Download Free Learning Android Malware Analysis Book in PDF and EPUB Free Download. You can read online Learning Android Malware Analysis and write the review.

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.
The authors develop a malware fingerprinting framework to cover accurate android malware detection and family attribution in this book. The authors emphasize the following: (1) the scalability over a large malware corpus; (2) the resiliency to common obfuscation techniques; (3) the portability over different platforms and architectures. First, the authors propose an approximate fingerprinting technique for android packaging that captures the underlying static structure of the android applications in the context of bulk and offline detection at the app-market level. This book proposes a malware clustering framework to perform malware clustering by building and partitioning the similarity network of malicious applications on top of this fingerprinting technique. Second, the authors propose an approximate fingerprinting technique that leverages dynamic analysis and natural language processing techniques to generate Android malware behavior reports. Based on this fingerprinting technique, the authors propose a portable malware detection framework employing machine learning classification. Third, the authors design an automatic framework to produce intelligence about the underlying malicious cyber-infrastructures of Android malware. The authors then leverage graph analysis techniques to generate relevant intelligence to identify the threat effects of malicious Internet activity associated with android malware. The authors elaborate on an effective android malware detection system, in the online detection context at the mobile device level. It is suitable for deployment on mobile devices, using machine learning classification on method call sequences. Also, it is resilient to common code obfuscation techniques and adaptive to operating systems and malware change overtime, using natural language processing and deep learning techniques. Researchers working in mobile and network security, machine learning and pattern recognition will find this book useful as a reference. Advanced-level students studying computer science within these topic areas will purchase this book as well.
Learn the tools and techniques needed to detect and dissect malicious Android apps.
The rapid growth and development of Android-based devices has resulted in a wealth of sensitive information on mobile devices that offer minimal malware protection. This has created an immediate need for security professionals that understand how to best approach the subject of Android malware threats and analysis. In Android Malware and Analysis, Ken Dunham, renowned global malware expert and author, teams up with international experts to document the best tools and tactics available for analyzing Android malware. The book covers both methods of malware analysis: dynamic and static. This tactical and practical book shows you how to use to use dynamic malware analysis to check the behavior of an application/malware as it has been executed in the system. It also describes how you can apply static analysis to break apart the application/malware using reverse engineering tools and techniques to recreate the actual code and algorithms used. The book presents the insights of experts in the field, who have already sized up the best tools, tactics, and procedures for recognizing and analyzing Android malware threats quickly and effectively. You also get access to an online library of tools that supplies what you will need to begin your own analysis of Android malware threats. Tools available on the book’s site include updated information, tutorials, code, scripts, and author assistance. This is not a book on Android OS, fuzz testing, or social engineering. Instead, it is about the best ways to analyze and tear apart Android malware threats. After reading the book, you will be able to immediately implement the tools and tactics covered to identify and analyze the latest evolution of Android threats. Updated information, tutorials, a private forum, code, scripts, tools, and author assistance are available at AndroidRisk.com for first-time owners of the book.
Improvement in technology has inevitably altered the tactic of criminals to thievery. In recent times, information is the real commodity and it is thus subject to theft as any other possessions: cryptocurrency, credit card numbers, and illegal digital material are on the top. If globally available platforms for smartphones are considered, the Android open source platform (AOSP) emerges as a prevailing contributor to the market and its popularity continues to intensify. Whilst it is beneficiary for users, this development simultaneously makes a prolific environment for exploitation by immoral developers who create malware or reuse software illegitimately acquired by reverse engineering. Android malware analysis techniques are broadly categorized into static and dynamic analysis. Many researchers have also used feature-based learning to build and sustain working security solutions. Although Android has its base set of permissions in place to protect the device and resources, it does not provide strong enough security framework to defend against attacks. This thesis presents several contributions in the domain of security of Android applications and the data within these applications. First, a brief survey of threats, vulnerability and security analysis tools for the AOSP is presented. Second, we develop and use a genre extraction algorithm for Android applications to check the availability of those applications in Google Play Store. Third, an algorithm for extracting unclaimed permissions is proposed which will give a set of unnecessary permissions for applications under examination. Finally, machine learning aided approaches for analysis of Android malware were adopted. Features including permissions, APIs, content providers, broadcast receivers, and services are extracted from benign (~2,000) and malware (5,560) applications and examined for evaluation. We create feature vector combinations using these features and feed these vectors to various classifiers. Based on the evaluation metrics of classifiers, we scrutinize classifier performance with respect to specific feature combination. Classifiers such as SVM, Logistic Regression and Random Forests spectacle a good performance whilst the dataset of combination of permissions and APIs records the maximum accuracy for Logistic Regression.
In response to the exponential growth of mobile device use, malicious apps have increased. Yet the industry is lacking professionals capable of identifying and combating these threats. Adding malware analysis to your skill set can help set you apart to employers and clients-and help you keep your users and organization safe. Security intelligence engineer Kristina Balaam introduces the basic tools and techniques needed to detect and dissect malicious Android apps. Learn how to set up your analysis lab, with tools like APKTool, Dex2Jar, and JD-Project, and find malicious apps to deconstruct. Kristina shows how to search the codebase for indicators of malicious activity, and provides a challenge and solution set that allows you to practice your new skills.
The authors develop a malware fingerprinting framework to cover accurate android malware detection and family attribution in this book. The authors emphasize the following: (1) the scalability over a large malware corpus; (2) the resiliency to common obfuscation techniques; (3) the portability over different platforms and architectures. First, the authors propose an approximate fingerprinting technique for android packaging that captures the underlying static structure of the android applications in the context of bulk and offline detection at the app-market level. This book proposes a malware clustering framework to perform malware clustering by building and partitioning the similarity network of malicious applications on top of this fingerprinting technique. Second, the authors propose an approximate fingerprinting technique that leverages dynamic analysis and natural language processing techniques to generate Android malware behavior reports. Based on this fingerprinting technique, the authors propose a portable malware detection framework employing machine learning classification. Third, the authors design an automatic framework to produce intelligence about the underlying malicious cyber-infrastructures of Android malware. The authors then leverage graph analysis techniques to generate relevant intelligence to identify the threat effects of malicious Internet activity associated with android malware. The authors elaborate on an effective android malware detection system, in the online detection context at the mobile device level. It is suitable for deployment on mobile devices, using machine learning classification on method call sequences. Also, it is resilient to common code obfuscation techniques and adaptive to operating systems and malware change overtime, using natural language processing and deep learning techniques. Researchers working in mobile and network security, machine learning and pattern recognition will find this book useful as a reference. Advanced-level students studying computer science within these topic areas will purchase this book as well.