Network Anomaly Detection
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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.
Network Traffic Anomaly Detection And Prevention
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Author : Monowar H. Bhuyan
language : en
Publisher: Springer
Release Date : 2017-09-03
Network Traffic Anomaly Detection And Prevention written by Monowar H. Bhuyan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-03 with Computers categories.
This indispensable text/reference presents a comprehensive overview on the detection and prevention of anomalies in computer network traffic, from coverage of the fundamental theoretical concepts to in-depth analysis of systems and methods. Readers will benefit from invaluable practical guidance on how to design an intrusion detection technique and incorporate it into a system, as well as on how to analyze and correlate alerts without prior information. Topics and features: introduces the essentials of traffic management in high speed networks, detailing types of anomalies, network vulnerabilities, and a taxonomy of network attacks; describes a systematic approach to generating large network intrusion datasets, and reviews existing synthetic, benchmark, and real-life datasets; provides a detailed study of network anomaly detection techniques and systems under six different categories: statistical, classification, knowledge-base, cluster and outlier detection, soft computing, and combination learners; examines alert management and anomaly prevention techniques, including alert preprocessing, alert correlation, and alert post-processing; presents a hands-on approach to developing network traffic monitoring and analysis tools, together with a survey of existing tools; discusses various evaluation criteria and metrics, covering issues of accuracy, performance, completeness, timeliness, reliability, and quality; reviews open issues and challenges in network traffic anomaly detection and prevention. This informative work is ideal for graduate and advanced undergraduate students interested in network security and privacy, intrusion detection systems, and data mining in security. Researchers and practitioners specializing in network security will also find the book to be a useful reference.
Network Anomaly Detection
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Author : Jugal Kalita
language : en
Publisher:
Release Date : 2013
Network Anomaly Detection written by Jugal Kalita and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with 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.
Network Anomaly Detection Based On Late Fusion Of Several Machine Learning Algorithms
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Author :
language : en
Publisher:
Release Date : 2021
Network Anomaly Detection Based On Late Fusion Of Several Machine Learning Algorithms written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Today's Internet and enterprise networks are so popular as they can easily provide multimedia and ecommerce services to millions of users over the Internet in our daily lives. Since then, security has been a challenging problem in the Internet's world. That issue is called Cyberwar, in which attackers can aim or raise Distributed Denial of Service (DDoS) to others to take down the operation of enterprises Intranet. Therefore, the need of applying an Intrusion Detection System (IDS) is very important to enterprise networks. In this paper, we propose a smarter solution to detect network anomalies in Cyberwar using Stacking techniques in which we apply three popular machine learning models: k-nearest neighbor algorithm (KNN), Adaptive Boosting (AdaBoost), and Random Decision Forests (RandomForest). Our proposed scheme uses the Logistic Regression method to automatically search for better parameters to the Stacking model. We do the performance evaluation of our proposed scheme on the latest data set NSLKDD 2019 dataset. We also compare the achieved results with individual machine learning models to show that our proposed model achieves much higher accuracy than previous works.
Anomaly Detection As A Service
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Author : Danfeng (Daphne) Yao
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2017-10-24
Anomaly Detection As A Service written by Danfeng (Daphne) Yao and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-24 with Computers categories.
Anomaly detection has been a long-standing security approach with versatile applications, ranging from securing server programs in critical environments, to detecting insider threats in enterprises, to anti-abuse detection for online social networks. Despite the seemingly diverse application domains, anomaly detection solutions share similar technical challenges, such as how to accurately recognize various normal patterns, how to reduce false alarms, how to adapt to concept drifts, and how to minimize performance impact. They also share similar detection approaches and evaluation methods, such as feature extraction, dimension reduction, and experimental evaluation. The main purpose of this book is to help advance the real-world adoption and deployment anomaly detection technologies, by systematizing the body of existing knowledge on anomaly detection. This book is focused on data-driven anomaly detection for software, systems, and networks against advanced exploits and attacks, but also touches on a number of applications, including fraud detection and insider threats. We explain the key technical components in anomaly detection workflows, give in-depth description of the state-of-the-art data-driven anomaly-based security solutions, and more importantly, point out promising new research directions. This book emphasizes on the need and challenges for deploying service-oriented anomaly detection in practice, where clients can outsource the detection to dedicated security providers and enjoy the protection without tending to the intricate details.
Unsupervised Network Anomaly Detection
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Author : Johan Mazel
language : en
Publisher:
Release Date : 2011
Unsupervised Network Anomaly Detection written by Johan Mazel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
Anomaly detection has become a vital component of any network in today's Internet. Ranging from non-malicious unexpected events such as flash-crowds and failures, to network attacks such as denials-of-service and network scans, network traffic anomalies can have serious detrimental effects on the performance and integrity of the network. The continuous arising of new anomalies and attacks create a continuous challenge to cope with events that put the network integrity at risk. Moreover, the inner polymorphic nature of traffic caused, among other things, by a highly changing protocol landscape, complicates anomaly detection system's task. In fact, most network anomaly detection systems proposed so far employ knowledge-dependent techniques, using either misuse detection signature-based detection methods or anomaly detection relying on supervised-learning techniques. However, both approaches present major limitations: the former fails to detect and characterize unknown anomalies (letting the network unprotected for long periods) and the latter requires training over labeled normal traffic, which is a difficult and expensive stage that need to be updated on a regular basis to follow network traffic evolution. Such limitations impose a serious bottleneck to the previously presented problem.We introduce an unsupervised approach to detect and characterize network anomalies, without relying on signatures, statistical training, or labeled traffic, which represents a significant step towards the autonomy of networks. Unsupervised detection is accomplished by means of robust data-clustering techniques, combining Sub-Space clustering with Evidence Accumulation or Inter-Clustering Results Association, to blindly identify anomalies in traffic flows. Correlating the results of several unsupervised detections is also performed to improve detection robustness. The correlation results are further used along other anomaly characteristics to build an anomaly hierarchy in terms of dangerousness. Characterization is then achieved by building efficient filtering rules to describe a detected anomaly. The detection and characterization performances and sensitivities to parameters are evaluated over a substantial subset of the MAWI repository which contains real network traffic traces.Our work shows that unsupervised learning techniques allow anomaly detection systems to isolate anomalous traffic without any previous knowledge. We think that this contribution constitutes a great step towards autonomous network anomaly detection.This PhD thesis has been funded through the ECODE project by the European Commission under the Framework Programme 7. The goal of this project is to develop, implement, and validate experimentally a cognitive routing system that meet the challenges experienced by the Internet in terms of manageability and security, availability and accountability, as well as routing system scalability and quality. The concerned use case inside the ECODE project is network anomaly.
Scalable And Efficient Network Anomaly Detection On Connection Data Streams
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Author : Aniss Chohra
language : en
Publisher:
Release Date : 2019
Scalable And Efficient Network Anomaly Detection On Connection Data Streams written by Aniss Chohra and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Everyday, security experts and analysts must deal with and face the huge increase of cyber security threats that are propagating very fast on the Internet and threatening the security of hundreds of millions of users worldwide. The detection of such threats and attacks is of paramount importance to these experts in order to prevent these threats and mitigate their effects in the future. Thus, the need for security solutions that can prevent, detect, and mitigate such threats is imminent and must be addressed with scalable and efficient solutions. To this end, we propose a scalable framework, called Daedalus, to analyze streams of NIDS (network-based intrusion detection system) logs in near real-time and to extract useful threat security intelligence. The proposed system pre-processes massive amounts of connections stream logs received from different participating organizations and applies an elaborated anomaly detection technique in order to distinguish between normal and abnormal or anomalous network behaviors. As such, Daedalus detects network traffic anomalies by extracting a set of significant pre-defined features from the connection logs and then applying a time series-based technique in order to detect abnormal behavior in near real-time. Moreover, we correlate IP blocks extracted from the logs with some external security signature-based feeds that detect factual malicious activities (e.g., malware families and hashes, ransomware distribution, and command and control centers) in order to validate the proposed approach. Performed experiments demonstrate that Daedalus accurately identifies the malicious activities with an average F_1 score of 92.88\%. We further compare our proposed approach with existing K-Means and deep learning (LSTMs) approaches and demonstrate the accuracy and efficiency of our system.
An Analysis Of Anomaly Detection In Network Traffic And Role Of Wavelets
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Author : Gagandeep Kaur
language : en
Publisher:
Release Date : 2010
An Analysis Of Anomaly Detection In Network Traffic And Role Of Wavelets written by Gagandeep Kaur and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
Detection of anomalies in today's world is a cumbersome task due to highly dynamic nature of intrusions. The traditional practices of Network Anomaly Detection (NAD) fail to detect and identify the dynamic intrusions in real times. They are based on patterns initially saved in database. Large number of tools exists in open source as well as commercial market, but fast and accurate detection and identification of anomalies still remains a gruesome task. In the past few years signal processing techniques have found applications in Network Intrusion Detection (NID) Systems due to their efficiency in tracing out deviations as well as transformations in the network traffic data. Any Intrusion Detection System (IDS) needs excellent visualization of obtained results so as to provide network administrator good information in the least possible time. This survey explains the main techniques known in the field of statistical-based and wavelet-based anomaly detection approaches and focuses on the role of data traffic visualization tools in network traffic anomaly detection.
Network Traffic Anomaly Detection Using Modified Hidden Markov Model
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Author :
language : en
Publisher:
Release Date : 2019
Network Traffic Anomaly Detection Using Modified Hidden Markov Model written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Computer networks categories.
With the growing number of attacks and malicious threats on the Internet services and network infrastructures, the need for techniques to identity and detect attacks is increasing. Therefore, using machine learning techniques along traditional security mechanisms such as firewall and cryptography, can improve the performance of intrusion detection systems (IDSs). Network anomaly detection has become a very important area for both industrial application and academic research in the recent years. It is involved widely in a broad spectrum of domains and many research areas. Detection anomalies (attacks are detected as anomalies) in data is a crucial problem to diverse real-world applications. The goal of anomaly detection is to identify anomalous behavior, events based on deviations from expected normal usage. Hidden Markov Models (HMM) have been applied to anomaly detection since 1996. The previous researches applying HMM were limited to small data sets. In our work, we have used the term anomaly detection to describe the process of differentiating abnormal behavior from normal behavior on datasets available in this study. In this dissertation, we describe our research contributions for detecting anomalous patterns in network traffic data using HMM. We built HMM correlates the observation sequences and state transitions to predict the most probable intrusion state sequences that are capable of reducing false positive rate.
Profile Based Adaptive Anomaly Detection For Network Security
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Author : Pengchu C. Zhang
language : en
Publisher:
Release Date : 2005
Profile Based Adaptive Anomaly Detection For Network Security written by Pengchu C. Zhang 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.
As information systems become increasingly complex and pervasive, they become inextricably intertwined with the critical infrastructure of national, public, and private organizations. The problem of recognizing and evaluating threats against these complex, heterogeneous networks of cyber and physical components is a difficult one, yet a solution is vital to ensuring security. In this paper we investigate profile-based anomaly detection techniques that can be used to address this problem. We focus primarily on the area of network anomaly detection, but the approach could be extended to other problem domains. We investigate using several data analysis techniques to create profiles of network hosts and perform anomaly detection using those profiles. The ''profiles'' reduce multi-dimensional vectors representing ''normal behavior'' into fewer dimensions, thus allowing pattern and cluster discovery. New events are compared against the profiles, producing a quantitative measure of how ''anomalous'' the event is. Most network intrusion detection systems (IDSs) detect malicious behavior by searching for known patterns in the network traffic. This approach suffers from several weaknesses, including a lack of generalizability, an inability to detect stealthy or novel attacks, and lack of flexibility regarding alarm thresholds. Our research focuses on enhancing current IDS capabilities by addressing some of these shortcomings. We identify and evaluate promising techniques for data mining and machine-learning. The algorithms are ''trained'' by providing them with a series of data-points from ''normal'' network traffic. A successful algorithm can be trained automatically and efficiently, will have a low error rate (low false alarm and miss rates), and will be able to identify anomalies in ''pseudo real-time'' (i.e., while the intrusion is still in progress, rather than after the fact). We also build a prototype anomaly detection tool that demonstrates how the techniques might be integrated into an operational intrusion detection framework.