Malware Data Science
Malware Data Science

Author: Joshua Saxe

Publisher: No Starch Press

Published: 2018-09-25

Total Pages: 274

ISBN-13: 1593278594

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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.

Malware Analysis and Detection Engineering
Malware Analysis and Detection Engineering

Author: Abhijit Mohanta

Publisher: Apress

Published: 2020-11-05

Total Pages: 780

ISBN-13: 9781484261927

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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

Data Mining Tools for Malware Detection
Data Mining Tools for Malware Detection

Author: Mehedy Masud

Publisher: CRC Press

Published: 2016-04-19

Total Pages: 450

ISBN-13: 1439854556

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Although the use of data mining for security and malware detection is quickly on the rise, most books on the subject provide high-level theoretical discussions to the near exclusion of the practical aspects. Breaking the mold, Data Mining Tools for Malware Detection provides a step-by-step breakdown of how to develop data mining tools for malware d

Malware Detection
Malware Detection

Author: Mihai Christodorescu

Publisher: Springer Science & Business Media

Published: 2007-03-06

Total Pages: 307

ISBN-13: 0387445994

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This book captures the state of the art research in the area of malicious code detection, prevention and mitigation. It contains cutting-edge behavior-based techniques to analyze and detect obfuscated malware. The book analyzes current trends in malware activity online, including botnets and malicious code for profit, and it proposes effective models for detection and prevention of attacks using. Furthermore, the book introduces novel techniques for creating services that protect their own integrity and safety, plus the data they manage.

Detection of Intrusions and Malware, and Vulnerability Assessment
Detection of Intrusions and Malware, and Vulnerability Assessment

Author: Leyla Bilge

Publisher: Springer Nature

Published: 2021-07-09

Total Pages: 403

ISBN-13: 3030808254

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This book constitutes the proceedings of the 18th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, DIMVA 2021, held virtually in July 2021. The 18 full papers and 1 short paper presented in this volume were carefully reviewed and selected from 65 submissions. DIMVA serves as a premier forum for advancing the state of the art in intrusion detection, malware detection, and vulnerability assessment. Each year, DIMVA brings together international experts from academia, industry, and government to present and discuss novel research in these areas. Chapter “SPECULARIZER: Detecting Speculative Execution Attacks via Performance Tracing” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Confluence of AI, Machine, and Deep Learning in Cyber Forensics
Confluence of AI, Machine, and Deep Learning in Cyber Forensics

Author: Misra, Sanjay

Publisher: IGI Global

Published: 2020-12-18

Total Pages: 248

ISBN-13: 1799849015

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Developing a knowledge model helps to formalize the difficult task of analyzing crime incidents in addition to preserving and presenting the digital evidence for legal processing. The use of data analytics techniques to collect evidence assists forensic investigators in following the standard set of forensic procedures, techniques, and methods used for evidence collection and extraction. Varieties of data sources and information can be uniquely identified, physically isolated from the crime scene, protected, stored, and transmitted for investigation using AI techniques. With such large volumes of forensic data being processed, different deep learning techniques may be employed. Confluence of AI, Machine, and Deep Learning in Cyber Forensics contains cutting-edge research on the latest AI techniques being used to design and build solutions that address prevailing issues in cyber forensics and that will support efficient and effective investigations. This book seeks to understand the value of the deep learning algorithm to handle evidence data as well as the usage of neural networks to analyze investigation data. Other themes that are explored include machine learning algorithms that allow machines to interact with the evidence, deep learning algorithms that can handle evidence acquisition and preservation, and techniques in both fields that allow for the analysis of huge amounts of data collected during a forensic investigation. This book is ideally intended for forensics experts, forensic investigators, cyber forensic practitioners, researchers, academicians, and students interested in cyber forensics, computer science and engineering, information technology, and electronics and communication.

Detection of Intrusions and Malware, and Vulnerability Assessment
Detection of Intrusions and Malware, and Vulnerability Assessment

Author: Roberto Perdisci

Publisher: Springer

Published: 2019-06-10

Total Pages: 509

ISBN-13: 3030220389

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This book constitutes the proceedings of the 16th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, DIMVA 2019, held in Gothenburg, Sweden, in June 2019. The 23 full papers presented in this volume were carefully reviewed and selected from 80 submissions. The contributions were organized in topical sections named: wild wild web; cyber-physical systems; malware; software security and binary analysis; network security; and attack mitigation.

The Art of Mac Malware
The Art of Mac Malware

Author: Patrick Wardle

Publisher: No Starch Press

Published: 2022-07-12

Total Pages: 329

ISBN-13: 1718501943

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A comprehensive guide to the threats facing Apple computers and the foundational knowledge needed to become a proficient Mac malware analyst. Defenders must fully understand how malicious software works if they hope to stay ahead of the increasingly sophisticated threats facing Apple products today. The Art of Mac Malware: The Guide to Analyzing Malicious Software is a comprehensive handbook to cracking open these malicious programs and seeing what’s inside. Discover the secrets of nation state backdoors, destructive ransomware, and subversive cryptocurrency miners as you uncover their infection methods, persistence strategies, and insidious capabilities. Then work with and extend foundational reverse-engineering tools to extract and decrypt embedded strings, unpack protected Mach-O malware, and even reconstruct binary code. Next, using a debugger, you’ll execute the malware, instruction by instruction, to discover exactly how it operates. In the book’s final section, you’ll put these lessons into practice by analyzing a complex Mac malware specimen on your own. You’ll learn to: Recognize common infections vectors, persistence mechanisms, and payloads leveraged by Mac malware Triage unknown samples in order to quickly classify them as benign or malicious Work with static analysis tools, including disassemblers, in order to study malicious scripts and compiled binaries Leverage dynamical analysis tools, such as monitoring tools and debuggers, to gain further insight into sophisticated threats Quickly identify and bypass anti-analysis techniques aimed at thwarting your analysis attempts A former NSA hacker and current leader in the field of macOS threat analysis, Patrick Wardle uses real-world examples pulled from his original research. The Art of Mac Malware: The Guide to Analyzing Malicious Software is the definitive resource to battling these ever more prevalent and insidious Apple-focused threats.

Intelligent Mobile Malware Detection
Intelligent Mobile Malware Detection

Author: Tony Thomas

Publisher: CRC Press

Published: 2022-12-30

Total Pages: 189

ISBN-13: 1000824985

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The popularity of Android mobile phones has caused more cybercriminals to create malware applications that carry out various malicious activities. The attacks, which escalated after the COVID-19 pandemic, proved there is great importance in protecting Android mobile devices from malware attacks. Intelligent Mobile Malware Detection will teach users how to develop intelligent Android malware detection mechanisms by using various graph and stochastic models. The book begins with an introduction to the Android operating system accompanied by the limitations of the state-of-the-art static malware detection mechanisms as well as a detailed presentation of a hybrid malware detection mechanism. The text then presents four different system call-based dynamic Android malware detection mechanisms using graph centrality measures, graph signal processing and graph convolutional networks. Further, the text shows how most of the Android malware can be detected by checking the presence of a unique subsequence of system calls in its system call sequence. All the malware detection mechanisms presented in the book are based on the authors' recent research. The experiments are conducted with the latest Android malware samples, and the malware samples are collected from public repositories. The source codes are also provided for easy implementation of the mechanisms. This book will be highly useful to Android malware researchers, developers, students and cyber security professionals to explore and build defense mechanisms against the ever-evolving Android malware.

Android Malware Detection using Machine Learning
Android Malware Detection using Machine Learning

Author: ElMouatez Billah Karbab

Publisher: Springer Nature

Published: 2021-07-10

Total Pages: 212

ISBN-13: 303074664X

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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.