Tetali Sai Deep (21 results)

Paperback. Condition: Very Good. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

paperback. Condition: New. New with remainder mark. Buy multiples from our store to save on shipping.

paperback. Condition: As New. Book is in very fine condition. Spine is strong and inside pages are clean. Near new.

Condition: As New. Unread book in perfect condition.

paperback. Condition: As New. New with remainder mark. Buy multiples from our store to save on shipping.

Condition: New.

Paperback. Condition: 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.…

Paperback. Condition: New. Brand new book, sourced directly from publisher. Dispatch time is 9-10 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Paperback. Condition: New.

Paperback. Condition: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Condition: New.

Condition: New.

Condition: New.

Condition: New.

Paperback. Condition: Brand New. 280 pages. 9.25x7.00x1.02 inches. In Stock.

Condition: New. In English.

Condition: 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.

Condition: As New. Unread book in perfect condition.

Paperback. Condition: 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.…

Condition: 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.

Paperback. Condition: 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.…