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Adversarial Machine Learning
The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many applications of machine learning are adversarial in nature. Some are adversarial because they are safety critical, such as autonomous driving. An adversary in these applications can be a malicious party aimed at causing congestion or accidents, or may even model unusual situations that expose vulnerabilities in the prediction engine. Other applications are ...
Developing and Securing the Cloud
Although the use of cloud computing platforms and applications has expanded rapidly, most books on the subject focus on high-level concepts. There has long been a need for a book that provides detailed guidance on how to develop secure clouds.Filling this void, Developing and Securing the Cloud provides a comprehensive overview of cloud computing t
Applied Machine Learning for Smart Data Analysis
The book focuses on how machine learning and the Internet of Things (IoT) has empowered the advancement of information driven arrangements including key concepts and advancements. Ontologies that are used in heterogeneous IoT environments have been discussed including interpretation, context awareness, analyzing various data sources, machine learning algorithms and intelligent services and applications. Further, it includes unsupervised and semi-supervised machine learning techniques with study of semantic analysis and thorough analysis of reviews. Divided into sections such as machine learning, security, IoT and data mining, the concepts are explained with practical implementation including results. Key Features Follows an algorithmic approach for data analysis in machine learning Introduces machine learning methods in applications Address the emerging issues in computing such as deep learning, machine learning, Internet of Things and data analytics Focuses on machine learning techniques namely unsupervised and semi-supervised for unseen and seen data sets Case studies are covered relating to human health, transportation and Internet applications
Data and Applications Security and Privacy XXX
This book constitutes the refereed proceedings of the 30th Annual IFIP WG 11.3 International Working Conference on Data and Applications Security and Privacy, DBSec 2016, held in trento, Itlay, in July 2016. The 17 full papers and 7 short papers presented were carefully reviewed and selected from 54 submissions. Their topics cover a wide range of data and application security and privacy problems including those of mobile devices, collaborative systems, databases, big data, virtual systems, cloud computing, and social networks. The program also included twoinvited talks.
Game Theory and Machine Learning for Cyber Security
GAME THEORY AND MACHINE LEARNING FOR CYBER SECURITY Move beyond the foundations of machine learning and game theory in cyber security to the latest research in this cutting-edge field In Game Theory and Machine Learning for Cyber Security, a team of expert security researchers delivers a collection of central research contributions from both machine learning and game theory applicable to cybersecurity. The distinguished editors have included resources that address open research questions in game theory and machine learning applied to cyber security systems and examine the strengths and limitations of current game theoretic models for cyber security. Readers will explore the vulnerabilities o...
Computational Trust Models and Machine Learning
Computational Trust Models and Machine Learning provides a detailed introduction to the concept of trust and its application in various computer science areas, including multi-agent systems, online social networks, and communication systems. Identifying trust modeling challenges that cannot be addressed by traditional approaches, this book:Explains
Data and Applications Security and Privacy XXVI
This book constitutes the refereed proceedings of the 26th IFIP WG 11.3 International Conference on Data and Applications Security and Privacy, DBSec 2012, held in Paris, France in July 2012. The 17 revised full and 15 short papers presented together with 1 invited paper were carefully reviewed and selected from 49 submissions. The papers are organized in topical sections on access control, confidentiality and privacy, smart cards security, privacy-preserving technologies, data management, intrusion and malware, probabilistic attacks and protection, and cloud computing.
Advances in Web Intelligence
This book constitutes the refereed proceedings of the Third International Atlantic Web Intelligence Conference, AWIC 2005, held in Lodz, Poland in June 2005. The 74 revised papers presented together with abstracts of 4 invited papers were carefully reviewed and selected from 140 submissions. All current aspects Web intelligence are addressed including semantic Web issues, ambient intelligence, intelligent information services, Web search, distributed service management, clustering, visualization, data mining, description logics, ontologies, Web query processing, categorization, classification, Web services, e-learning, and knowledge discovery.
Cybersecurity Analytics
Cybersecurity Analytics is for the cybersecurity student and professional who wants to learn data science techniques critical for tackling cybersecurity challenges, and for the data science student and professional who wants to learn about cybersecurity adaptations. Trying to build a malware detector, a phishing email detector, or just interested in finding patterns in your datasets? This book can let you do it on your own. Numerous examples and datasets links are included so that the reader can "learn by doing." Anyone with a basic college-level calculus course and some probability knowledge can easily understand most of the material. The book includes chapters containing: unsupervised learning, semi-supervised learning, supervised learning, text mining, natural language processing, and more. It also includes background on security, statistics, and linear algebra. The website for the book contains a listing of datasets, updates, and other resources for serious practitioners.
Secure Mobile Ad-hoc Networks and Sensors
This book constitutes the thoroughly refereed post-proceedings of the First International Workshop on Secure Mobile Ad-hoc Networks and Sensors, MADNES 2005, held in Singapore, in September 2005.The 12 revised full papers presented together with 5 keynote papers and 1 invited paper were carefully reviewed and selected from a total of 33 submissions. The papers address current topics of all security aspects of constrained network environments with special focus to mobile agents, sensor networks and radio frequency (RF) devices.
