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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.
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 network aspects.Chapters can be read as standalone sections and provide rich reviews of the latest research within the field of cyber-security. Academic readers will benefit from state-of-the-art descriptions of new methodologies and their extension to real practical problems while industry professionals will appreciate access to more advanced methodology than ever before.
Open Source Investigations In The Age Of Google
How did a journalist find out who was responsible for bombing hospitals in Syria from his desk in New York? How can South Sudanese monitors safely track and detail the weapons in their communities and make sure that global audiences take notice? How do researchers in London coordinate worldwide work uncovering global corruption? What are policy-makers, lawyers, and intelligence agencies doing to keep up with and make use of these activities?In the age of Google, threats to human security are being tracked in completely new ways. Human rights abuses, political violence, nuclear weapons, corruption, radicalization, and conflict are all being monitored, analyzed, and documented. Although open s...
Using Science In Cybersecurity
Deploying the scientific method in cybersecurity today is a common-sense approach that is a tough topic in the field of cybersecurity. While most publications in the field emphasize that scientific principles are necessary, there are very few, if any, guides that uncover these principles.This book will give readers practical tools for cybersecurity. It examines the path of developing cybersecurity foundations while taking into account uncertain data. Extensive examples demonstrate how to deploy cybersecurity to sort our day-to-day problems. Using Science in Cybersecurity is intended for advanced undergraduate and graduate students, researchers and practitioners in the fields of cybersecurity, information security, and science of cybersecurity.
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,...
Essentials of Statistical Inference
Aimed at advanced undergraduates and graduate students in mathematics and related disciplines, this engaging textbook gives a concise account of the main approaches to inference, with particular emphasis on the contrasts between them. It is the first textbook to synthesize contemporary material on computational topics with basic mathematical theory.
Gender Representation in Learning Materials
Representations of gender in learning materials convey an implicit message to students about attitudes towards culturally appropriate gender roles for women and men. This collection takes a linguistic approach to exploring theories about gender representation within the sphere of education and textbooks, and their effects on readers and students within an international context. In the opening section, contributors discuss theories of representation and effect, challenging the conventional Althusserian model of interpellation, and acknowledging the challenges of applying Western feminist models within an international context. Following chapters provide detailed analyses focusing on a number ...
Statistical Mechanics of Complex Networks
Networks can provide a useful model and graphic image useful for the description of a wide variety of web-like structures in the physical and man-made realms, e.g. protein networks, food webs and the Internet. The contributions gathered in the present volume provide both an introduction to, and an overview of, the multifaceted phenomenology of complex networks. Statistical Mechanics of Complex Networks also provides a state-of-the-art picture of current theoretical methods and approaches.
Federated Learning
This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated lear...
Theory of Statistics
The aim of this graduate textbook is to provide a comprehensive advanced course in the theory of statistics covering those topics in estimation, testing, and large sample theory which a graduate student might typically need to learn as preparation for work on a Ph.D. An important strength of this book is that it provides a mathematically rigorous and even-handed account of both Classical and Bayesian inference in order to give readers a broad perspective. For example, the "uniformly most powerful" approach to testing is contrasted with available decision-theoretic approaches.
