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.
The integration of fog computing with the resource-limited Internet of Things (IoT) network formulates the concept of the fog-enabled IoT system. Due to a large number of IoT devices, the IoT is a main source of Big Data. A large volume of sensing data is generated by IoT systems such as smart cities and smart-grid applications. A fundamental research issue is how to provide a fast and efficient data analytics solution for fog-enabled IoT systems. Big Data Analytics in Fog-Enabled IoT Networks: Towards a Privacy and Security Perspective focuses on Big Data analytics in a fog-enabled-IoT system and provides a comprehensive collection of chapters that touch on different issues related to healthcare systems, cyber-threat detection, malware detection, and the security and privacy of IoT Big Data and IoT networks. This book also emphasizes and facilitates a greater understanding of various security and privacy approaches using advanced artificial intelligence and Big Data technologies such as machine and deep learning, federated learning, blockchain, and edge computing, as well as the countermeasures to overcome the vulnerabilities of the fog-enabled IoT system.
Recent decades have seen a proliferation of cybersecurity guidance in the form of government regulations and standards with which organizations must comply. As society becomes more heavily dependent on cyberspace, increasing levels of security measures will need to be established and maintained to protect the confidentiality, integrity, and availability of information. Global Perspectives on Information Security Regulations: Compliance, Controls, and Assurance summarizes current cybersecurity guidance and provides a compendium of innovative and state-of-the-art compliance and assurance practices and tools. It provides a synopsis of current cybersecurity guidance that organizations should consider so that management and their auditors can regularly evaluate their extent of compliance. Covering topics such as cybersecurity laws, deepfakes, and information protection, this premier reference source is an excellent resource for cybersecurity consultants and professionals, IT specialists, business leaders and managers, government officials, faculty and administration of both K-12 and higher education, libraries, students and educators of higher education, researchers, and academicians.
The advancement of information and communication technology has led to a multi-dimensional impact in the areas of law, regulation, and governance. Many countries have declared data protection a fundamental right and established reforms of data protection law aimed at modernizing the global regulatory framework. Due to these advancements in policy, the legal domain has to face many challenges at a rapid pace making it essential to study and discuss policies and laws that regulate and monitor these activities and anticipate new laws that should be implemented in order to protect users. The Handbook of Research on Cyber Law, Data Protection, and Privacy focuses acutely on the complex relationships of technology and law both in terms of substantive legal responses to legal, social, and ethical issues arising in connection with growing public engagement with technology and the procedural impacts and transformative potential of technology on traditional and emerging forms of dispute resolution. Covering a range of topics such as artificial intelligence, data protection, and social media, this major reference work is ideal for government officials, policymakers, industry professionals, academicians, scholars, researchers, practitioners, instructors, and students.
Cyber security is a key focus in the modern world as more private information is stored and saved online. In order to ensure vital information is protected from various cyber threats, it is essential to develop a thorough understanding of technologies that can address cyber security challenges. Artificial intelligence has been recognized as an important technology that can be employed successfully in the cyber security sector. Due to this, further study on the potential uses of artificial intelligence is required. Methods, Implementation, and Application of Cyber Security Intelligence and Analytics discusses critical artificial intelligence technologies that are utilized in cyber security and considers various cyber security issues and their optimal solutions supported by artificial intelligence. Covering a range of topics such as malware, smart grid, data breachers, and machine learning, this major reference work is ideal for security analysts, cyber security specialists, data analysts, security professionals, computer scientists, government officials, researchers, scholars, academicians, practitioners, instructors, and students.
Developing nations have seen many technological advances in the last decade. Although beneficial and progressive, they can lead to unsafe mobile devices, system networks, and internet of things (IoT) devices, causing security vulnerabilities that can have ripple effects throughout society. While researchers attempt to find solutions, improper implementation and negative uses of technology continue to create new security threats to users. Cybersecurity Capabilities in Developing Nations and Its Impact on Global Security brings together research-based chapters and case studies on systems security techniques and current methods to identify and overcome technological vulnerabilities, emphasizing security issues in developing nations. Focusing on topics such as data privacy and security issues, this book is an essential reference source for researchers, university academics, computing professionals, and upper-level students in developing countries interested in the techniques, laws, and training initiatives currently being implemented and adapted for secure computing.
Author: Management Association, Information Resources
Publisher: IGI Global
The rise of technology has proven to be a threat to personal data, cyberspace protection, and organizational security. However, these technologies can be used to enhance the effectiveness of institutional security. Through the use of blockchain and the internet of things (IoT), organizations may combat cybercriminals and better protect their privacy. The Research Anthology on Convergence of Blockchain, Internet of Things, and Security describes the implementation of blockchain and IoT technologies to better protect personal and organizational data as well as enhance overall security. It also explains the tools, applications, and emerging innovations in security and the ways in which they are enhanced by blockchain and IoT. Covering topics such as electronic health records, intrusion detection, and software engineering, this major reference work is an essential resource for business leaders and executives, IT managers, computer scientists, hospital administrators, security professionals, law enforcement, students and faculty of higher education, librarians, researchers, and academicians.
The COVID-19 pandemic has forced organizations and individuals to embrace new practices such as social distancing and remote working. During these unprecedented times, many have increasingly relied on the internet for work, shopping, and healthcare. However, while the world focuses on the health and economic threats posed by the COVID-19 pandemic, cyber criminals are capitalizing on this crisis as the world has become more digitally dependent and vulnerable than ever. Cybersecurity Crisis Management and Lessons Learned From the COVID-19 Pandemic provides cutting-edge research on the best guidelines for preventing, detecting, and responding to cyber threats within educational, business, health, and governmental organizations during the COVID-19 pandemic. It further highlights the importance of focusing on cybersecurity within organizational crisis management. Covering topics such as privacy and healthcare, remote work, and personal health data, this premier reference source is an indispensable resource for startup companies, health and business executives, ICT procurement managers, IT professionals, libraries, students and educators of higher education, entrepreneurs, government officials, social media experts, researchers, and academicians.
This book introduces various machine learning methods for cyber security analytics. With an overwhelming amount of data being generated and transferred over various networks, monitoring everything that is exchanged and identifying potential cyber threats and attacks poses a serious challenge for cyber experts. Further, as cyber attacks become more frequent and sophisticated, there is a requirement for machines to predict, detect, and identify them more rapidly. Machine learning offers various tools and techniques to automate and quickly predict, detect, and identify cyber attacks.
CYBERSECURITY IN INTELLIGENT NETWORKING SYSTEMS Help protect your network system with this important reference work on cybersecurity Cybersecurity and privacy are critical to modern network systems. As various malicious threats have been launched that target critical online services—such as e-commerce, e-health, social networks, and other major cyber applications—it has become more critical to protect important information from being accessed. Data-driven network intelligence is a crucial development in protecting the security of modern network systems and ensuring information privacy. Cybersecurity in Intelligent Networking Systems provides a background introduction to data-driven cybersecurity, privacy preservation, and adversarial machine learning. It offers a comprehensive introduction to exploring technologies, applications, and issues in data-driven cyber infrastructure. It describes a proposed novel, data-driven network intelligence system that helps provide robust and trustworthy safeguards with edge-enabled cyber infrastructure, edge-enabled artificial intelligence (AI) engines, and threat intelligence. Focusing on encryption-based security protocol, this book also highlights the capability of a network intelligence system in helping target and identify unauthorized access, malicious interactions, and the destruction of critical information and communication technology. Cybersecurity in Intelligent Networking Systems readers will also find: Fundamentals in AI for cybersecurity, including artificial intelligence, machine learning, and security threats Latest technologies in data-driven privacy preservation, including differential privacy, federated learning, and homomorphic encryption Key areas in adversarial machine learning, from both offense and defense perspectives Descriptions of network anomalies and cyber threats Background information on data-driven network intelligence for cybersecurity Robust and secure edge intelligence for network anomaly detection against cyber intrusions Detailed descriptions of the design of privacy-preserving security protocols Cybersecurity in Intelligent Networking Systems is an essential reference for all professional computer engineers and researchers in cybersecurity and artificial intelligence, as well as graduate students in these fields.
The authors develop a malware fingerprinting framework to cover accurate android malware detection and family attribution in this book. The authors emphasize the following: (1) the scalability over a large malware corpus; (2) the resiliency to common obfuscation techniques; (3) the portability over different platforms and architectures. First, the authors propose an approximate fingerprinting technique for android packaging that captures the underlying static structure of the android applications in the context of bulk and offline detection at the app-market level. This book proposes a malware clustering framework to perform malware clustering by building and partitioning the similarity network of malicious applications on top of this fingerprinting technique. Second, the authors propose an approximate fingerprinting technique that leverages dynamic analysis and natural language processing techniques to generate Android malware behavior reports. Based on this fingerprinting technique, the authors propose a portable malware detection framework employing machine learning classification. Third, the authors design an automatic framework to produce intelligence about the underlying malicious cyber-infrastructures of Android malware. The authors then leverage graph analysis techniques to generate relevant intelligence to identify the threat effects of malicious Internet activity associated with android malware. The authors elaborate on an effective android malware detection system, in the online detection context at the mobile device level. It is suitable for deployment on mobile devices, using machine learning classification on method call sequences. Also, it is resilient to common code obfuscation techniques and adaptive to operating systems and malware change overtime, using natural language processing and deep learning techniques. Researchers working in mobile and network security, machine learning and pattern recognition will find this book useful as a reference. Advanced-level students studying computer science within these topic areas will purchase this book as well.
The book illustrates the inter-relationship between several data management, analytics and decision support techniques and methods commonly adopted in Cybersecurity-oriented frameworks. The recent advent of Big Data paradigms and the use of data science methods, has resulted in a higher demand for effective data-driven models that support decision-making at a strategic level. This motivates the need for defining novel data analytics and decision support approaches in a myriad of real-life scenarios and problems, with Cybersecurity-related domains being no exception. This contributed volume comprises nine chapters, written by leading international researchers, covering a compilation of recent advances in Cybersecurity-related applications of data analytics and decision support approaches. In addition to theoretical studies and overviews of existing relevant literature, this book comprises a selection of application-oriented research contributions. The investigations undertaken across these chapters focus on diverse and critical Cybersecurity problems, such as Intrusion Detection, Insider Threats, Insider Threats, Collusion Detection, Run-Time Malware Detection, Intrusion Detection, E-Learning, Online Examinations, Cybersecurity noisy data removal, Secure Smart Power Systems, Security Visualization and Monitoring. Researchers and professionals alike will find the chapters an essential read for further research on the topic.