Deployable Machine Learning for Security Defense


Book Description

This book constitutes selected and extended papers from the Second International Workshop on Deployable Machine Learning for Security Defense, MLHat 2021, held in August 2021. Due to the COVID-19 pandemic the conference was held online. The 6 full papers were thoroughly reviewed and selected from 7 qualified submissions. The papers are organized in topical sections on machine learning for security, and malware attack and defense.




Strategic Innovations of AI and ML for E-Commerce Data Security


Book Description

As e-commerce continues to increase in usage and popularity, safeguarding consumers private data becomes critical. Strategic innovations in artificial intelligence and machine learning revolutionize data security by offering advanced tools for threat detection and mitigation. Integrating AI and machine learning into their security solutions will allow businesses to build customer trust and maintain a competitive edge throughout the growing digital landscapes. A thorough examination of cutting-edge innovations in e-commerce data security may ensure security measures keep up with current technological advancements in the industry. Strategic Innovations of AI and ML for E-Commerce Data Security explores practical applications in data security, algorithms, and modelling. It examines solutions for securing e-commerce data, utilizing AI and machine learning for modelling techniques, and navigating complex algorithms. This book covers topics such as data science, threat detection, and cybersecurity, and is a useful resource for computer engineers, data scientists, business owners, academicians, scientists, and researchers.







Utilizing Generative AI for Cyber Defense Strategies


Book Description

As cyber threats become increasingly sophisticated, the need for innovative defense strategies becomes urgent. Generative artificial intelligence (AI) offers a revolutionary approach to enhance cybersecurity. By utilizing advanced algorithms, data analysis, and machine learning, generative AI can simulate complex attack scenarios, identify vulnerabilities, and develop proactive defense mechanisms while adapting to modern-day cyber-attacks. AI strengthens current organizational security while offering quick, effective responses to emerging threats. Decisive strategies are needed to integrate generative AI into businesses defense strategies and protect organizations from attacks, secure digital data, and ensure safe business processes. Utilizing Generative AI for Cyber Defense Strategies explores the utilization of generative AI tools in organizational cyber security and defense. Strategies for effective threat detection and mitigation are presented, with an emphasis on deep learning, artificial intelligence, and Internet of Things (IoT) technology. This book covers topics such as cyber security, threat intelligence, and behavior analysis, and is a useful resource for computer engineers, security professionals, business owners, government officials, data analysts, academicians, scientists, and researchers.




Applying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection


Book Description

APPLYING ARTIFICIAL INTELLIGENCE IN CYBERSECURITY ANALYTICS AND CYBER THREAT DETECTION Comprehensive resource providing strategic defense mechanisms for malware, handling cybercrime, and identifying loopholes using artificial intelligence (AI) and machine learning (ML) Applying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection is a comprehensive look at state-of-the-art theory and practical guidelines pertaining to the subject, showcasing recent innovations, emerging trends, and concerns as well as applied challenges encountered, and solutions adopted in the fields of cybersecurity using analytics and machine learning. The text clearly explains theoretical aspects, framework, system architecture, analysis and design, implementation, validation, and tools and techniques of data science and machine learning to detect and prevent cyber threats. Using AI and ML approaches, the book offers strategic defense mechanisms for addressing malware, cybercrime, and system vulnerabilities. It also provides tools and techniques that can be applied by professional analysts to safely analyze, debug, and disassemble any malicious software they encounter. With contributions from qualified authors with significant experience in the field, Applying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection explores topics such as: Cybersecurity tools originating from computational statistics literature and pure mathematics, such as nonparametric probability density estimation, graph-based manifold learning, and topological data analysis Applications of AI to penetration testing, malware, data privacy, intrusion detection system (IDS), and social engineering How AI automation addresses various security challenges in daily workflows and how to perform automated analyses to proactively mitigate threats Offensive technologies grouped together and analyzed at a higher level from both an offensive and defensive standpoint Providing detailed coverage of a rapidly expanding field, Applying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection is an essential resource for a wide variety of researchers, scientists, and professionals involved in fields that intersect with cybersecurity, artificial intelligence, and machine learning.







Implications of Artificial Intelligence for Cybersecurity


Book Description

In recent years, interest and progress in the area of artificial intelligence (AI) and machine learning (ML) have boomed, with new applications vigorously pursued across many sectors. At the same time, the computing and communications technologies on which we have come to rely present serious security concerns: cyberattacks have escalated in number, frequency, and impact, drawing increased attention to the vulnerabilities of cyber systems and the need to increase their security. In the face of this changing landscape, there is significant concern and interest among policymakers, security practitioners, technologists, researchers, and the public about the potential implications of AI and ML for cybersecurity. The National Academies of Sciences, Engineering, and Medicine convened a workshop on March 12-13, 2019 to discuss and explore these concerns. This publication summarizes the presentations and discussions from the workshop.




Semantic Web Technologies


Book Description

Semantic web technologies (SWTs) offer the richest machine-interpretable (rather than just machine-processable) and explicit semantics that are being extensively used in various domains and industries. This book provides a roadmap for semantic web technologies (SWTs) and highlights their role in a wide range of domains including cloud computing, Internet of Things, big data, sensor network, and so forth. It also explores the prospects of these technologies including different data interchange formats, query languages, ontologies, Linked Data, and notations. The role of SWTs in ‘epidemic Covid-19’, ‘e-learning platforms and systems’, ‘block chain’, ‘open online courses’, and ‘visual analytics in healthcare’ is described as well. This book: Explores all the critical aspects of semantic web technologies (SWTs) Discusses the impact of SWTs on cloud computing, Internet of Things, big data, and sensor network Offers a comprehensive examination of the emerging research in the areas of SWTs and their related domains Provides a template to develop a wide range of smart and intelligent applications Includes latest applications and examples with real data This book is aimed at researchers and graduate students in computer science, informatics, web technology, cloud computing, and Internet of Things.







Deployable Machine Learning for Security Defense


Book Description

This book constitutes selected papers from the First International Workshop on Deployable Machine Learning for Security Defense, MLHat 2020, held in August 2020. Due to the COVID-19 pandemic the conference was held online. The 8 full papers were thoroughly reviewed and selected from 13 qualified submissions. The papers are organized in the following topical sections: understanding the adversaries; adversarial ML for better security; threats on networks.