Isbn: 9781718503304 - the android malware handbook: detection and analysis by human and machine (33 risultati)

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Paperback. Condizione: new. Paperback. 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.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 malwareExplore the static, dynamic, and complex features you can extract from malware for analysisMaster the machine learning algorithms useful for malware detectionSurvey the efficacy of machine learning techniques at detecting common Android malware categoriesThe 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. "Explores the history of Android attacks and covers static and dynamic approaches to analyzing real malware specimens, machine-learning techniques to detect malicious apps, and how to identify banking trojans, ransomware, and SMS fraud"-- Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. 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.…

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Paperback. Condizione: new. Paperback. 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.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 malwareExplore the static, dynamic, and complex features you can extract from malware for analysisMaster the machine learning algorithms useful for malware detectionSurvey the efficacy of machine learning techniques at detecting common Android malware categoriesThe 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. "Explores the history of Android attacks and covers static and dynamic approaches to analyzing real malware specimens, machine-learning techniques to detect malicious apps, and how to identify banking trojans, ransomware, and SMS fraud"-- Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. 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.…

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Condizione: 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.