Welcome to our book review site go-pdf.online!
You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
Silicon Valley Cybersecurity Conference
This open access book constitutes refereed proceedings of the Third Conference on Silicon Valley Cybersecurity Conference, SVCC 2022, held as virtual event, in August 17-19, 2022. The 8 full papers included in this book were carefully reviewed and selected from 10 submissions. The contributions are divided into the following thematic blocks: Malware Analysis; Blockchain and Smart Contracts; Remote Device Assessment. This is an open access book.
Malware Analysis Using Artificial Intelligence and Deep Learning
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.
Silicon Valley Cybersecurity Conference
This book constitutes selected and revised papers from the Second Silicon Valley Cybersecurity Conference, held in San Jose, USA, in December 2021. Due to the COVID-19 pandemic the conference was held in a virtual format. The 9 full papers and one shoprt paper presented in this volume were thoroughly reviewed and selected from 15 submissions. They present most recent research on dependability, reliability, and security to address cyber-attacks, vulnerabilities, faults, and errors in networks and systems. Chapters 1, 4, 5, 6, and 8-10 are published open access under a CC BY license (Creative Commons Attribution 4.0 International License).
Artificial Intelligence for Cybersecurity
This book explores new and novel applications of machine learning, deep learning, and artificial intelligence that are related to major challenges in the field of cybersecurity. The provided research goes beyond simply applying AI techniques to datasets and instead delves into deeper issues that arise at the interface between deep learning and cybersecurity. This book also provides insight into the difficult "how" and "why" questions that arise in AI within the security domain. For example, this book includes chapters covering "explainable AI", "adversarial learning", "resilient AI", and a wide variety of related topics. It’s not limited to any specific cybersecurity subtopics and the chapters touch upon a wide range of cybersecurity domains, ranging from malware to biometrics and more. Researchers and advanced level students working and studying in the fields of cybersecurity (equivalently, information security) or artificial intelligence (including deep learning, machine learning, big data, and related fields) will want to purchase this book as a reference. Practitioners working within these fields will also be interested in purchasing this book.
Introduction to Machine Learning with Applications in Information Security
Introduction to Machine Learning with Applications in Information Security, Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques, reinforced via realistic applications. The book is accessible and doesn’t prove theorems, or dwell on mathematical theory. The goal is to present topics at an intuitive level, with just enough detail to clarify the underlying concepts. The book covers core classic machine learning topics in depth, including Hidden Markov Models (HMM), Support Vector Machines (SVM), and clustering. Additional machine learning topics include k-Nearest Neighbor (k-NN), boosting, Random Forests, and...
Guide to Vulnerability Analysis for Computer Networks and Systems
This professional guide and reference examines the challenges of assessing security vulnerabilities in computing infrastructure. Various aspects of vulnerability assessment are covered in detail, including recent advancements in reducing the requirement for expert knowledge through novel applications of artificial intelligence. The work also offers a series of case studies on how to develop and perform vulnerability assessment techniques using start-of-the-art intelligent mechanisms. Topics and features: provides tutorial activities and thought-provoking questions in each chapter, together with numerous case studies; introduces the fundamentals of vulnerability assessment, and reviews the st...
Advances on P2P, Parallel, Grid, Cloud and Internet Computing
This book presents the latest research findings, innovative research results, methods and development techniques related to P2P, grid, cloud and Internet computing from both theoretical and practical perspectives. It also reveals the synergies among such large-scale computing paradigms. P2P, grid, cloud and Internet computing technologies have rapidly become established as breakthrough paradigms for solving complex problems by enabling aggregation and sharing of an increasing variety of distributed computational resources at large scale. Grid computing originated as a paradigm for high-performance computing, as an alternative to expensive supercomputers through different forms of large-scale...
Machine Learning Approaches in Cyber Security Analytics
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.
Silicon Valley Cybersecurity Conference
This book constitutes selected and revised papers from the First Silicon Valley Cybersecurity Conference, held in San Jose, USA, in December 2020. Due to the COVID-19 pandemic the conference was held in a virtual format. The 9 full papers and 6 short papers presented in this volume were thoroughly reviewed and selected from 30 submissions. They present most recent research on dependability, reliability, and security to address cyber-attacks, vulnerabilities,faults, and errors in networks and systems.
Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023)
This is an open access book. The 2nd International Conference on Emerging Trends in Engineering (ICETE 2023) will be held in-person from April 28-30, 2023 at University College of Engineering, Osmania University, Hyderabad, India. Since its inception in 2019, The International Conference on Emerging Trends in Engineering (ICETE) has established to enhance the information exchange of theoretical research and practical advancements at national and international levels in the fields of Bio-Medical, Civil, Computer Science, Electrical, Electronics & Communication Engineering, Mechanical and Mining Engineering. This encourages and promotes professional interaction among students, scholars, resear...
