Hybrid Intrusion Detection System for Contemporary Network Intrusion Dataset PDF Download
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Author: 廖證模
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: 廖證模
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Álvaro Herrero
Publisher: Springer Science & Business Media
ISBN: 3642182984
Category : Computers
Languages : en
Pages : 151
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Book Description
This monograph comprises work on network-based Intrusion Detection (ID) that is grounded in visualisation and hybrid Artificial Intelligence (AI). It has led to the design of MOVICAB-IDS (MObile VIsualisation Connectionist Agent-Based IDS), a novel Intrusion Detection System (IDS), which is comprehensively described in this book. This novel IDS combines different AI paradigms to visualise network traffic for ID at packet level. It is based on a dynamic Multiagent System (MAS), which integrates an unsupervised neural projection model and the Case-Based Reasoning (CBR) paradigm through the use of deliberative agents that are capable of learning and evolving with the environment. The proposed novel hybrid IDS provides security personnel with a synthetic, intuitive snapshot of network traffic and protocol interactions. This visualisation interface supports the straightforward detection of anomalous situations and their subsequent identification. The performance of MOVICAB-IDS was tested through a novel mutation-based testing method in different real domains which entailed several attacks and anomalous situations.
Author: Álvaro Herrero
Publisher: Springer Nature
ISBN: 3030578054
Category : Technology & Engineering
Languages : en
Pages : 477
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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.
Author: Kwangjo Kim
Publisher: Springer
ISBN: 9811314446
Category : Computers
Languages : en
Pages : 92
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Book Description
This book presents recent advances in intrusion detection systems (IDSs) using state-of-the-art deep learning methods. It also provides a systematic overview of classical machine learning and the latest developments in deep learning. In particular, it discusses deep learning applications in IDSs in different classes: generative, discriminative, and adversarial networks. Moreover, it compares various deep learning-based IDSs based on benchmarking datasets. The book also proposes two novel feature learning models: deep feature extraction and selection (D-FES) and fully unsupervised IDS. Further challenges and research directions are presented at the end of the book. Offering a comprehensive overview of deep learning-based IDS, the book is a valuable reerence resource for undergraduate and graduate students, as well as researchers and practitioners interested in deep learning and intrusion detection. Further, the comparison of various deep-learning applications helps readers gain a basic understanding of machine learning, and inspires applications in IDS and other related areas in cybersecurity.
Author: Zhenwei Yu
Publisher: World Scientific
ISBN: 1848164475
Category : Computers
Languages : en
Pages : 185
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Book Description
Introduces the concept of intrusion detection, discusses various approaches for intrusion detection systems (IDS), and presents the architecture and implementation of IDS. This title also includes the performance comparison of various IDS via simulation.
Author: Asghar Ali Shah
Publisher: Infinite Study
ISBN:
Category :
Languages : en
Pages : 11
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Book Description
Security is a key issue to both computer and computer networks. Intrusion detection System (IDS) is one of the major research problems in network security. IDSs are developed to detect both known and unknown attacks. There are many techniques used in IDS for protecting computers and networks from network based and host based attacks. Various Machine learning techniques are used in IDS. This study analyzes machine learning techniques in IDS. It also reviews many related studies done in the period from 2000 to 2012 and it focuses on machine learning techniques. Related studies include single, hybrid, ensemble classifiers, baseline and datasets used.
Author: Sumeet Dua
Publisher: CRC Press
ISBN: 146650823X
Category : Computers
Languages : en
Pages : 275
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Book Description
With the rapid advancement of information discovery techniques, machine learning and data mining continue to play a significant role in cybersecurity. Although several conferences, workshops, and journals focus on the fragmented research topics in this area, there has been no single interdisciplinary resource on past and current works and possible paths for future research in this area. This book fills this need. From basic concepts in machine learning and data mining to advanced problems in the machine learning domain, Data Mining and Machine Learning in Cybersecurity provides a unified reference for specific machine learning solutions to cybersecurity problems. It supplies a foundation in cybersecurity fundamentals and surveys contemporary challenges—detailing cutting-edge machine learning and data mining techniques. It also: Unveils cutting-edge techniques for detecting new attacks Contains in-depth discussions of machine learning solutions to detection problems Categorizes methods for detecting, scanning, and profiling intrusions and anomalies Surveys contemporary cybersecurity problems and unveils state-of-the-art machine learning and data mining solutions Details privacy-preserving data mining methods This interdisciplinary resource includes technique review tables that allow for speedy access to common cybersecurity problems and associated data mining methods. Numerous illustrative figures help readers visualize the workflow of complex techniques and more than forty case studies provide a clear understanding of the design and application of data mining and machine learning techniques in cybersecurity.
Author: Sheng-Lung Peng
Publisher: Springer
ISBN: 3319764519
Category : Technology & Engineering
Languages : en
Pages : 377
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Book Description
In the dawning era of Intelligent Computing and Big-data Services, security issues will be an important consideration in promoting these new technologies into the future. This book presents the proceedings of the 2017 International Conference on Security with Intelligent Computing and Big-data Services, the Workshop on Information and Communication Security Science and Engineering, and the Workshop on Security in Forensics, Medical, and Computing Services and Applications. The topics addressed include: Algorithms and Security Analysis, Cryptanalysis and Detection Systems, IoT and E-commerce Applications, Privacy and Cloud Computing, Information Hiding and Secret Sharing, Network Security and Applications, Digital Forensics and Mobile Systems, Public Key Systems and Data Processing, and Blockchain Applications in Technology. The conference is intended to promote healthy exchanges between researchers and industry practitioners regarding advances in the state of art of these security issues. The proceedings not only highlight novel and interesting ideas, but will also stimulate interesting discussions and inspire new research directions.
Author: Inam Ullah Khan
Publisher: CRC Press
ISBN: 1040097081
Category : Computers
Languages : en
Pages : 373
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Book Description
Cognitive Machine Intelligence: Applications, Challenges, and Related Technologies offers a compelling exploration of the transformative landscape shaped by the convergence of machine intelligence, artificial intelligence, and cognitive computing. In this book, the authors navigate through the intricate realms of technology, unveiling the profound impact of cognitive machine intelligence on diverse fields such as communication, healthcare, cybersecurity, and smart city development. The chapters present study on robots and drones to the integration of machine learning with wireless communication networks, IoT, quantum computing, and beyond. The book explores the essential role of machine learning in healthcare, security, and manufacturing. With a keen focus on privacy, trust, and the improvement of human lifestyles, this book stands as a comprehensive guide to the novel techniques and applications driving the evolution of cognitive machine intelligence. The vision presented here extends to smart cities, where AI-enabled techniques contribute to optimal decision-making, and future computing systems address end-to-end delay issues with a central focus on Quality-of-Service metrics. Cognitive Machine Intelligence is an indispensable resource for researchers, practitioners, and enthusiasts seeking a deep understanding of the dynamic landscape at the intersection of artificial intelligence and cognitive computing. This book: Covers a comprehensive exploration of cognitive machine intelligence and its intersection with emerging technologies such as federated learning, blockchain, and 6G and beyond. Discusses the integration of machine learning with various technologies such as wireless communication networks, ad-hoc networks, software-defined networks, quantum computing, and big data. Examines the impact of machine learning on various fields such as healthcare, unmanned aerial vehicles, cybersecurity, and neural networks. Provides a detailed discussion on the challenges and solutions to future computer networks like end-to-end delay issues, Quality of Service (QoS) metrics, and security. Emphasizes the need to ensure privacy and trust while implementing the novel techniques of machine intelligence. It is primarily written for senior undergraduate and graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering.
Author: Maitreyee Dutta
Publisher: Springer Nature
ISBN: 9811530203
Category : Technology & Engineering
Languages : en
Pages : 455
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Book Description
This book gathers selected research papers presented at the AICTE-sponsored International Conference on IoT Inclusive Life (ICIIL 2019), which was organized by the Department of Computer Science and Engineering, National Institute of Technical Teachers Training and Research, Chandigarh, India, on December 19–20, 2019. In contributions by active researchers, the book presents innovative findings and important developments in IoT-related studies, making it a valuable resource for researchers, engineers, and industrial professionals around the globe.
