Defending Cyber Systems Through Reverse Engineering Of Criminal Malware
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Defending Cyber Systems through Reverse Engineering of Criminal Malware
This SpringerBrief discusses underlying principles of malware reverse engineering and introduces the major techniques and tools needed to effectively analyze malware that targets business organizations. It also covers the examination of real-world malware samples, which illustrates the knowledge and skills necessary to take control of cyberattacks. This SpringerBrief explores key tools and techniques to learn the main elements of malware analysis from the inside out. It also presents malware reverse engineering using several methodical phases, in order to gain a window into the mind set of hackers. Furthermore, this brief examines malicious program’s behavior and views its code-level patterns. Real world malware specimens are used to demonstrate the emerging behavioral patterns of battlefield malware as well. This SpringerBrief is unique, because it demonstrates the capabilities of emerging malware by conducting reverse-code engineering on real malware samples and conducting behavioral analysis in isolated lab system. Specifically, the author focuses on analyzing malicious Windows executables. This type of malware poses a large threat to modern enterprises. Attackers often deploy malicious documents and browser-based exploits to attack Windows enterprise environment. Readers learn how to take malware inside-out using static properties analysis, behavioral analysis and code-level analysis techniques. The primary audience for this SpringerBrief is undergraduate students studying cybersecurity and researchers working in this field. Cyber security professionals that desire to learn more about malware analysis tools and techniques will also want to purchase this SpringerBrief.
Application of Large Language Models (LLMs) for Software Vulnerability Detection
Large Language Models (LLMs) are redefining the landscape of cybersecurity, offering innovative methods for detecting software vulnerabilities. By applying advanced AI techniques to identify and predict weaknesses in software code, including zero-day exploits and complex malware, LLMs provide a proactive approach to securing digital environments. This integration of AI and cybersecurity presents new possibilities for enhancing software security measures. Application of Large Language Models (LLMs) for Software Vulnerability Detection offers a comprehensive exploration of this groundbreaking field. These chapters are designed to bridge the gap between AI research and practical application in cybersecurity, in order to provide valuable insights for researchers, AI specialists, software developers, and industry professionals. Through real-world examples and actionable strategies, the publication will drive innovation in vulnerability detection and set new standards for leveraging AI in cybersecurity.
Innovations, Securities, and Case Studies Across Healthcare, Business, and Technology
Author: Burrell, Darrell Norman
language: en
Publisher: IGI Global
Release Date: 2024-01-15
The longstanding practice of keeping academic disciplines separate has been a barrier to effectively addressing the complex challenges in our world. The boundaries separating fields like healthcare, social sciences, and technology have obscured the potential for interdisciplinary collaboration, preventing us from unlocking innovative solutions to the most pressing issues of our time. As a result, the critical problems we face, from healthcare inequities to technological advancements with ethical dilemmas, have remained largely unresolved. This fragmented approach to academic inquiry has left a void in our quest to tackle these challenges effectively. The solution is found within the pages of Innovations, Securities, and Case Studies Across Healthcare, Business, and Technology. This groundbreaking compendium illuminates the transformative potential of interdisciplinary collaboration, offering direction and support in the form of knowledge for scholars, researchers, practitioners, and students committed to solving real-world problems. By harnessing the collective wisdom of diverse disciplines, the book demonstrates how convergence across healthcare, social sciences, organizational behavior, and technology can lead to groundbreaking insights and solutions. It showcases success stories and innovative strategies that drive positive change within our societies, offering a roadmap towards a brighter, more interconnected future.