Network Intrusion Detection Using Deep Learning
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Network Intrusion Detection Using Deep Learning
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Author : Kwangjo Kim
language : en
Publisher: Springer
Release Date : 2018-09-25
Network Intrusion Detection Using Deep Learning written by Kwangjo Kim and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-25 with Computers categories.
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.
Deep Learning For Intrusion Detection
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Author : Faheem Syeed Masoodi
language : en
Publisher: John Wiley & Sons
Release Date : 2026-01-28
Deep Learning For Intrusion Detection written by Faheem Syeed Masoodi and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-01-28 with Computers categories.
Comprehensive resource exploring deep learning techniques for intrusion detection in various applications such as cyber physical systems and IoT networks Deep Learning for Intrusion Detection provides a practical guide to understand the challenges of intrusion detection in various application areas and how deep learning can be applied to address those challenges. It begins by discussing the basic concepts of intrusion detection systems (IDS) and various deep learning techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs). Later chapters cover timely topics including network communication between vehicles and unmanned aerial vehicles. The book closes by discussing security and intrusion issues associated with lightweight IoTs, MQTT networks, and Zero-Day attacks. The book presents real-world examples and case studies to highlight practical applications, along with contributions from leading experts who bring rich experience in both theory and practice. Deep Learning for Intrusion Detection includes information on: Types of datasets commonly used in intrusion detection research including network traffic datasets, system call datasets, and simulated datasets The importance of feature extraction and selection in improving the accuracy and efficiency of intrusion detection systems Security challenges associated with cloud computing, including unauthorized access, data loss, and other malicious activities Mobile Adhoc Networks (MANETs) and their significant security concerns due to high mobility and the absence of a centralized authority Deep Learning for Intrusion Detection is an excellent reference on the subject for computer science researchers, practitioners, and students as well as engineers and professionals working in cybersecurity.
A Novel Approach To Network Intrusion Detection System Using Deep Learning For Sdn
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Author : MHMOOD HADI
language : en
Publisher:
Release Date : 2022
A Novel Approach To Network Intrusion Detection System Using Deep Learning For Sdn written by MHMOOD HADI and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Software-Defined Networking (SDN) is the next generation to change the architecture of traditional networks. SDN is one of the promising solutions to change the architecture of internet networks. Attacks become more common due to the centralized nature of SDN architecture. It is vital to provide security for the SDN. In this study, we propose a Network Intrusion Detection System-Deep Learning module (NIDS-DL) approach in the context of SDN. Our suggested method combines Network Intrusion Detection Systems (NIDS) with many types of deep learning algorithms. Our approach employs 12 features extracted from 41 features in the NSL-KDD dataset using a feature selection method. We employed classifiers (CNN, DNN, RNN, LSTM, and GRU). When we compare classifier scores, our technique produced accuracy results of (98.63%, 98.53%, 98.13%, 98.04%, and 97.78%) respectively. The novelty of our new approach (NIDS-DL) uses 5 deep learning classifiers and made pre-processing dataset to harvests the best results. Our proposed approach was successful in binary classification and detecting attacks, implying that our approach (NIDS-DL) might be used with great efficiency in the future.
Network Anomaly Detection
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Author : Dhruba Kumar Bhattacharyya
language : en
Publisher: CRC Press
Release Date : 2013-06-18
Network Anomaly Detection written by Dhruba Kumar Bhattacharyya and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-18 with Computers categories.
With the rapid rise in the ubiquity and sophistication of Internet technology and the accompanying growth in the number of network attacks, network intrusion detection has become increasingly important. Anomaly-based network intrusion detection refers to finding exceptional or nonconforming patterns in network traffic data compared to normal behavior. Finding these anomalies has extensive applications in areas such as cyber security, credit card and insurance fraud detection, and military surveillance for enemy activities. Network Anomaly Detection: A Machine Learning Perspective presents machine learning techniques in depth to help you more effectively detect and counter network intrusion. In this book, you’ll learn about: Network anomalies and vulnerabilities at various layers The pros and cons of various machine learning techniques and algorithms A taxonomy of attacks based on their characteristics and behavior Feature selection algorithms How to assess the accuracy, performance, completeness, timeliness, stability, interoperability, reliability, and other dynamic aspects of a network anomaly detection system Practical tools for launching attacks, capturing packet or flow traffic, extracting features, detecting attacks, and evaluating detection performance Important unresolved issues and research challenges that need to be overcome to provide better protection for networks Examining numerous attacks in detail, the authors look at the tools that intruders use and show how to use this knowledge to protect networks. The book also provides material for hands-on development, so that you can code on a testbed to implement detection methods toward the development of your own intrusion detection system. It offers a thorough introduction to the state of the art in network anomaly detection using machine learning approaches and systems.
Design And Implementation Of A Deep Learning Based Intrusion Detection System In Software Defined Networking Environment
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Author : Quamar Niyaz
language : en
Publisher:
Release Date : 2017
Design And Implementation Of A Deep Learning Based Intrusion Detection System In Software Defined Networking Environment written by Quamar Niyaz and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Computer networks categories.
Network management becomes difficult when the size of the network grows. An ill-managed network opens several ways for the adversaries to exploit the security vulnerabilities for intrusions. Also, low-priced Internet subscriptions and publicly available attack tools enable the attackers to launch undiscovered or zero-day attacks in a network. Machine learning based approaches are well-suited to detect such kinds of undiscovered attacks. However, the hand-engineering involved in machine learning approaches for the proper selection of features from the network traffic puts a constraint on the accuracy of attack detection. The recently emerged networking paradigm named as software-defined networks (SDN) and the reincarnation of the neural network as deep learning (DL) promise to revolutionize the relevant industries. The SDN centralizes the network management and controls the network from a logically single point. The DL-based approach significantly improves the selection of features for the classification or prediction in an unsupervised manner. In our work, we utilize the benefits offered by the SDN and DL for the design and implementation of a network intrusion detection system (NIDS). The NIDS, implemented as an SDN application, can monitor the entire network for intrusions from a single point. Using the DL-based approach for the implementation helps in proper feature selection from a large traffic feature set and produces high accuracy with very low false alarms in intrusion detection. Before a real-world implementation of the NIDS, we develop a DL-based NIDS using a benchmark intrusion dataset (NSL-KDD) to explore the applicability of a DL-based approach for the NIDS implementation. An evaluation of the attack impact on network services running in the SDN environment is also performed. We analyze the response time and loss of service delivery in different attack scenarios. Finally, we discuss the implementation of a light-weight testbed for network security experiments developed with the tools used in an SDN infrastructure.
Application Of Machine Learning And Deep Learning For Intrusion Detection System
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Author : Nivedaaaiyer Ananda Subramaniam
language : en
Publisher:
Release Date : 2017
Application Of Machine Learning And Deep Learning For Intrusion Detection System written by Nivedaaaiyer Ananda Subramaniam and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
In today's world, a computer is highly exposed to attacks. In here, I try to build a predictive model to identify if the connection coming is an attack or genuine. Machine learning is that part of computer science in which instead of programming a machine we provide the ability to learn. Knowingly or unknowingly machine learning has become a part of our day to day lives. It could be in many ways like predicting stock market or image recognition while uploading a picture in Facebook and so on. Deep learning is a new concept which is trending these days, which moves a step towards the main aim of Machine Learning which is artificial intelligence. This machine learning/artificial intelligence can be used to make intrusion detection in a network more intelligent. We use different machine learning techniques including deep learning to figure out which approach is best for intrusion detection. To do this, we take a network intrusion dataset by Lincoln Labs who created an artificial set up to imitate U.S. Air Force LAN and get the TCP dumps generated. This also includes simulations of various types of attacks. We apply different machine learning algorithms on this data. And choose the machine learning algorithm which is most efficient to build a predictive model for intrusion detection. Now to the same dataset, we will apply Deep Learning mechanisms to build a predictive model with the algorithm that works the best for this data, after comparing the results generated by various deep learning algorithms. We build tool for each of the models (i.e. machine learning and deep learning). Now, the two tools one generated by machine learning and other by deep learning will be compared for accuracy.
Network Intrusion Detection Using Machine Learning And Voting Techniques
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Author : Tich Phuoc Tran
language : en
Publisher:
Release Date : 2010
Network Intrusion Detection Using Machine Learning And Voting Techniques written by Tich Phuoc Tran and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Games categories.
Network Intrusion Detection using Machine Learning and Voting techniques.
A Study On Network Intrusion Detection Using Classifiers
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Author : Balamurugan Rengeswaran
language : en
Publisher: GRIN Verlag
Release Date : 2019-10-21
A Study On Network Intrusion Detection Using Classifiers written by Balamurugan Rengeswaran and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-21 with Computers categories.
Research Paper (undergraduate) from the year 2019 in the subject Computer Science - Applied, VIT University, language: English, abstract: In these days of rising internet usage, almost everyone has access to the internet. It is available easily and readily. So along with increase in popularity and importance it also leads to an increase in risks and susceptibility to unwanted attacks. Networks and servers and more prone to malicious attacks than ever. Cyber security is vital in this age. Lots of organizations now interact and communicate with people via the internet. They store huge amounts of data in their computers or devices connected to the network. This data should only be accessed by authorized members of the organization. It is possible for hackers to gain unauthorized access to this data. A lot of sensitive information is present in the data which might lead to harm in the hands of hackers. It is important to protect the network from being attacked in such a way. Network security is an element of cyber security which aims to provide services so that the organizations are safe from such attacks. Intrusion detection systems are present in the network which work along with the firewalls to detect and prevent such attacks. For this project, we aim to identify the suitable machine learning technique to detect such attacks and which can be used in state of the art system.
Network Intrusion Detection And Deep Learning Mechanisms
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Author : Suvosree Chatterjee
language : en
Publisher: Independently Published
Release Date : 2023-04-18
Network Intrusion Detection And Deep Learning Mechanisms written by Suvosree Chatterjee and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-18 with categories.
Cyber attack is a strong threat to the digital world. So, it's very essential to keep the network safe. Network Intrusion Detection system is the system to address this problem. This book will provide everyone the fundamental idea of the Intrusion Detection System and a clear overview of the Deep learning concepts (Python with Tensorflow and Kears used in this book ).
Network Intrusion Detection System For Detecting Unknown Network Attacks Using Machine Learning Methods
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Author : Saif Mohammad Yousef Alzubi
language : en
Publisher:
Release Date : 2022
Network Intrusion Detection System For Detecting Unknown Network Attacks Using Machine Learning Methods written by Saif Mohammad Yousef Alzubi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.