Author: Yihua Liao
Publisher:
ISBN:
Category :
Languages : en
Pages : 230
Book Description
Detection of anomalies in data is one of the fundamental machine learning tasks. Anomaly detection provides the core technology for a broad spectrum of security-centric applications. In this dissertation, we examine various aspects of anomaly based intrusion detection in computer security. First, we present a new approach to learn program behavior for intrusion detection. Text categorization techniques are adopted to convert each process to a vector and calculate the similarity between two program activities. Then the k-nearest neighbor classifier is employed to classify program behavior as normal or intrusive. We demonstrate that our approach is able to effectively detect intrusive program behavior while a low false positive rate is achieved. Second, we describe an adaptive anomaly detection framework that is de- signed to handle concept drift and online learning for dynamic, changing environments. Through the use of unsupervised evolving connectionist systems, normal behavior changes are efficiently accommodated while anomalous activities can still be recognized. We demonstrate the performance of our adaptive anomaly detection systems and show that the false positive rate can be significantly reduced.
Machine Learning in Intrusion Detection
Knowledge-Based Systems
Author: Rajendra Akerkar
Publisher: Jones & Bartlett Publishers
ISBN: 1449662706
Category : Computers
Languages : en
Pages : 375
Book Description
A knowledge-based system (KBS) is a system that uses artificial intelligence techniques in problem-solving processes to support human decision-making, learning, and action. Ideal for advanced-undergraduate and graduate students, as well as business professionals, this text is designed to help users develop an appreciation of KBS and their architecture and understand a broad variety of knowledge-based techniques for decision support and planning. It assumes basic computer science skills and a math background that includes set theory, relations, elementary probability, and introductory concepts of artificial intelligence. Each of the 12 chapters is designed to be modular, providing instructors with the flexibility to model the book to their own course needs. Exercises are incorporated throughout the text to highlight certain aspects of the material presented and to simulate thought and discussion. A comprehensive text and resource, Knowledge-Based Systems provides access to the most current information in KBS and new artificial intelligences, as well as neural networks, fuzzy logic, genetic algorithms, and soft systems.
Publisher: Jones & Bartlett Publishers
ISBN: 1449662706
Category : Computers
Languages : en
Pages : 375
Book Description
A knowledge-based system (KBS) is a system that uses artificial intelligence techniques in problem-solving processes to support human decision-making, learning, and action. Ideal for advanced-undergraduate and graduate students, as well as business professionals, this text is designed to help users develop an appreciation of KBS and their architecture and understand a broad variety of knowledge-based techniques for decision support and planning. It assumes basic computer science skills and a math background that includes set theory, relations, elementary probability, and introductory concepts of artificial intelligence. Each of the 12 chapters is designed to be modular, providing instructors with the flexibility to model the book to their own course needs. Exercises are incorporated throughout the text to highlight certain aspects of the material presented and to simulate thought and discussion. A comprehensive text and resource, Knowledge-Based Systems provides access to the most current information in KBS and new artificial intelligences, as well as neural networks, fuzzy logic, genetic algorithms, and soft systems.
13th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2020)
Author: Álvaro Herrero
Publisher: Springer Nature
ISBN: 3030578054
Category : Technology & Engineering
Languages : en
Pages : 477
Book Description
This book contains accepted papers presented at CISIS 2020 held in the beautiful and historic city of Burgos (Spain), in September 2020. The aim of the CISIS 2020 conference is to offer a meeting opportunity for academic and industry-related researchers belonging to the various, vast communities of computational intelligence, information security, and data mining. The need for intelligent, flexible behaviour by large, complex systems, especially in mission-critical domains, is intended to be the catalyst and the aggregation stimulus for the overall event. After a thorough peer-review process, the CISIS 2020 International Program Committee selected 43 papers which are published in these conference proceedings achieving an acceptance rate of 28%. Due to the COVID-19 outbreak, the CISIS 2020 edition was blended, combining on-site and on-line participation. In this relevant edition, a special emphasis was put on the organization of five special sessions related to relevant topics as Fake News Detection and Prevention, Mathematical Methods and Models in Cybersecurity, Measurements for a Dynamic Cyber-Risk Assessment, Cybersecurity in a Hybrid Quantum World, Anomaly/Intrusion Detection, and From the least to the least: cryptographic and data analytics solutions to fulfil least minimum privilege and endorse least minimum effort in information systems. The selection of papers was extremely rigorous in order to maintain the high quality of the conference and we would like to thank the members of the Program Committees for their hard work in the reviewing process. This is a crucial process to the creation of a high standard conference, and the CISIS conference would not exist without their help.
Publisher: Springer Nature
ISBN: 3030578054
Category : Technology & Engineering
Languages : en
Pages : 477
Book Description
This book contains accepted papers presented at CISIS 2020 held in the beautiful and historic city of Burgos (Spain), in September 2020. The aim of the CISIS 2020 conference is to offer a meeting opportunity for academic and industry-related researchers belonging to the various, vast communities of computational intelligence, information security, and data mining. The need for intelligent, flexible behaviour by large, complex systems, especially in mission-critical domains, is intended to be the catalyst and the aggregation stimulus for the overall event. After a thorough peer-review process, the CISIS 2020 International Program Committee selected 43 papers which are published in these conference proceedings achieving an acceptance rate of 28%. Due to the COVID-19 outbreak, the CISIS 2020 edition was blended, combining on-site and on-line participation. In this relevant edition, a special emphasis was put on the organization of five special sessions related to relevant topics as Fake News Detection and Prevention, Mathematical Methods and Models in Cybersecurity, Measurements for a Dynamic Cyber-Risk Assessment, Cybersecurity in a Hybrid Quantum World, Anomaly/Intrusion Detection, and From the least to the least: cryptographic and data analytics solutions to fulfil least minimum privilege and endorse least minimum effort in information systems. The selection of papers was extremely rigorous in order to maintain the high quality of the conference and we would like to thank the members of the Program Committees for their hard work in the reviewing process. This is a crucial process to the creation of a high standard conference, and the CISIS conference would not exist without their help.
The Nature of Statistical Learning Theory
Author: Vladimir Vapnik
Publisher: Springer Science & Business Media
ISBN: 1475732643
Category : Mathematics
Languages : en
Pages : 324
Book Description
The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.
Publisher: Springer Science & Business Media
ISBN: 1475732643
Category : Mathematics
Languages : en
Pages : 324
Book Description
The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.
Deep Learning Applications for Cyber Security
Author: Mamoun Alazab
Publisher: Springer
ISBN: 3030130576
Category : Computers
Languages : en
Pages : 260
Book Description
Cybercrime remains a growing challenge in terms of security and privacy practices. Working together, deep learning and cyber security experts have recently made significant advances in the fields of intrusion detection, malicious code analysis and forensic identification. This book addresses questions of how deep learning methods can be used to advance cyber security objectives, including detection, modeling, monitoring and analysis of as well as defense against various threats to sensitive data and security systems. Filling an important gap between deep learning and cyber security communities, it discusses topics covering a wide range of modern and practical deep learning techniques, frameworks and development tools to enable readers to engage with the cutting-edge research across various aspects of cyber security. The book focuses on mature and proven techniques, and provides ample examples to help readers grasp the key points.
Publisher: Springer
ISBN: 3030130576
Category : Computers
Languages : en
Pages : 260
Book Description
Cybercrime remains a growing challenge in terms of security and privacy practices. Working together, deep learning and cyber security experts have recently made significant advances in the fields of intrusion detection, malicious code analysis and forensic identification. This book addresses questions of how deep learning methods can be used to advance cyber security objectives, including detection, modeling, monitoring and analysis of as well as defense against various threats to sensitive data and security systems. Filling an important gap between deep learning and cyber security communities, it discusses topics covering a wide range of modern and practical deep learning techniques, frameworks and development tools to enable readers to engage with the cutting-edge research across various aspects of cyber security. The book focuses on mature and proven techniques, and provides ample examples to help readers grasp the key points.
Recent Advances in Intrusion Detection
Author: Herve Debar
Publisher: Springer Science & Business Media
ISBN: 3540410856
Category : Business & Economics
Languages : en
Pages : 237
Book Description
This book constitutes the refereed proceedings of the Third International Workshop on Recent Advances in Intrusion Detection, RAID 2000, held in Toulouse, France in October 2000. The 14 revised full papers presented were carefully reviewed and selected from a total of 26 papers and 30 extended abstracts submitted. The papers are organized in sections on logging, data mining, modeling process behaviour, IDS evaluation, and modeling.
Publisher: Springer Science & Business Media
ISBN: 3540410856
Category : Business & Economics
Languages : en
Pages : 237
Book Description
This book constitutes the refereed proceedings of the Third International Workshop on Recent Advances in Intrusion Detection, RAID 2000, held in Toulouse, France in October 2000. The 14 revised full papers presented were carefully reviewed and selected from a total of 26 papers and 30 extended abstracts submitted. The papers are organized in sections on logging, data mining, modeling process behaviour, IDS evaluation, and modeling.
2020 5th International Conference on Communication and Electronics Systems (ICCES)
Author: IEEE Staff
Publisher:
ISBN: 9781728153728
Category :
Languages : en
Pages :
Book Description
5th International Conference on Communication and Electronics Systems (ICCES 2020) is being organized on 10 12, June 2020 ICCES will provide an outstanding international forum for sharing knowledge and results in all fields of Engineering and Technology ICCES provides quality key experts who provide an opportunity in bringing up innovative ideas Recent updates in the in the field of technology will be a platform for the upcoming researchers The conference will be Complete, Concise, Clear and Cohesive in terms of research related to Communication and Electronics systems
Publisher:
ISBN: 9781728153728
Category :
Languages : en
Pages :
Book Description
5th International Conference on Communication and Electronics Systems (ICCES 2020) is being organized on 10 12, June 2020 ICCES will provide an outstanding international forum for sharing knowledge and results in all fields of Engineering and Technology ICCES provides quality key experts who provide an opportunity in bringing up innovative ideas Recent updates in the in the field of technology will be a platform for the upcoming researchers The conference will be Complete, Concise, Clear and Cohesive in terms of research related to Communication and Electronics systems
Machine Learning Techniques and Analytics for Cloud Security
Author: Rajdeep Chakraborty
Publisher: John Wiley & Sons
ISBN: 1119764092
Category : Computers
Languages : en
Pages : 484
Book Description
MACHINE LEARNING TECHNIQUES AND ANALYTICS FOR CLOUD SECURITY This book covers new methods, surveys, case studies, and policy with almost all machine learning techniques and analytics for cloud security solutions The aim of Machine Learning Techniques and Analytics for Cloud Security is to integrate machine learning approaches to meet various analytical issues in cloud security. Cloud security with ML has long-standing challenges that require methodological and theoretical handling. The conventional cryptography approach is less applied in resource-constrained devices. To solve these issues, the machine learning approach may be effectively used in providing security to the vast growing cloud environment. Machine learning algorithms can also be used to meet various cloud security issues, such as effective intrusion detection systems, zero-knowledge authentication systems, measures for passive attacks, protocols design, privacy system designs, applications, and many more. The book also contains case studies/projects outlining how to implement various security features using machine learning algorithms and analytics on existing cloud-based products in public, private and hybrid cloud respectively. Audience Research scholars and industry engineers in computer sciences, electrical and electronics engineering, machine learning, computer security, information technology, and cryptography.
Publisher: John Wiley & Sons
ISBN: 1119764092
Category : Computers
Languages : en
Pages : 484
Book Description
MACHINE LEARNING TECHNIQUES AND ANALYTICS FOR CLOUD SECURITY This book covers new methods, surveys, case studies, and policy with almost all machine learning techniques and analytics for cloud security solutions The aim of Machine Learning Techniques and Analytics for Cloud Security is to integrate machine learning approaches to meet various analytical issues in cloud security. Cloud security with ML has long-standing challenges that require methodological and theoretical handling. The conventional cryptography approach is less applied in resource-constrained devices. To solve these issues, the machine learning approach may be effectively used in providing security to the vast growing cloud environment. Machine learning algorithms can also be used to meet various cloud security issues, such as effective intrusion detection systems, zero-knowledge authentication systems, measures for passive attacks, protocols design, privacy system designs, applications, and many more. The book also contains case studies/projects outlining how to implement various security features using machine learning algorithms and analytics on existing cloud-based products in public, private and hybrid cloud respectively. Audience Research scholars and industry engineers in computer sciences, electrical and electronics engineering, machine learning, computer security, information technology, and cryptography.
Introduction to Neural Networks with Java
Author: Jeff Heaton
Publisher: Heaton Research Incorporated
ISBN: 097732060X
Category : Computers
Languages : en
Pages : 380
Book Description
In addition to showing the programmer how to construct Neural Networks, the book discusses the Java Object Oriented Neural Engine (JOONE), a free open source Java neural engine. (Computers)
Publisher: Heaton Research Incorporated
ISBN: 097732060X
Category : Computers
Languages : en
Pages : 380
Book Description
In addition to showing the programmer how to construct Neural Networks, the book discusses the Java Object Oriented Neural Engine (JOONE), a free open source Java neural engine. (Computers)
Handbook of Research on Machine and Deep Learning Applications for Cyber Security
Author: Ganapathi, Padmavathi
Publisher: IGI Global
ISBN: 1522596135
Category : Computers
Languages : en
Pages : 506
Book Description
As the advancement of technology continues, cyber security continues to play a significant role in todays world. With society becoming more dependent on the internet, new opportunities for virtual attacks can lead to the exposure of critical information. Machine and deep learning techniques to prevent this exposure of information are being applied to address mounting concerns in computer security. The Handbook of Research on Machine and Deep Learning Applications for Cyber Security is a pivotal reference source that provides vital research on the application of machine learning techniques for network security research. While highlighting topics such as web security, malware detection, and secure information sharing, this publication explores recent research findings in the area of electronic security as well as challenges and countermeasures in cyber security research. It is ideally designed for software engineers, IT specialists, cybersecurity analysts, industrial experts, academicians, researchers, and post-graduate students.
Publisher: IGI Global
ISBN: 1522596135
Category : Computers
Languages : en
Pages : 506
Book Description
As the advancement of technology continues, cyber security continues to play a significant role in todays world. With society becoming more dependent on the internet, new opportunities for virtual attacks can lead to the exposure of critical information. Machine and deep learning techniques to prevent this exposure of information are being applied to address mounting concerns in computer security. The Handbook of Research on Machine and Deep Learning Applications for Cyber Security is a pivotal reference source that provides vital research on the application of machine learning techniques for network security research. While highlighting topics such as web security, malware detection, and secure information sharing, this publication explores recent research findings in the area of electronic security as well as challenges and countermeasures in cyber security research. It is ideally designed for software engineers, IT specialists, cybersecurity analysts, industrial experts, academicians, researchers, and post-graduate students.