of expert authors will guide you through the Android threat landscape and prepare you for the next wave of malware to come.

About the author

Qian Han, Research Scientist at Meta since 2021, received his PhD in Computer Science from Dartmouth College and his Bachelor's in Electronic Engineering from Tsinghua University, Beijing, China.

Salvador Mandujano, Security Engineering Manager at Google, has led product security engineering, malware reverse engineering and payments security teams. Before Google, he held senior security research and architecture positions at Intel and Nvidia. He has a PhD in Artificial Intelligence from Tecnolgico de Monterrey, an MSc in Computer Science from Purdue, an MBA from The University of Texas, and a BSc in Computer Engineering from Universidad Nacional Autnoma de Mxico.

Sebastian Porst is manager of Google's Android Application Security Research team, which tries to predict or research novel attacks on Android devices and Android users by malware or through app vulnerabilities. He has an MSc Masters from Trier University of Applied Sciences, Germany in 2007.

V.S. Subrahmanian is the Walter P. Murphy Professor of Computer Science and Buffet Faculty Fellow in the Buffet Institute of Global Affairs at Northwestern University. Prof. Subrahmanian is one of the world's foremost experts at the intersection of AI and security issues. He has written eight books, edited ten, and published over 300 refereed articles.

Sai Deep Tetali, Principal Engineer and Tech Lead Manager at Meta, works on privacy solutions for augmented and virtual reality applications. He spent 5 years at Google developing machine learning techniques to detect Android malware and has a PhD from University of California Los Angeles.

Yanhai Xiong is currently an Assistant Professor in the Department of Computer Science and Engineering at the University of Louisville. She has a PhD from Nanyang Technological University focusing on applying AI techniques to improve the efficiency of electric vehicle infrastructure and a BS in Engineering from the University of Science and Technology of China.

Please wait a moment while we load the requested page.

tracking-