Android Malware Detection And Adversarial Methods
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Android Malware Detection And Adversarial Methods
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Author : Weina Niu
language : en
Publisher: Springer Nature
Release Date : 2024-05-23
Android Malware Detection And Adversarial Methods written by Weina Niu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-23 with Computers categories.
The rise of Android malware poses a significant threat to users’ information security and privacy. Malicious software can inflict severe harm on users by employing various tactics, including deception, personal information theft, and device control. To address this issue, both academia and industry are continually engaged in research and development efforts focused on detecting and countering Android malware. This book is a comprehensive academic monograph crafted against this backdrop. The publication meticulously explores the background, methods, adversarial approaches, and future trends related to Android malware. It is organized into four parts: the overview of Android malware detection, the general Android malware detection method, the adversarial method for Android malware detection, and the future trends of Android malware detection. Within these sections, the book elucidates associated issues, principles, and highlights notable research. By engaging with this book, readers will gain not only a global perspective on Android malware detection and adversarial methods but also a detailed understanding of the taxonomy and general methods outlined in each part. The publication illustrates both the overarching model and representative academic work, facilitating a profound comprehension of Android malware detection.
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.
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.
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.
A Comparative Study Of Machine Learning Methods For Android Malware Detection
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Author : Fairuz Amalina Narudin
language : en
Publisher:
Release Date : 2015
A Comparative Study Of Machine Learning Methods For Android Malware Detection written by Fairuz Amalina Narudin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Androids categories.
An Analysis Of Android Malware Detection Using Tree Learning Techniques
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Author : Kyler D. Dickey
language : en
Publisher:
Release Date : 2022
An Analysis Of Android Malware Detection Using Tree Learning Techniques written by Kyler D. Dickey and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Android (Electronic resource) categories.
Android malware is a growing threat, coinciding with the increasing adoption of the Android platform. Malware detection methods used to maintain user privacy and system integrity are increasingly becoming the subject of research. Many new methods studied employ learning algorithms to detect malicious programs. This study investigates the use of byte and opcode frequency features as inputs for tree-based machine learning methods. The algorithm is optimized to reduce overfitting given input hyperparameter combinations and is tuned using cross-validation procedures. Lastly, the study deliberates on possible avenues for future research to gather more concrete evidence for the efficacy and cost-effectiveness of such a system in a productive environment, emphasizing the need for more strenuous testing processes.
Malware
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Author : Dimitris Gritzalis
language : en
Publisher: Springer Nature
Release Date : 2024-11-14
Malware written by Dimitris Gritzalis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-14 with Computers categories.
This book provides a holistic overview of current state of the art and practice in malware research as well as the challenges of malware research from multiple angles. It also provides step-by-step guides in various practical problems, such as unpacking real-world malware and dissecting it to collect and perform a forensic analysis. Similarly, it includes a guide on how to apply state-of-the-art Machine Learning methods to classify malware. Acknowledging that the latter is a serious trend in malware, one part of the book is devoted to providing the reader with the state-of-the-art in Machine Learning methods in malware classification, highlighting the different approaches that are used for, e.g., mobile malware samples and introducing the reader to the challenges that are faced when shifting from a lab to production environment. Modern malware is fueling a worldwide underground economy. The research for this book is backed by theoretical models that simulate how malware propagates and how the spread could be mitigated. The necessary mathematical foundations and probabilistic theoretical models are introduced, and practical results are demonstrated to showcase the efficacy of such models in detecting and countering malware. It presents an outline of the methods that malware authors use to evade detection. This book also provides a thorough overview of the ecosystem, its dynamics and the geopolitical implications are introduced. The latter are complemented by a legal perspective from the African legislative efforts, to allow the reader to understand the human and social impact of malware. This book is designed mainly for researchers and advanced-level computer science students trying to understand the current landscape in malware, as well as applying artificial intelligence and machine learning in malware detection and classification. Professionals who are searching for a perspective to streamline the challenges that arise, when bringing lab solutions into a production environment, and how to timely identify ransomware signals at scale will also want to purchase this book. Beyond data protection experts, who would like to understand how malware siphons private information, experts from law enforcement authorities and the judiciary system, who want to keep up with the recent developments will find this book valuable as well.
Anti Evasion Techniques For Static And Dynamic Android Malware Detection
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Author : 陳允中
language : en
Publisher:
Release Date : 2025
Anti Evasion Techniques For Static And Dynamic Android Malware Detection written by 陳允中 and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025 with categories.
Deployable Machine Learning For Security Defense
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Author : Gang Wang
language : en
Publisher: Springer Nature
Release Date : 2021-09-24
Deployable Machine Learning For Security Defense written by Gang Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-24 with Computers categories.
This book constitutes selected and extended papers from the Second International Workshop on Deployable Machine Learning for Security Defense, MLHat 2021, held in August 2021. Due to the COVID-19 pandemic the conference was held online. The 6 full papers were thoroughly reviewed and selected from 7 qualified submissions. The papers are organized in topical sections on machine learning for security, and malware attack and defense.
Security And Artificial Intelligence
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Author : Lejla Batina
language : en
Publisher: Springer Nature
Release Date : 2022-04-07
Security And Artificial Intelligence written by Lejla Batina and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-07 with Computers categories.
AI has become an emerging technology to assess security and privacy, with many challenges and potential solutions at the algorithm, architecture, and implementation levels. So far, research on AI and security has looked at subproblems in isolation but future solutions will require sharing of experience and best practice in these domains. The editors of this State-of-the-Art Survey invited a cross-disciplinary team of researchers to a Lorentz workshop in 2019 to improve collaboration in these areas. Some contributions were initiated at the event, others were developed since through further invitations, editing, and cross-reviewing. This contributed book contains 14 invited chapters that address side-channel attacks and fault injection, cryptographic primitives, adversarial machine learning, and intrusion detection. The chapters were evaluated based on their significance, technical quality, and relevance to the topics of security and AI, and each submission was reviewed in single-blind mode and revised.