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Data Science For Cyber-security
Cyber-security is a matter of rapidly growing importance in industry and government. This book provides insight into a range of data science techniques for addressing these pressing concerns.The application of statistical and broader data science techniques provides an exciting growth area in the design of cyber defences. Networks of connected devices, such as enterprise computer networks or the wider so-called Internet of Things, are all vulnerable to misuse and attack, and data science methods offer the promise to detect such behaviours from the vast collections of cyber traffic data sources that can be obtained. In many cases, this is achieved through anomaly detection of unusual behaviour against understood statistical models of normality.This volume presents contributed papers from an international conference of the same name held at Imperial College. Experts from the field have provided their latest discoveries and review state of the art technologies.
Data Analysis for Network Cyber-Security
There is increasing pressure to protect computer networks against unauthorized intrusion, and some work in this area is concerned with engineering systems that are robust to attack. However, no system can be made invulnerable. Data Analysis for Network Cyber-Security focuses on monitoring and analyzing network traffic data, with the intention of preventing, or quickly identifying, malicious activity. Such work involves the intersection of statistics, data mining and computer science. Fundamentally, network traffic is relational, embodying a link between devices. As such, graph analysis approaches are a natural candidate. However, such methods do not scale well to the demands of real problems...
Dynamic Networks and Cyber-Security
As an under-studied area of academic research, the analysis of computer network traffic data is still in its infancy. However, the challenge of detecting and mitigating malicious or unauthorised behaviour through the lens of such data is becoming an increasingly prominent issue. This collection of papers by leading researchers and practitioners synthesises cutting-edge work in the analysis of dynamic networks and statistical aspects of cyber security. The book is structured in such a way as to keep security application at the forefront of discussions. It offers readers easy access into the area of data analysis for complex cyber-security applications, with a particular focus on temporal and ...
Methods and Models in Statistics
John Nelder is one of today''s leading statisticians, having made an impact on many parts of the discipline. This book contains reviews of some of those areas, written by top researchers. It is accessible to non-specialists, and is noteworthy for its breadth of coverage.
Statistical Data Science
As an emerging discipline, data science broadly means different things across different areas. Exploring the relationship of data science with statistics, a well-established and principled data-analytic discipline, this book provides insights about commonalities in approach, and differences in emphasis. Featuring chapters from established authors in both disciplines, the book also presents a number of applications and accompanying papers. remove
Advances in Intelligent Data Analysis IX
Annotation. This book constitutes the refereed proceedings of the 9th International Conference on Intelligent Data Analysis, IDA 2010, held in Tucson, AZ, USA in May 2010. The 21 revised papers presented together with 2 invited papers were carefully reviewed and selected from more than 40 submissions. All current aspects of intelligent data analysis are addressed, particularly intelligent support for modeling and analyzing complex, dynamical systems. Topics covered are end-to-end software systems; modeling complex systems such as gene regulatory networks, economic systems, ecological systems, resources such as water, and dynamical social systems such as online communities; and robustness, scaling properties and other usability issues.
Advances in Intelligent Data Analysis VIII
This book constitutes the refereed proceedings of the 8th International Conference on Intelligent Data Analysis, IDA 2009, held in Lyon, France, August 31 – September 2, 2009. The 33 revised papers, 18 full oral presentations and 15 poster and short oral presentations, presented were carefully reviewed and selected from almost 80 submissions. All current aspects of this interdisciplinary field are addressed; for example interactive tools to guide and support data analysis in complex scenarios, increasing availability of automatically collected data, tools that intelligently support and assist human analysts, how to control clustering results and isotonic classification trees. In general the areas covered include statistics, machine learning, data mining, classification and pattern recognition, clustering, applications, modeling, and interactive dynamic data visualization.
Pattern Detection and Discovery
The collation of large electronic databases of scienti?c and commercial infor- tion has led to a dramatic growth of interest in methods for discovering struc- res in such databases. These methods often go under the general name of data mining. One important subdiscipline within data mining is concerned with the identi?cation and detection of anomalous, interesting, unusual, or valuable - cords or groups of records, which we call patterns. Familiar examples are the detection of fraud in credit-card transactions, of particular coincident purchases in supermarket transactions, of important nucleotide sequences in gene sequence analysis, and of characteristic traces in EEG records. Tools for the...
Advances in Nuclear Architecture
This book provides a snapshot of the state-of-the art in the study of mammalian cell nuclear architecture, and features a diverse range of chapters written by top researchers. A key aspect is an emphasis on precise and repeatable quantitative analysis and simulation in addition to the more familiar biological perspective. The fusion of such material frames the future of the discipline. Quantitative contributions stress reproducible and robust 3D analysis, using a variety of tools ranging from point pattern analysis to shape registration methods. Biological insights include the role of nuclear subdomains in cancer, nuclear molecular motors, and a holistic view of gene transcription.
