Da: Bellwetherbooks, McKeesport, PA, U.S.A.
paperback. Condizione: Very Good. 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.
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Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: As New. Unread book in perfect condition.
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: New.
paperback. Condizione: As New. New with remainder mark. Buy multiples from our store to save on shipping.
EUR 37,96
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Aggiungi al carrelloPaperback. Condizione: New. 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 analyse thousands of apps to detect malware at scale.
Condizione: New.
Da: Massive Bookshop, Greenfield, MA, U.S.A.
Paperback. Condizione: New.
EUR 47,08
Quantità: 2 disponibili
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 37,46
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EUR 46,07
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 40,18
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 44,52
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Da: Revaluation Books, Exeter, Regno Unito
EUR 44,66
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Aggiungi al carrelloPaperback. Condizione: Brand New. 280 pages. 9.25x7.00x1.02 inches. In Stock.
Lingua: Inglese
Editore: No Starch Press 2023-11-07, 2023
ISBN 10: 171850330X ISBN 13: 9781718503304
Da: Chiron Media, Wallingford, Regno Unito
EUR 41,86
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Aggiungi al carrelloPaperback. Condizione: New.
Condizione: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
EUR 39,08
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Aggiungi al carrelloPaperback. Condizione: New. 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 analyse thousands of apps to detect malware at scale.
EUR 36,35
Quantità: 1 disponibili
Aggiungi al carrelloCondizione: New. Qian Han, Research Scientist at Meta since 2021, received his PhD in Computer Science from Dartmouth College and his Bachelor&rsquos in Electronic Engineering from Tsinghua University, Beijing, China.Salvador Mandujano, Security Engin.
EUR 37,47
Quantità: Più di 20 disponibili
Aggiungi al carrelloPaperback. Condizione: New. 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 analyse thousands of apps to detect malware at scale.