Malware Science

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.

Unlock the secrets of malware data science with cutting-edge techniques, AI-driven analysis, and international compliance standards to stay ahead of the ever-evolving cyber threat landscape Key Features Get introduced to three primary AI tactics used in malware and detection Leverage data science tools to combat critical cyber threats Understand regulatory requirements for using AI in cyber threat management Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn today's world full of online threats, the complexity of harmful software presents a significant challenge for detection and analysis. This insightful guide will teach you how to apply the principles of data science to online security, acting as both an educational resource and a practical manual for everyday use. Malware Science starts by explaining the nuances of malware, from its lifecycle to its technological aspects before introducing you to the capabilities of data science in malware detection by leveraging machine learning, statistical analytics, and social network analysis. As you progress through the chapters, you’ll explore the analytical methods of reverse engineering, machine language, dynamic scrutiny, and behavioral assessments of malicious software. You’ll also develop an understanding of the evolving cybersecurity compliance landscape with regulations such as GDPR and CCPA, and gain insights into the global efforts in curbing cyber threats. By the end of this book, you’ll have a firm grasp on the modern malware lifecycle and how you can employ data science within cybersecurity to ward off new and evolving threats.What you will learn Understand the science behind malware data and its management lifecycle Explore anomaly detection with signature and heuristics-based methods Analyze data to uncover relationships between data points and create a network graph Discover methods for reverse engineering and analyzing malware Use ML, advanced analytics, and data mining in malware data analysis and detection Explore practical insights and the future state of AI’s use for malware data science Understand how NLP AI employs algorithms to analyze text for malware detection Who this book is for This book is for cybersecurity experts keen on adopting data-driven defense methods. Data scientists will learn how to apply their skill set to address critical security issues, and compliance officers navigating global regulations like GDPR and CCPA will gain indispensable insights. Academic researchers exploring the intersection of data science and cybersecurity, IT decision-makers overseeing organizational strategy, and tech enthusiasts eager to understand modern cybersecurity will also find plenty of useful information in this guide. A basic understanding of cybersecurity and information technology is a prerequisite.

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 Diffusion Models for Wireless Complex Networks: Theory and Applications provides a timely update on malicious software (malware), a serious concern for all types of network users, from laymen to experienced administrators. As the proliferation of portable devices, namely smartphones and tablets, and their increased capabilities, has propelled the intensity of malware spreading and increased its consequences in social life and the global economy, this book provides the theoretical aspect of malware dissemination, also presenting modeling approaches that describe the behavior and dynamics of malware diffusion in various types of wireless complex networks. Sections include a systematic introduction to malware diffusion processes in computer and communications networks, an analysis of the latest state-of-the-art malware diffusion modeling frameworks, such as queuing-based techniques, calculus of variations based techniques, and game theory based techniques, also demonstrating how the methodologies can be used for modeling in more general applications and practical scenarios. Presents a timely update on malicious software (malware), a serious concern for all types of network users, from laymen to experienced administrators Systematically introduces malware diffusion processes, providing the relevant mathematical background Discusses malware modeling frameworks and how to apply them to complex wireless networks Provides guidelines and directions for extending the corresponding theories in other application domains, demonstrating such possibility by using application models in information dissemination scenarios

Mobile devices, such as smart phones, have achieved computing and networking capabilities comparable to traditional personal computers. Their successful consumerization has also become a source of pain for adopting users and organizations. In particular, the widespread presence of information-stealing applications and other types of mobile malware raises substantial security and privacy concerns. Android Malware presents a systematic view on state-of-the-art mobile malware that targets the popular Android mobile platform. Covering key topics like the Android malware history, malware behavior and classification, as well as, possible defense techniques.

Malware has gone mobile, and the security landscape is changing quickly with emerging attacks on cell phones, PDAs, and other mobile devices. This first book on the growing threat covers a wide range of malware targeting operating systems like Symbian and new devices like the iPhone. Examining code in past, current, and future risks, protect your banking, auctioning, and other activities performed on mobile devices. * Visual Payloads View attacks as visible to the end user, including notation of variants. * Timeline of Mobile Hoaxes and Threats Understand the history of major attacks and horizon for emerging threates. * Overview of Mobile Malware Families Identify and understand groups of mobile malicious code and their variations. * Taxonomy of Mobile Malware Bring order to known samples based on infection, distribution, and payload strategies. * Phishing, SMishing, and Vishing Attacks Detect and mitigate phone-based phishing (vishing) and SMS phishing (SMishing) techniques. * Operating System and Device Vulnerabilities Analyze unique OS security issues and examine offensive mobile device threats. * Analyze Mobile Malware Design a sandbox for dynamic software analysis and use MobileSandbox to analyze mobile malware. * Forensic Analysis of Mobile Malware Conduct forensic analysis of mobile devices and learn key differences in mobile forensics. * Debugging and Disassembling Mobile Malware Use IDA and other tools to reverse-engineer samples of malicious code for analysis. * Mobile Malware Mitigation Measures Qualify risk, understand threats to mobile assets, defend against attacks, and remediate incidents. * Understand the History and Threat Landscape of Rapidly Emerging Mobile Attacks * Analyze Mobile Device/Platform Vulnerabilities and Exploits * Mitigate Current and Future Mobile Malware Threats

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.

Malicious software (i.e., malware) has become a severe threat to interconnected computer systems for decades and has caused billions of dollars damages each year. A large volume of new malware samples are discovered daily. Even worse, malware is rapidly evolving becoming more sophisticated and evasive to strike against current malware analysis and defense systems. Automatic Malware Analysis presents a virtualized malware analysis framework that addresses common challenges in malware analysis. In regards to this new analysis framework, a series of analysis techniques for automatic malware analysis is developed. These techniques capture intrinsic characteristics of malware, and are well suited for dealing with new malware samples and attack mechanisms.