Malware Detection
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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.
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.
Malware Analysis And Detection Engineering
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Author : Abhijit Mohanta
language : en
Publisher: Apress
Release Date : 2020-11-05
Malware Analysis And Detection Engineering written by Abhijit Mohanta and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-05 with Computers categories.
Discover how the internals of malware work and how you can analyze and detect it. You will learn not only how to analyze and reverse malware, but also how to classify and categorize it, giving you insight into the intent of the malware. Malware Analysis and Detection Engineering is a one-stop guide to malware analysis that simplifies the topic by teaching you undocumented tricks used by analysts in the industry. You will be able to extend your expertise to analyze and reverse the challenges that malicious software throws at you. The book starts with an introduction to malware analysis and reverse engineering to provide insight on the different types of malware and also the terminology used in the anti-malware industry. You will know how to set up an isolated lab environment to safely execute and analyze malware. You will learn about malware packing, code injection, and process hollowing plus how to analyze, reverse, classify, and categorize malware using static and dynamic tools. You will be able to automate your malware analysis process by exploring detection tools to modify and trace malware programs, including sandboxes, IDS/IPS, anti-virus, and Windows binary instrumentation. The book provides comprehensive content in combination with hands-on exercises to help you dig into the details of malware dissection, giving you the confidence to tackle malware that enters your environment. What You Will Learn Analyze, dissect, reverse engineer, and classify malware Effectively handle malware with custom packers and compilers Unpack complex malware to locate vital malware components and decipher their intent Use various static and dynamic malware analysis tools Leverage the internals of various detection engineering tools to improve your workflow Write Snort rules and learn to use them with Suricata IDS Who This Book Is For Security professionals, malware analysts, SOC analysts, incident responders, detection engineers, reverse engineers, and network security engineers "This book is a beast! If you're looking to master the ever-widening field of malware analysis, look no further. This is the definitive guide for you." Pedram Amini, CTO Inquest; Founder OpenRCE.org and ZeroDayInitiative
Behavior Based Malware Detection
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Author : Mihai Christodorescu
language : en
Publisher:
Release Date : 2007
Behavior Based Malware Detection written by Mihai Christodorescu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.
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.
Automating Malware Detection By Inferring Intent
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Author : Weidong Cui
language : en
Publisher:
Release Date : 2006
Automating Malware Detection By Inferring Intent written by Weidong Cui and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 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.
Malware Data Science
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Author : Joshua Saxe
language : en
Publisher: No Starch Press
Release Date : 2018-09-25
Malware Data Science written by Joshua Saxe 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 2018-09-25 with Computers categories.
Malware Data Science explains how to identify, analyze, and classify large-scale malware using machine learning and data visualization. Security has become a "big data" problem. The growth rate of malware has accelerated to tens of millions of new files per year while our networks generate an ever-larger flood of security-relevant data each day. In order to defend against these advanced attacks, you'll need to know how to think like a data scientist. In Malware Data Science, security data scientist Joshua Saxe introduces machine learning, statistics, social network analysis, and data visualization, and shows you how to apply these methods to malware detection and analysis. You'll learn how to: - Analyze malware using static analysis - Observe malware behavior using dynamic analysis - Identify adversary groups through shared code analysis - Catch 0-day vulnerabilities by building your own machine learning detector - Measure malware detector accuracy - Identify malware campaigns, trends, and relationships through data visualization Whether you're a malware analyst looking to add skills to your existing arsenal, or a data scientist interested in attack detection and threat intelligence, Malware Data Science will help you stay ahead of the curve.
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 And Intrusion Detection In Cyber Physical Systems
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Author : Shiva Darshan, S.L.
language : en
Publisher: IGI Global
Release Date : 2023-09-26
Malware Analysis And Intrusion Detection In Cyber Physical Systems written by Shiva Darshan, S.L. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-26 with Computers categories.
Many static and behavior-based malware detection methods have been developed to address malware and other cyber threats. Even though these cybersecurity systems offer good outcomes in a large dataset, they lack reliability and robustness in terms of detection. There is a critical need for relevant research on enhancing AI-based cybersecurity solutions such as malware detection and malicious behavior identification. Malware Analysis and Intrusion Detection in Cyber-Physical Systems focuses on dynamic malware analysis and its time sequence output of observed activity, including advanced machine learning and AI-based malware detection and categorization tasks in real time. Covering topics such as intrusion detection systems, low-cost manufacturing, and surveillance robots, this premier reference source is essential for cyber security professionals, computer scientists, students and educators of higher education, researchers, and academicians.