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Many static and behavior-based malware detection methods have been developed to address malware and other cyber threats. Even though these cybersecurity systems offer good outcomes in a large dataset, they lack reliability and robustness in terms of detection. There is a critical need for relevant research on enhancing AI-based cybersecurity solutions such as malware detection and malicious behavior identification. Malware Analysis and Intrusion Detection in Cyber-Physical Systems focuses on dynamic malware analysis and its time sequence output of observed activity, including advanced machine learning and AI-based malware detection and categorization tasks in real time. Covering topics such as intrusion detection systems, low-cost manufacturing, and surveillance robots, this premier reference source is essential for cyber security professionals, computer scientists, students and educators of higher education, researchers, and academicians.
This book documents recent advances in the field of modeling, simulation, control, security and reliability of Cyber- Physical Systems (CPS) in power grids. The aim of this book is to help the reader gain insights into working of CPSs and understand their potential in transforming the power grids of tomorrow. This book will be useful for all those who are interested in design of cyber-physical systems, be they students or researchers in power systems, CPS modeling software developers, technical marketing professionals and business policy-makers.
Every day approximately three-hundred thousand to four-hundred thousand new malware are registered, many of them being adware and variants of previously known malware. Anti-virus companies and researchers cannot deal with such a deluge of malware – to analyze and build patches. The only way to scale the efforts is to build algorithms to enable machines to analyze malware and classify and cluster them to such a level of granularity that it will enable humans (or machines) to gain critical insights about them and build solutions that are specific enough to detect and thwart existing malware and generic-enough to thwart future variants. Advances in Malware and Data-Driven Network Security comprehensively covers data-driven malware security with an emphasis on using statistical, machine learning, and AI as well as the current trends in ML/statistical approaches to detecting, clustering, and classification of cyber-threats. Providing information on advances in malware and data-driven network security as well as future research directions, it is ideal for graduate students, academicians, faculty members, scientists, software developers, security analysts, computer engineers, programmers, IT specialists, and researchers who are seeking to learn and carry out research in the area of malware and data-driven network security.
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.
​This book is focused on the use of deep learning (DL) and artificial intelligence (AI) as tools to advance the fields of malware detection and analysis. The individual chapters of the book deal with a wide variety of state-of-the-art AI and DL techniques, which are applied to a number of challenging malware-related problems. DL and AI based approaches to malware detection and analysis are largely data driven and hence minimal expert domain knowledge of malware is needed. This book fills a gap between the emerging fields of DL/AI and malware analysis. It covers a broad range of modern and practical DL and AI techniques, including frameworks and development tools enabling the audience to innovate with cutting-edge research advancements in a multitude of malware (and closely related) use cases.
Cyber-attacks significantly impact all sectors of the economy, reduce public confidence in e-services, and threaten the development of the economy using information and communication technologies. The security of information systems and electronic services is crucial to each citizen's social and economic well-being, health, and life. As cyber threats continue to grow, developing, introducing, and improving defense mechanisms becomes an important issue. Cyber Security Policies and Strategies of the World's Leading States is a comprehensive book that analyzes the impact of cyberwarfare on world politics, political conflicts, and the identification of new types of threats. It establishes a definition of civil cyberwarfare and explores its impact on political processes. This book is essential for government officials, academics, researchers, non-government organization (NGO) representatives, mass-media representatives, business sector representatives, and students interested in cyber warfare, cyber security, information security, defense and security, and world political issues. With its comprehensive coverage of cyber security policies and strategies of the world's leading states, it is a valuable resource for those seeking to understand the evolving landscape of cyber security and its impact on global politics. It provides methods to identify, prevent, reduce, and eliminate existing threats through a comprehensive understanding of cyber security policies and strategies used by leading countries worldwide.
In the digital transformation era, integrating business intelligence and data analytics has become critical for the growth and sustainability of industrial organizations. However, with this technological evolution comes the pressing need for robust cybersecurity measures to safeguard valuable business intelligence from security threats. Strengthening Industrial Cybersecurity to Protect Business Intelligence delves into the theoretical foundations and empirical studies surrounding the intersection of business intelligence and cybersecurity within various industrial domains. This book addresses the importance of cybersecurity controls in mitigating financial losses and reputational damage caused by cyber-attacks. The content spans a spectrum of topics, including advances in business intelligence, the role of artificial intelligence in various business applications, and the integration of intelligent systems across industry 5.0. Ideal for academics in information systems, cybersecurity, and organizational science, as well as government officials and organizations, this book serves as a vital resource for understanding the intricate relationship between business intelligence and cybersecurity. It is equally beneficial for students seeking insights into the security implications of digital transformation processes for achieving business continuity.
In contemporary engineering domains, optimization and decision-making issues are crucial. Given the vast amounts of available data, processing times and memory usage can be substantial. Developing and implementing novel heuristic algorithms is time-consuming, yet even minor improvements in solutions can significantly reduce computational costs. In such scenarios, the creation of heuristics and metaheuristic algorithms has proven advantageous. The convergence of machine learning and metaheuristic algorithms offers a promising approach to address these challenges. Metaheuristic and Machine Learning Optimization Strategies for Complex Systems covers all areas of comprehensive information about hyper-heuristic models, hybrid meta-heuristic models, nature-inspired computing models, and meta-heuristic models. The key contribution of this book is the construction of a hyper-heuristic approach for any general problem domain from a meta-heuristic algorithm. Covering topics such as cloud computing, internet of things, and performance evaluation, this book is an essential resource for researchers, postgraduate students, educators, data scientists, machine learning engineers, software developers and engineers, policy makers, and more.
Intelligent Cyber-Physical Systems Security for Industry 4.0: Applications, Challenges and Management presents new cyber-physical security findings for Industry 4.0 using emerging technologies like artificial intelligence (with machine/deep learning), data mining, applied mathematics. All these are the essential components for processing data, recognizing patterns, modeling new techniques, and improving the advantages of data science. Features • Presents an integrated approach with Cyber-Physical Systems, CPS security, and Industry 4.0 in one place • Exposes the necessity of security initiatives, standards, security policies, and procedures in the context of industry 4.0 • Suggests solutions for enhancing the protection of 5G and the Internet of Things (IoT) security • Promotes how optimization or intelligent techniques envisage the role of artificial intelligence-machine/deep learning (AI-ML/DL) in cyberphysical systems security for industry 4.0 This book is primarily aimed at graduates, researchers and professionals working in the field of security. Executives concerned with security management, knowledge dissemination, information, and policy development for data and network security in different educational, government, and non-government organizations will also find this book useful.
In an age defined by the transformative ascent of cloud computing and the Internet of Things (IoT), our technological landscape has undergone a revolutionary evolution, enhancing convenience and connectivity in unprecedented ways. This convergence, while redefining how we interact with data and devices, has also brought to the forefront a pressing concern – the susceptibility of these systems to security breaches. As cloud services integrate further into our daily lives and the IoT saturates every aspect of our routines, the looming potential for cyberattacks and data breaches necessitates immediate and robust solutions to fortify the protection of sensitive information, ensuring the privacy and integrity of individuals, organizations, and critical infrastructure. Emerging Technologies for Securing the Cloud and IoT emerges as a comprehensive and timely solution to address the multifaceted security challenges posed by these groundbreaking technologies. Edited by Amina Ahmed Nacer from the University of Lorraine, France, and Mohammed Riyadh Abdmeziem from Ecole Nationale Supérieur d’Informatique, Algeria, this book serves as an invaluable guide for both academic scholars and industry experts. Its content delves deeply into the intricate web of security concerns, elucidating the potential ramifications of unaddressed vulnerabilities within cloud and IoT systems. With a pragmatic focus on real-world applications, the book beckons authors to explore themes like security frameworks, integration of AI and machine learning, data safeguarding, threat modeling, and more. Authored by esteemed researchers, practitioners, and luminaries, each chapter bridges the divide between theory and implementation, aiming to be an authoritative reference empowering readers to adeptly navigate the complexities of securing cloud-based IoT systems. A crucial resource for scholars, students, professionals, and policymakers striving to comprehend, confront, and surmount contemporary and future security challenges, this book stands as the quintessential guide for ushering in an era of secure technological advancement.