Nour Moustafa – författare

The Internet is making our daily lives as digital as possible, and this new era is called the Internet of Everything (IoE). The key force behind the rapid growth of the Internet is the technological advancement of enterprises. The digital world we live in is facilitated by these enterprises’ advances and business intelligence. These enterprises need to deal with gazillions of bytes of data, and in today’s age of General Data Protection Regulation, enterprises are required to ensure privacy and security of large-scale data collections. However, the increased connectivity and devices used to facilitate IoE are continually creating more room for cybercriminals to find vulnerabilities in enterprise systems and flaws in their corporate governance. Ensuring cybersecurity and corporate governance for enterprises should not be an afterthought or present a huge challenge. In recent times, the complex diversity of cyber-attacks has been skyrocketing, and zero-day attacks, such as ransomware, botnet, and telecommunication attacks, are happening more frequently than before. New hacking strategies would easily bypass existing enterprise security and governance platforms using advanced, persistent threats. For example, in 2020, the Toll Group firm was exploited by a new crypto-attack family for violating its data privacy, where an advanced ransomware technique was launched to exploit the corporation and request a huge figure of monetary ransom. Even after applying rational governance hygiene, cybersecurity configuration and software updates are often overlooked when they are most needed to fight cyber-crime and ensure data privacy. Therefore, the threat landscape in the context of enterprises has become wider and far more challenging. There is a clear need for collaborative work throughout the entire value chain of this network. In this context, this book addresses the cybersecurity and cooperate governance challenges associated with enterprises, which will provide a bigger picture of the concepts, intelligent techniques, practices, and open research directions in this area. This book serves as a single source of reference for acquiring the knowledge on the technology, process, and people involved in next-generation privacy and security.

Digital forensics plays a crucial role in identifying, analysing, and presenting cyber threats as evidence in a court of law. Artificial intelligence, particularly machine learning and deep learning, enables automation of the digital investigation process. This book provides an in-depth look at the fundamental and advanced methods in digital forensics. It also discusses how machine learning and deep learning algorithms can be used to detect and investigate cybercrimes.This book demonstrates digital forensics and cyber-investigating techniques with real-world applications. It examines hard disk analytics and style architectures, including Master Boot Record and GUID Partition Table as part of the investigative process. It also covers cyberattack analysis in Windows, Linux, and network systems using virtual machines in real-world scenarios.Digital Forensics in the Era of Artificial Intelligence will be helpful for those interested in digital forensics and using machine learning techniques in the investigation of cyberattacks and the detection of evidence in cybercrimes.

Digital forensics plays a crucial role in identifying, analysing, and presenting cyber threats as evidence in a court of law. Artificial intelligence, particularly machine learning and deep learning, enables automation of the digital investigation process. This book provides an in-depth look at the fundamental and advanced methods in digital forensics. It also discusses how machine learning and deep learning algorithms can be used to detect and investigate cybercrimes.This book demonstrates digital forensics and cyber-investigating techniques with real-world applications. It examines hard disk analytics and style architectures, including Master Boot Record and GUID Partition Table as part of the investigative process. It also covers cyberattack analysis in Windows, Linux, and network systems using virtual machines in real-world scenarios.Digital Forensics in the Era of Artificial Intelligence will be helpful for those interested in digital forensics and using machine learning techniques in the investigation of cyberattacks and the detection of evidence in cybercrimes.

The Internet is making our daily lives as digital as possible, and this new era is called the Internet of Everything (IoE). The key force behind the rapid growth of the Internet is the technological advancement of enterprises. The digital world we live in is facilitated by these enterprises’ advances and business intelligence. These enterprises need to deal with gazillions of bytes of data, and in today’s age of General Data Protection Regulation, enterprises are required to ensure privacy and security of large-scale data collections. However, the increased connectivity and devices used to facilitate IoE are continually creating more room for cybercriminals to find vulnerabilities in enterprise systems and flaws in their corporate governance. Ensuring cybersecurity and corporate governance for enterprises should not be an afterthought or present a huge challenge. In recent times, the complex diversity of cyber-attacks has been skyrocketing, and zero-day attacks, such as ransomware, botnet, and telecommunication attacks, are happening more frequently than before. New hacking strategies would easily bypass existing enterprise security and governance platforms using advanced, persistent threats. For example, in 2020, the Toll Group firm was exploited by a new crypto-attack family for violating its data privacy, where an advanced ransomware technique was launched to exploit the corporation and request a huge figure of monetary ransom. Even after applying rational governance hygiene, cybersecurity configuration and software updates are often overlooked when they are most needed to fight cyber-crime and ensure data privacy. Therefore, the threat landscape in the context of enterprises has become wider and far more challenging. There is a clear need for collaborative work throughout the entire value chain of this network. In this context, this book addresses the cybersecurity and cooperate governance challenges associated with enterprises, which will provide a bigger picture of the concepts, intelligent techniques, practices, and open research directions in this area. This book serves as a single source of reference for acquiring the knowledge on the technology, process, and people involved in next-generation privacy and security.

More frequent and complex cyber threats require robust, automated, and rapid responses from cyber-security specialists. This book offers a complete study in the area of graph learning in cyber, emphasizing graph neural networks (GNNs) and their cyber-security applications.Three parts examine the basics, methods and practices, and advanced topics. The first part presents a grounding in graph data structures and graph embedding and gives a taxonomic view of GNNs and cyber-security applications. The second part explains three different categories of graph learning, including deterministic, generative, and reinforcement learning and how they can be used for developing cyber defense models. The discussion of each category covers the applicability of simple and complex graphs, scalability, representative algorithms, and technical details.Undergraduate students, graduate students, researchers, cyber analysts, and AI engineers looking to understand practical deep learning methods will find this book an invaluable resource.

More frequent and complex cyber threats require robust, automated, and rapid responses from cyber-security specialists. This book offers a complete study in the area of graph learning in cyber, emphasizing graph neural networks (GNNs) and their cyber-security applications.Three parts examine the basics, methods and practices, and advanced topics. The first part presents a grounding in graph data structures and graph embedding and gives a taxonomic view of GNNs and cyber-security applications. The second part explains three different categories of graph learning, including deterministic, generative, and reinforcement learning and how they can be used for developing cyber defense models. The discussion of each category covers the applicability of simple and complex graphs, scalability, representative algorithms, and technical details.Undergraduate students, graduate students, researchers, cyber analysts, and AI engineers looking to understand practical deep learning methods will find this book an invaluable resource.

Deep Learning Approaches for Security Threats in IoT Environments An expert discussion of the application of deep learning methods in the IoT security environment In Deep Learning Approaches for Security Threats in IoT Environments, a team of distinguished cybersecurity educators deliver an insightful and robust exploration of how to approach and measure the security of Internet-of-Things (IoT) systems and networks. In this book, readers will examine critical concepts in artificial intelligence (AI) and IoT, and apply effective strategies to help secure and protect IoT networks. The authors discuss supervised, semi-supervised, and unsupervised deep learning techniques, as well as reinforcement and federated learning methods for privacy preservation. This book applies deep learning approaches to IoT networks and solves the security problems that professionals frequently encounter when working in the field of IoT, as well as providing ways in which smart devices can solve cybersecurity issues. Readers will also get access to a companion website with PowerPoint presentations, links to supporting videos, and additional resources. They’ll also find: A thorough introduction to artificial intelligence and the Internet of Things, including key concepts like deep learning, security, and privacyComprehensive discussions of the architectures, protocols, and standards that form the foundation of deep learning for securing modern IoT systems and networksIn-depth examinations of the architectural design of cloud, fog, and edge computing networksFulsome presentations of the security requirements, threats, and countermeasures relevant to IoT networksPerfect for professionals working in the AI, cybersecurity, and IoT industries, Deep Learning Approaches for Security Threats in IoT Environments will also earn a place in the libraries of undergraduate and graduate students studying deep learning, cybersecurity, privacy preservation, and the security of IoT networks.

A major challenge for machine learning solutions is that their efficiency in real-world applications is constrained by the current lack of ability of the machine to explain its decisions and activities to human users. Biases based on race, gender, age or location have been a long-standing risk in training AI models. Furthermore, AI model performance can degrade because production data differs from training data.Explainable AI (XAI) is the practice of interpreting how and why a machine learning algorithm estimates its predictions. It can also help machine learning practitioners and data scientists understand and interpret a model's behaviour. XAI supports end-users to trust a model's auditability and the productive use of AI. It also mitigates AI compliance, legal, security and reputational risks.Among these applications, the security of IoT infrastructures is vitally essential for improving trust in broad-scale applications such as smart healthcare, smart manufacturing, smart agriculture and smart transportation.This comprehensive co-authored book offers a complete study of explainable artificial intelligence (XAI) for securing the Internet of things (IoT). The authors present innovative XAI solutions for securing IoT infrastructures against security attacks and privacy threats and cover advanced research topics including responsible security intelligence.Providing a systematic and thorough overview of the field, this book will be a valuable resource for ICT researchers, AI and data science engineers, security analysts, undergraduate and graduate students and professionals who wish to gain a fundamental understanding of intelligent security solutions.

This book constitutes the proceedings of the 33rd Australasian Joint Conference on Artificial Intelligence, AI 2020, held in Canberra, ACT, Australia, in November 2020.*The 36 full papers presented in this volume were carefully reviewed and selected from 57 submissions.

This book states that the major aim audience are people who have some familiarity with Internet of things (IoT) but interested to get a comprehensive interpretation of the role of deep Learning in maintaining the security and privacy of IoT. A reader should be friendly with Python and the basics of machine learning and deep learning.

This book states that the major aim audience are people who have some familiarity with Internet of things (IoT) but interested to get a comprehensive interpretation of the role of deep Learning in maintaining the security and privacy of IoT. A reader should be friendly with Python and the basics of machine learning and deep learning.

This book presents that explainable artificial intelligence (XAI) is going to replace the traditional artificial, machine learning, deep learning algorithms which work as a black box as of today.