Deep Learning For Intrusion Detection
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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.
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.
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.
Machine Learning In Intrusion Detection
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Author : Yihua Liao
language : en
Publisher:
Release Date : 2005
Machine Learning In Intrusion Detection written by Yihua Liao and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.
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.
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.
Deep Learning Approach For Intrusion Detection System Ids In The Internet Of Things Iot Network Using Gated Recurrent Neural Networks Gru
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Author : Manoj Kumar Putchala
language : en
Publisher:
Release Date : 2017
Deep Learning Approach For Intrusion Detection System Ids In The Internet Of Things Iot Network Using Gated Recurrent Neural Networks Gru written by Manoj Kumar Putchala 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 science categories.
The Internet of Things (IoT) is a complex paradigm where billions of devices are connected to a network. These connected devices form an intelligent system of systems that share the data without human-to-computer or human-to-human interaction. These systems extract meaningful data that can transform human lives, businesses, and the world in significant ways. However, the reality of IoT is prone to countless cyber-attacks in the extremely hostile environment like the internet. The recent hack of 2014 Jeep Cherokee, iStan pacemaker, and a German steel plant are a few notable security breaches. To secure an IoT system, the traditional high-end security solutions are not suitable, as IoT devices are of low storage capacity and less processing power. Moreover, the IoT devices are connected for longer time periods without human intervention. This raises a need to develop smart security solutions which are light-weight, distributed and have a high longevity of service. Rather than per-device security for numerous IoT devices, it is more feasible to implement security solutions for network data. The artificial intelligence theories like Machine Learning and Deep Learning have already proven their significance when dealing with heterogeneous data of various sizes. To substantiate this, in this research, we have applied concepts of Deep Learning and Transmission Control Protocol/Internet Protocol (TCP/IP) to build a light-weight distributed security solution with high durability for IoT network security. First, we have examined the ways of improving IoT architecture and proposed a light-weight and multi-layered design for an IoT network. Second, we have analyzed the existingapplications of Machine Learning and Deep Learning to the IoT and Cyber-Security. Third, we have evaluated deep learning's Gated Recurrent Neural Networks (LSTM and GRU) on the DARPA/KDD Cup '99 intrusion detection data set for each layer in the designed architecture. Finally, from the evaluated metrics, we have proposed the best neural network design suitable for the IoT Intrusion Detection System. With an accuracy of 98.91% and False Alarm Rate of 0.76 %, this unique research outperformed the performance results of existing methods over the KDD Cup '99 dataset. For this first time in the IoT research, the concepts of Gated Recurrent Neural Networks are applied for the IoT security.
Intrusion Detection
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Author : Zhenwei Yu
language : en
Publisher: World Scientific
Release Date : 2011
Intrusion Detection written by Zhenwei Yu and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Computers categories.
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.
Analysis Of Machine Learning Techniques For Intrusion Detection System A Review
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Author : Asghar Ali Shah
language : en
Publisher: Infinite Study
Release Date :
Analysis Of Machine Learning Techniques For Intrusion Detection System A Review written by Asghar Ali Shah and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
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.
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.
Deep Learning Applications For Cyber Security
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Author : Mamoun Alazab
language : en
Publisher: Springer
Release Date : 2019-08-14
Deep Learning Applications For Cyber Security written by Mamoun Alazab and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-14 with Computers categories.
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.