Description

This comprehensive guide to Android malware introduces current threats facing the world's most widely used operating system. After exploring the history of attacks seen in the wild since the time Android first launched, including several malware families previously absent from the literature, you'll practice static and dynamic approaches to analysing real malware specimens. Next, you'll examine the machine-learning techniques used to detect malicious apps, the types of classification models that defenders can use, and the various features of malware specimens that can become input to these models. You'll then adapt these machine-learning strategies to the identification of malware categories like banking trojans, ransomware, and SMS fraud. You'll learn: How historical Android malware can elevate your understanding of current threats; How to manually identify and analyse current Android malware using static and dynamic reverse-engineering tools; How machine-learning algorithms can anal

The Android Malware Handbook: Using Manual Analysis and ML-Based Detection

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RRP: £47.99 You save £4.80 (10%)
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Paperback / softback by Qian Han , Sai Deep Tetali

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Short Description:

This comprehensive guide to Android malware introduces current threats facing the world's most widely used operating system. After exploring the... Read more

    Publisher: No Starch Press,US
    Publication Date: 07/11/2023
    ISBN13: 9781718503304, 978-1718503304
    ISBN10: 171850330X

    Number of Pages: 328

    Non Fiction , Computing

    Description

    This comprehensive guide to Android malware introduces current threats facing the world's most widely used operating system. After exploring the history of attacks seen in the wild since the time Android first launched, including several malware families previously absent from the literature, you'll practice static and dynamic approaches to analysing real malware specimens. Next, you'll examine the machine-learning techniques used to detect malicious apps, the types of classification models that defenders can use, and the various features of malware specimens that can become input to these models. You'll then adapt these machine-learning strategies to the identification of malware categories like banking trojans, ransomware, and SMS fraud. You'll learn: How historical Android malware can elevate your understanding of current threats; How to manually identify and analyse current Android malware using static and dynamic reverse-engineering tools; How machine-learning algorithms can anal

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