Bøger af Marwan Omar

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.

This SpringerBrief presents the underlying principles of machine learning and how to deploy various deep learning tools and techniques to tackle and solve certain challenges facing the cybersecurity industry.By implementing innovative deep learning solutions, cybersecurity researchers, students and practitioners can analyze patterns and learn how to prevent cyber-attacks and respond to changing malware behavior. The knowledge and tools introduced in this brief can also assist cybersecurity teams to become more proactive in preventing threats and responding to active attacks in real time. It can reduce the amount of time spent on routine tasks and enable organizations to use their resources more strategically. In short, the knowledge and techniques provided in this brief can help make cybersecurity simpler, more proactive, less expensive and far more effectiveAdvanced-level students in computer science studying machine learning with a cybersecurity focus will find this SpringerBrief useful as a study guide. Researchers and cybersecurity professionals focusing on the application of machine learning tools and techniques to the cybersecurity domain will also want to purchase this SpringerBrief.