MACHINE LEARNING TECHNIQUES AND ANALYTICS FOR CLOUD SECURITY This book covers new methods, surveys, case studies, and policy with almost all machine learning techniques and analytics for cloud security solutions The aim of Machine Learning Techniques and Analytics for Cloud Security is to integrate machine learning approaches to meet various analytical issues in cloud security. Cloud security with ML has long-standing challenges that require methodological and theoretical handling. The conventional cryptography approach is less applied in resource-constrained devices. To solve these issues, the machine learning approach may be effectively used in providing security to the vast growing cloud environment. Machine learning algorithms can also be used to meet various cloud security issues, such as effective intrusion detection systems, zero-knowledge authentication systems, measures for passive attacks, protocols design, privacy system designs, applications, and many more. The book also contains case studies/projects outlining how to implement various security features using machine learning algorithms and analytics on existing cloud-based products in public, private and hybrid cloud respectively. Audience Research scholars and industry engineers in computer sciences, electrical and electronics engineering, machine learning, computer security, information technology, and cryptography.

Explore the power of machine learning to revolutionize signal processing and optimization with cutting-edge techniques and practical insights in this outstanding new volume from Scrivener Publishing. Modeling and Optimization of Signals using Machine Learning Techniques is designed for researchers from academia, industries, and R&D organizations worldwide who are passionate about advancing machine learning methods, signal processing theory, data mining, artificial intelligence, and optimization. This book addresses the role of machine learning in transforming vast signal databases from sensor networks, internet services, and communication systems into actionable decision systems. It explores the development of computational solutions and novel models to handle complex real-world signals such as speech, music, biomedical data, and multimedia. Through comprehensive coverage of cutting-edge techniques, this book equips readers with the tools to automate signal processing and analysis, ultimately enhancing the retrieval of valuable information from extensive data storage systems. By providing both theoretical insights and practical guidance, the book serves as a comprehensive resource for researchers, engineers, and practitioners aiming to harness the power of machine learning in signal processing. Whether for the veteran engineer, scientist in the lab, student, or faculty, this groundbreaking new volume is a valuable resource for researchers and other industry professionals interested in the intersection of technology and agriculture.

MACHINE LEARNING TECHNIQUES FOR VLSI CHIP DESIGN This cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, the efficient hardware of machine learning applications with FPGA or CMOS circuits, and many other aspects and applications of machine learning techniques for VLSI chip design. Artificial intelligence (AI) and machine learning (ML) have, or will have, an impact on almost every aspect of our lives and every device that we own. AI has benefitted every industry in terms of computational speeds, accurate decision prediction, efficient machine learning (ML), and deep learning (DL) algorithms. The VLSI industry uses the electronic design automation tool (EDA), and the integration with ML helps in reducing design time and cost of production. Finding defects, bugs, and hardware Trojans in the design with ML or DL can save losses during production. Constraints to ML-DL arise when having to deal with a large set of training datasets. This book covers the learning algorithm for floor planning, routing, mask fabrication, and implementation of the computational architecture for ML-DL. The future aspect of the ML-DL algorithm is to be available in the format of an integrated circuit (IC). A user can upgrade to the new algorithm by replacing an IC. This new book mainly deals with the adaption of computation blocks like hardware accelerators and novel nano-material for them based upon their application and to create a smart solution. This exciting new volume is an invaluable reference for beginners as well as engineers, scientists, researchers, and other professionals working in the area of VLSI architecture development.

This book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be done from the detailed bibliography presented at the end of each chapter. Besides, this book's material includes concepts, algorithms, figures, graphs, and tables in guiding researchers through deep learning in data science and its applications for society. Deep learning approaches prevent loss of information and hence enhance the performance of data analysis and learning techniques. It brings up many research issues in the industry and research community to capture and access data effectively. The book provides the conceptual basis of deep learning required to achieve in-depth knowledge in computer and data science. It has been done to make the book more flexible and to stimulate further interest in topics. All these help researchers motivate towards learning and implementing the concepts in real-life applications.

This book presents research on the state-of-the-art methods and applications. Security and privacy related issues of cloud are addressed with best practices and approaches for secure cloud computing, such as cloud ontology, blockchain, recommender systems, optimization strategies, data security, intelligent algorithms, defense mechanisms for mitigating DDoS attacks, potential communication algorithms in cloud based IoT, secure cloud solutions.

DEEP LEARNING APPROACHES TO CLOUD SECURITY Covering one of the most important subjects to our society today, cloud security, this editorial team delves into solutions taken from evolving deep learning approaches, solutions allowing computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined through its relation to simpler concepts. Deep learning is the fastest growing field in computer science. Deep learning algorithms and techniques are found to be useful in different areas like automatic machine translation, automatic handwriting generation, visual recognition, fraud detection, and detecting developmental delay in children. However, applying deep learning techniques or algorithms successfully in these areas needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. This book provides state of the art approaches of deep learning in these areas, including areas of detection and prediction, as well as future framework development, building service systems and analytical aspects. In all these topics, deep learning approaches, such as artificial neural networks, fuzzy logic, genetic algorithms, and hybrid mechanisms are used. This book is intended for dealing with modeling and performance prediction of the efficient cloud security systems, thereby bringing a newer dimension to this rapidly evolving field. This groundbreaking new volume presents these topics and trends of deep learning, bridging the research gap, and presenting solutions to the challenges facing the engineer or scientist every day in this area. Whether for the veteran engineer or the student, this is a must-have for any library. Deep Learning Approaches to Cloud Security: Is the first volume of its kind to go in-depth on the newest trends and innovations in cloud security through the use of deep learning approaches Covers these important new innovations, such as AI, data mining, and other evolving computing technologies in relation to cloud security Is a useful reference for the veteran computer scientist or engineer working in this area or an engineer new to the area, or a student in this area Discusses not just the practical applications of these technologies, but also the broader concepts and theory behind how these deep learning tools are vital not just to cloud security, but society as a whole Audience: Computer scientists, scientists and engineers working with information technology, design, network security, and manufacturing, researchers in computers, electronics, and electrical and network security, integrated domain, and data analytics, and students in these areas

Prepare for the challenging CySA+ certification exam with this money-saving, up-to-date study package Designed as a complete self-study program, this collection offers a variety of proven resources to use in preparation for the latest edition of the CompTIA Cybersecurity Analyst (CySA+) certification exam. Comprised of CompTIA CySA+ Cybersecurity Analyst Certification All-In-One Exam Guide, Second Edition (Exam CS0-002) and CompTIA CySA+ Cybersecurity Analyst Certification Practice Exams (Exam CS0-002), this bundle thoroughly covers every topic on the exam. CompTIA CySA+ Cybersecurity Analyst Certification Bundle, Second Edition (Exam CS0-002) contains more than 800 practice questions that match those on the live exam in content, difficulty, tone, and format. The collection includes detailed explanations of both multiple choice and performance-based questions. This authoritative, cost-effective bundle serves both as a study tool and a valuable on-the-job reference for computer security professionals. •This bundle is 25% cheaper than purchasing the books individually and includes a 10% off the exam voucher offer •Online content includes additional practice questions, a cybersecurity audit checklist, and a quick review guide •Written by a team of recognized cybersecurity experts

Prepare for the CompTIA CySA+ certification exam with this fully updated self-study resource This highly effective self-study system provides complete coverage of every objective for the challenging CompTIA CySA+ Cybersecurity Analyst exam. You’ll find learning objectives at the beginning of each chapter, exam tips, in-depth explanations, and practice exam questions. All questions closely mirror those on the actual test in content, format, and tone. Designed to help you pass the CS0-002 exam with ease, this definitive guide also serves as an essential on-the-job reference. Covers all exam topics, including: Threat and vulnerability management Threat data and intelligence Vulnerability management, assessment tools, and mitigation Software and systems security Solutions for infrastructure management Software and hardware assurance best practices Security operations and monitoring Proactive threat hunting Automation concepts and technologies Incident response process, procedure, and analysis Compliance and assessment Data privacy and protection Support of organizational risk mitigation Online content includes: 200+ practice questions Interactive performance-based questions Test engine that provides full-length practice exams and customizable quizzes by exam objective

Everything you need to succeed on the Google Cloud Certified Professional Cloud Architect exam in one accessible study guide Take the challenging Google Cloud Certified Professional Cloud Architect exam with confidence using the comprehensive information contained in this invaluable self-study guide. The book provides a thorough overview of cloud architecture and Google Cloud Platform (GCP) and shows you how to pass the test. Beyond exam preparation, the guide also serves as a valuable on-the-job reference. Written by a recognized expert in the field, Google Cloud Certified Professional Cloud Architect All-In-One Exam Guide is based on proven pedagogy and features special elements that teach and reinforce practical skills. The book contains accurate practice questions and in-depth explanations. You will discover how to design, develop, and manage robust, secure, scalable, and highly available solutions to drive business objectives. Offers 100% coverage of every objective for the Google Cloud Certified Professional Cloud Architect exam Online content includes 100 additional practice questions in the TotalTester customizable exam engine Written by a Google Cloud Certified Professional Cloud Architect

Implement a High-Performance Enterprise Java Application Modernization Strategy Learn cutting-edge techniques and processes to systematically and strategically modernize legacy Java applications with predictability, consistency, and confidence. This Oracle Press guide offers an innovative blueprint that empowers corporate management teams to better understand necessary technical requirements and enables Java architects and developers to better align with agile business needs. Rapid Modernization of Java Applications: Practical Business and Technical Solutions for Upgrading Your Enterprise Portfolio contains modernization approaches that offer end-to-end Java application portfolio visibility so that application modernization projects can stay on-schedule and within budget.