This book contains original materials by leading researchers in the area and covers applications of different machine learning methods in the reliability, security, performance, and privacy issues of cyber space.
Author: Jeffrey J. P. Tsai
Publisher: Springer Science & Business Media
Many networked computer systems are far too vulnerable to cyber attacks that can inhibit their functioning, corrupt important data, or expose private information. Not surprisingly, the field of cyber-based systems is a fertile ground where many tasks can be formulated as learning problems and approached in terms of machine learning algorithms. This book contains original materials by leading researchers in the area and covers applications of different machine learning methods in the reliability, security, performance, and privacy issues of cyber space. It enables readers to discover what types of learning methods are at their disposal, summarizing the state-of-the-practice in this significant area, and giving a classification of existing work. Those working in the field of cyber-based systems, including industrial managers, researchers, engineers, and graduate and senior undergraduate students will find this an indispensable guide in creating systems resistant to and tolerant of cyber attacks.
Misleading learners: Coopting your spam filter. In Machine learning in cyber trust,
17–51. Springer. Norton, Andrew P., and Yanjun Qi. 2017. Adversarial-
playground: A visualization suite showing how adversarial examples fool deep
Author: Tony Thomas
Publisher: Springer Nature
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.
Mitchell, T. M. (2006), The discipline of machine learning, Technical Report CMU-
ML-06-108, Carnegie Mellon ... your spam filter, in J. J. P. Tsai & P. S. Yu, eds., Machine Learning in Cyber Trust: Security, Privacy, Reliability, Springer, pp.
Author: Anthony D. Joseph
Publisher: Cambridge University Press
This study allows readers to get to grips with the conceptual tools and practical techniques for building robust machine learning in the face of adversaries.
D. Zhang and J. J. P. Tsai, Advances in Machine Learning Applications in
Software Engineering, IGI Publishing Inc., PA, 2007. J. J. P. Tsai and P. S. Yu, Machine Learning in Cyber Trust: Security, Privacy, Reliability, Springer, New
Author: Zhenwei Yu
Publisher: World Scientific
This important book introduces the concept of intrusion detection, discusses various approaches for intrusion detection systems (IDS), and presents the architecture and implementation of IDS. It emphasizes on the prediction and learning algorithms for intrusion detection and highlights techniques for intrusion detection of wired computer networks and wireless sensor networks. The performance comparison of various IDS via simulation will also be included. Contents: Attacks and Countermeasures in Computer SecurityMachine Learning MethodsIntrusion Detection SystemTechniques for Intrusion DetectionAdaptive Automatically Tuning Intrusion Detection SystemSystem Prototype and Performance EvaluationAttacks Against Wireless Sensor NetworkIntrusion Detection System for Wireless Sensor NetworkConclusion and Future Research Readership: Academicians, researchers and graduate students in software engineering/programming; computer engineering, knowledge and system engineering. Keywords:Intrusion;Detection;Machine Learning;Computer Network;Sensor Network;Computer SecurityKey Features:Discusses attacks and countermeasures in computer securityPresents state-of-the-art intrusion detection researchDescribes adaptive automatically tuning intrusion detection for wired networks
Cyber-security and cyber-trust are two issues that are likely to dominate research
in the next decade. To address these ... our cyber-society. The rest of the book is
on the use of machine learning methods and tools and their performance.
Author: V. Rao Vemuri
Publisher: CRC Press
Divided into two major parts, Enhancing Computer Security with Smart Technology introduces the problems of computer security to researchers with a machine learning background, then introduces machine learning concepts to computer security professionals. Realizing the massive scope of these subjects, the author concentrates on problems related to the detection of intrusions through the application of machine learning methods and on the practical algorithmic aspects of machine learning and its role in security. A collection of tutorials that draw from a broad spectrum of viewpoints and experience, this volume is made up of chapters written by specialists in each subject field. It is accessible to any professional with a basic background in computer science. Following an introduction to the issue of cyber-security and cyber-trust, the book offers a broad survey of the state-of-the-art in firewall technology and of the importance of Web application security. The remainder of the book focuses on the use of machine learning methods and tools and their performance.
Cyber. Attacks. in. Cloud. Systems. Using. Machine. Learning. Md Tanzim
Khorshed, A. B. M. Shawkat Ali and Saleh A. ... One of the crucial but complicated
tasks is to detect cyber attacks and their types in any IT networking environment ...
Author: Surya Nepal
Publisher: Springer Science & Business Media
Category: Technology & Engineering
The book compiles technologies for enhancing and provisioning security, privacy and trust in cloud systems based on Quality of Service requirements. It is a timely contribution to a field that is gaining considerable research interest, momentum, and provides a comprehensive coverage of technologies related to cloud security, privacy and trust. In particular, the book includes - Cloud security fundamentals and related technologies to-date, with a comprehensive coverage of evolution, current landscape, and future roadmap. - A smooth organization with introductory, advanced and specialist content, i.e. from basics of security, privacy and trust in cloud systems, to advanced cartographic techniques, case studies covering both social and technological aspects, and advanced platforms. - Case studies written by professionals and/or industrial researchers. - Inclusion of a section on Cloud security and eGovernance tutorial that can be used for knowledge transfer and teaching purpose. - Identification of open research issues to help practitioners and researchers. The book is a timely topic for readers, including practicing engineers and academics, in the domains related to the engineering, science, and art of building networks and networked applications. Specifically, upon reading this book, audiences will perceive the following benefits: 1. Learn the state-of-the-art in research and development on cloud security, privacy and trust. 2. Obtain a future roadmap by learning open research issues. 3. Gather the background knowledge to tackle key problems, whose solutions will enhance the evolution of next-generation secure cloud systems.
... matching algorithms for intrusion detection . In Proc . of the IEEE Infocom ,
pages 333–340 . ( 16 ) B. et al . Nelson . Misleading learners : Co - opting your
spam filter . Tsai , J. and Yu , P. ( eds . ) Machine Learning and Cyber Trust , 8 :
This book constitutes the proceedings of the first International Symposium on Cyber Security Cryptography and Machine Learning, held in Beer-Sheva, Israel, in June 2017.
Author: Shlomi Dolev
This book constitutes the proceedings of the first International Symposium on Cyber Security Cryptography and Machine Learning, held in Beer-Sheva, Israel, in June 2017. The 17 full and 4 short papers presented include cyber security; secure software development methodologies, formal methods semantics and verification of secure systems; fault tolerance, reliability, availability of distributed secure systems; game-theoretic approaches to secure computing; automatic recovery of self-stabilizing and self-organizing systems; communication, authentication and identification security; cyber security for mobile and Internet of things; cyber security of corporations; security and privacy for cloud, edge and fog computing; cryptography; cryptographic implementation analysis and construction; secure multi-party computation; privacy-enhancing technologies and anonymity; post-quantum cryptography and security; machine learning and big data; anomaly detection and malware identification; business intelligence and security; digital forensics; digital rights management; trust management and reputation systems; information retrieval, risk analysis, DoS.
'Cybersecurity: The 'Zero-Trust' Movement. Cybersecurity Skills ... 'Machine Learning: Practical Applications for Cybersecurity'. Available at: https://www.
recordedfuture.com/machine-learning-cybersecurity-applications/ NormShield. ' Cyber ...
Author: Rodney D Ryder
Publisher: Bloomsbury Publishing
Category: Business & Economics
With the advent of big data technology, organisations worldwide are creating data exceeding terabytes in size. Due to the variety of data that it encompasses, big data always entails a number of challenges related to its volume, complexity and vulnerability. The need to manage cyber risks across an enterprise-inclusive of IT operations-is a growing concern as massive data breaches make news on an alarmingly frequent basis. The internet too has grown enormously over the past few years, consequently increasing the risk of many untoward cyber incidents that can cause irreparable loss to a corporate organisation. With a robust cyber risk management system now a necessary business requirement, organisations need to assess the effectiveness of their current systems in response to a dynamic and fast-moving threat landscape. This book goes beyond a mere response to cybercrime and addresses the entire crisis-management cycle. The authors have created a primer for corporate houses and individuals alike on how they should deal with cyber incidences and develop strategies on tackling such incidences.
This volume provides an abundance of valuable information for professionals and researchers working in the field of business analytics, big data, social network data, computer science, analytical engineering, and forensic analysis.
Author: Gulshan Shrivastava
Publisher: Apple Academic Press Incorporated
This comprehensive and timely book, New Age Analytics: Transforming the Internet through Machine Learning, IoT, and Trust Modeling, explores the importance of tools and techniques used in machine learning, big data mining, and more. The book explains how advancements in the world of the web have been achieved and how the experiences of users can be analyzed. It looks at data gathering by the various electronic means and explores techniques for analysis and management, how to manage voluminous data, user responses, and more. This volume provides an abundance of valuable information for professionals and researchers working in the field of business analytics, big data, social network data, computer science, analytical engineering, and forensic analysis. Moreover, the book provides insights and support from both practitioners and academia in order to highlight the most debated aspects in the field.
This book constitutes the refereed proceedings of the 17th International Conference on Trust, Privacy and Security in Digital Business, TrustBus 2020, held in Bratislava, Slovakia, in September 2020.
Author: Stefanos Gritzalis
Publisher: Springer Nature
This book constitutes the refereed proceedings of the 17th International Conference on Trust, Privacy and Security in Digital Business, TrustBus 2020, held in Bratislava, Slovakia, in September 2020. The conference was held virtually due to the COVID-19 pandemic. The 11 full and 4 short papers presented were carefully reviewed and selected from 28 submissions. The papers are organized in the following topical sections: blockchain, cloud security/hardware; economics/privacy; human aspects; privacy; privacy and machine learning; trust.
This research was supported in part by the Team for Research in Ubiquitous
Secure Technology ( TRUST ) , which ... in part by the cyber - DEfense
Technology Experimental Research laboratory ( DETERlab ) , which receives
support from the ...
Author: Marco Antonio Barreno
Two far-reaching trends in computing have grown in significance in recent years. First, statistical machine learning has entered the mainstream as a broadly useful tool set for building applications. Second, the need to protect systems against malicious adversaries continues to increase across computing applications. The growing intersection of these trends compels us to investigate how well machine learning performs under adversarial conditions. When a learning algorithm succeeds in adversarial conditions, it is an algorithm for secure learning. The crucial task is to evaluate the resilience of learning systems and determine whether they satisfy requirements for secure learning. In this thesis, we show that the space of attacks against machine learning has a structure that we can use to build secure learning systems. This thesis makes three high-level contributions. First, we develop a framework for analyzing attacks against machine learning systems. We present a taxonomy that describes the space of attacks against learning systems, and we model such attacks as a cost-sensitive game between the attacker and the defender. We survey attacks in the literature and describe them in terms of our taxonomy. Second, we develop two concrete attacks against a popular machine learning spam filter and present experimental results confirming their effectiveness. These attacks demonstrate that real systems using machine learning are vulnerable to compromise. Third, we explore defenses against attacks with both a high-level discussion of defenses within our taxonomy and a multi-level defense against attacks in the domain of virus detection. Using both global and local information, our virus defense successfully captures many viruses designed to evade detection. Our framework, exploration of attacks, and discussion of defenses provides a strong foundation for constructing secure learning systems.
Cybersecurity is most often, in practice, reactive. Based on the manual forensics of machine-generated data by humans, security efforts only begin after a loss has taken place. The current security paradigm can be significantly improved.
Author: Isaac Justin Faber
This research presents a warning systems model in which early-stage cyber threat signals are generated using machine learning and artificial intelligence (AI) techniques. Cybersecurity is most often, in practice, reactive. Based on the manual forensics of machine-generated data by humans, security efforts only begin after a loss has taken place. The current security paradigm can be significantly improved. Cyber-threat behaviors can be modeled as a set of discrete, observable steps called a 'kill chain.' Data produced from observing early kill chain steps can support the automation of manual defensive responses before an attack causes losses. However, early AI-based approaches to cybersecurity have been sensitive to exploitation and overly burdensome false positive rates resulting in low adoption and low trust from human experts. To address the problem, this research presents a collaborative decision paradigm with machines making low-impact/high-confidence decisions based on human risk preferences and uncertainty thresholds. Human experts only evaluate signals generated by the AI when decisions exceed these thresholds. This approach unifies core concepts from the disciplines of decision analysis and machine learning by creating a super-agent. An early warning system using these techniques has the potential to avoid more severe downstream consequences by disrupting threats at the beginning of the kill chain.
Author: Abu-Taieh, Evon M. O.Publish On: 2009-03-31
Goldberg D.E. (1989) Genetic algorithms in search optimization and machine learning. Addison-Wesley Publishers. ... Contract as a Source of Trust -
Commitment in Successful IT Outsourcing Relationship: An Empirical Study.
Proceedings of the 40th ... The Law and Economics of Cyber Security: An
Introduction. Cambridge ...
Author: Abu-Taieh, Evon M. O.
Publisher: IGI Global
Provides original material concerned with all aspects of information resources management, managerial and organizational applications, as well as implications of information technology.
Author: Deepthi Hassan LakshminarayanaPublish On: 2019
With the growing rate of cyber-attacks, there is a significant need for intrusion detection systems (IDS) in networked environments.
Author: Deepthi Hassan Lakshminarayana
With the growing rate of cyber-attacks, there is a significant need for intrusion detection systems (IDS) in networked environments. As intrusion tactics become more sophisticated and more challenging to detect, this necessitates improved intrusion detection technology to retain user trust and preserve network security. Over the last decade, several detection methodologies have been designed to provide users with reliability, privacy, and information security. The first half of this thesis surveys the literature on intrusion detection techniques based on machine learning, deep learning, and blockchain technology from 2009 to 2018. The survey identifies applications, drawbacks, and challenges of these three intrusion detection methodologies that identify threats in computer network environments. The second half of this thesis proposes a new machine learning Model for intrusion detection that employs random forest, naive Bayes, and decision tree algorithms. We evaluate its performance on a standard dataset of simulated network attacks used in the literature, NSL-KDD. We discuss preprocessing of the dataset and feature selection for training our hybrid model and report its performance using standard metrics such as accuracy, precision, recall, and f-measure. In the final part of the thesis, we evaluate our intrusion model against the performance of existing machine learning models for intrusion detection reported in the literature. Our model predicts the Denial of Service (DOS) attack using a random forest classifier with 99.81% accuracy, Probe attack with 97.89% accuracy, and R2L attack with 97.92% accuracy achieving equivalent or superior performance in comparison with the existing models.
This handbook introduces the basic principles and fundamentals of cyber security towards establishing an understanding of how to protect computers from hackers and adversaries.
Author: Brij B. Gupta
Publisher: Springer Nature
This handbook introduces the basic principles and fundamentals of cyber security towards establishing an understanding of how to protect computers from hackers and adversaries. The highly informative subject matter of this handbook, includes various concepts, models, and terminologies along with examples and illustrations to demonstrate substantial technical details of the field. It motivates the readers to exercise better protection and defense mechanisms to deal with attackers and mitigate the situation. This handbook also outlines some of the exciting areas of future research where the existing approaches can be implemented. Exponential increase in the use of computers as a means of storing and retrieving security-intensive information, requires placement of adequate security measures to safeguard the entire computing and communication scenario. With the advent of Internet and its underlying technologies, information security aspects are becoming a prime concern towards protecting the networks and the cyber ecosystem from variety of threats, which is illustrated in this handbook. This handbook primarily targets professionals in security, privacy and trust to use and improve the reliability of businesses in a distributed manner, as well as computer scientists and software developers, who are seeking to carry out research and develop software in information and cyber security. Researchers and advanced-level students in computer science will also benefit from this reference.
2123 : P . Perner ( Ed . ) , Machine Learning and Data Mining in Pattern
Recognition . ... 2167 : L . De Raedt , P . Flach ( Eds . ) , Machine Learning :
ECML 2001 . ... R . Falcone , M . Singh , Y . - H . Tan ( Eds . ) , Trust in Cyber -