Android Malware Detection Using Machine Learning
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Android Malware Detection Using Machine Learning
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Author : ElMouatez Billah Karbab
language : en
Publisher: Springer Nature
Release Date : 2021-07-10
Android Malware Detection Using Machine Learning written by ElMouatez Billah Karbab and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-10 with Computers categories.
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.
Android Malware Detection Using Static Analysis Machine Learning And Deep Learning
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Author : Fawad Ahmad
language : en
Publisher:
Release Date : 2022
Android Malware Detection Using Static Analysis Machine Learning And Deep Learning written by Fawad Ahmad and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Android Malware Detection Through Permission And App Component Analysis Using Machine Learning Algorithms
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Author : Keyur Milind Kulkarni
language : en
Publisher:
Release Date : 2018
Android Malware Detection Through Permission And App Component Analysis Using Machine Learning Algorithms written by Keyur Milind Kulkarni and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Android (Electronic resource) categories.
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.
Malware Analysis Using Artificial Intelligence And Deep Learning
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Author : Mark Stamp
language : en
Publisher: Springer Nature
Release Date : 2020-12-20
Malware Analysis Using Artificial Intelligence And Deep Learning written by Mark Stamp and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-20 with Computers categories.
This book is focused on the use of deep learning (DL) and artificial intelligence (AI) as tools to advance the fields of malware detection and analysis. The individual chapters of the book deal with a wide variety of state-of-the-art AI and DL techniques, which are applied to a number of challenging malware-related problems. DL and AI based approaches to malware detection and analysis are largely data driven and hence minimal expert domain knowledge of malware is needed. This book fills a gap between the emerging fields of DL/AI and malware analysis. It covers a broad range of modern and practical DL and AI techniques, including frameworks and development tools enabling the audience to innovate with cutting-edge research advancements in a multitude of malware (and closely related) use cases.
The Android Malware Handbook
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Author : Qian Han
language : en
Publisher: No Starch Press
Release Date : 2023-11-07
The Android Malware Handbook written by Qian Han and has been published by No Starch Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-07 with Computers categories.
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.
Android Malware Detection Using Machine Learning To Mitigate Adversarial Evasion Attacks
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Author : Husnain Rafiq
language : en
Publisher:
Release Date : 2022
Android Malware Detection Using Machine Learning To Mitigate Adversarial Evasion Attacks written by Husnain Rafiq and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Proceedings Of International Conference On Network Security And Blockchain Technology
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Author : Debasis Giri
language : en
Publisher: Springer Nature
Release Date : 2022-06-14
Proceedings Of International Conference On Network Security And Blockchain Technology written by Debasis Giri and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-14 with Technology & Engineering categories.
The book is a collection of best selected research papers presented at International Conference on Network Security and Blockchain Technology (ICNSBT 2021), organized by Computer Society of India—Kolkata Chapter, India, during December 2–4, 2021. The book discusses recent developments and contemporary research in cryptography, network security, cyber security, and blockchain technology. Authors are eminent academicians, scientists, researchers, and scholars in their respective fields from across the world.
Artificial Intelligence And Blockchain For Future Cybersecurity Applications
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Author : Yassine Maleh
language : en
Publisher: Springer Nature
Release Date : 2021-04-30
Artificial Intelligence And Blockchain For Future Cybersecurity Applications written by Yassine Maleh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-30 with Computers categories.
This book presents state-of-the-art research on artificial intelligence and blockchain for future cybersecurity applications. The accepted book chapters covered many themes, including artificial intelligence and blockchain challenges, models and applications, cyber threats and intrusions analysis and detection, and many other applications for smart cyber ecosystems. It aspires to provide a relevant reference for students, researchers, engineers, and professionals working in this particular area or those interested in grasping its diverse facets and exploring the latest advances on artificial intelligence and blockchain for future cybersecurity applications.
Applied Learning Algorithms For Intelligent Iot
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Author : Pethuru Raj Chelliah
language : en
Publisher: CRC Press
Release Date : 2021-10-28
Applied Learning Algorithms For Intelligent Iot written by Pethuru Raj Chelliah and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-28 with Computers categories.
This book vividly illustrates all the promising and potential machine learning (ML) and deep learning (DL) algorithms through a host of real-world and real-time business use cases. Machines and devices can be empowered to self-learn and exhibit intelligent behavior. Also, Big Data combined with real-time and runtime data can lead to personalized, prognostic, predictive, and prescriptive insights. This book examines the following topics: Cognitive machines and devices Cyber physical systems (CPS) The Internet of Things (IoT) and industrial use cases Industry 4.0 for smarter manufacturing Predictive and prescriptive insights for smarter systems Machine vision and intelligence Natural interfaces K-means clustering algorithm Support vector machine (SVM) algorithm A priori algorithms Linear and logistic regression Applied Learning Algorithms for Intelligent IoT clearly articulates ML and DL algorithms that can be used to unearth predictive and prescriptive insights out of Big Data. Transforming raw data into information and relevant knowledge is gaining prominence with the availability of data processing and mining, analytics algorithms, platforms, frameworks, and other accelerators discussed in the book. Now, with the emergence of machine learning algorithms, the field of data analytics is bound to reach new heights. This book will serve as a comprehensive guide for AI researchers, faculty members, and IT professionals. Every chapter will discuss one ML algorithm, its origin, challenges, and benefits, as well as a sample industry use case for explaining the algorithm in detail. The book’s detailed and deeper dive into ML and DL algorithms using a practical use case can foster innovative research.
Advances In Computational Intelligence
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Author : K. Venu Gopal Rao
language : en
Publisher: Springer Nature
Release Date : 2024-08-24
Advances In Computational Intelligence written by K. Venu Gopal Rao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-24 with Computers categories.
This book constitutes the refereed proceedings of the First International Conference on Advances in Computational Intelligence, ICACI 2023, held in Hyderabad, India, during December 15–16, 2023. The 7 full papers and 2 short papers included in this book were carefully reviewed and selected from 234 submissions. These papers focus on the diverse applications of Data engineering in various areas such as Data Mining, Artificial Intelligence, Natural Language Processing, Pattern Recognition, and Machine Learning.