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Author : Patrick Collins
Publisher :
Page : 74 pages
File Size : 22,34 MB
Release : 2016
Category : Dissertations, Academic
ISBN :
Author : Nivedaaaiyer Ananda Subramaniam
Publisher :
Page : 106 pages
File Size : 46,50 MB
Release : 2017
Category :
ISBN :
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.
Author : Kwangjo Kim
Publisher : Springer
Page : 79 pages
File Size : 47,71 MB
Release : 2018-10-02
Category : Computers
ISBN : 9789811314438
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
Page : 185 pages
File Size : 47,24 MB
Release : 2011
Category : Computers
ISBN : 1848164475
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 : Kwangjo Kim
Publisher : Springer
Page : 92 pages
File Size : 45,81 MB
Release : 2018-09-25
Category : Computers
ISBN : 9811314446
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 : Asghar Ali Shah
Publisher : Infinite Study
Page : 11 pages
File Size : 26,62 MB
Release :
Category :
ISBN :
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 : Yihua Liao
Publisher :
Page : 230 pages
File Size : 15,9 MB
Release : 2005
Category :
ISBN :
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.
Author : Dhruba Kumar Bhattacharyya
Publisher : CRC Press
Page : 368 pages
File Size : 14,12 MB
Release : 2013-06-18
Category : Computers
ISBN : 1466582081
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.
Author : Konrad
Publisher : Lulu.com
Page : 181 pages
File Size : 27,63 MB
Release : 2011-09-21
Category : Computers
ISBN : 144784808X
This book is concerned with the automatic detection of unknown attacks in network communication. Based on concepts of machine learning, a framework for self-learning intrusion detection is proposed which enables accurate and efficient identification of attacks in the application layer of network communication. The book is a doctoral thesis and targets researchers and postgraduate students in the area of computer security and machine learning.
Author : Ganapathi, Padmavathi
Publisher : IGI Global
Page : 482 pages
File Size : 36,21 MB
Release : 2019-07-26
Category : Computers
ISBN : 1522596135
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.