An Anomaly Detection Model Utilizing Attributes of Low Powered Networks, IEEE 802.15.4e/TSCH and Machine Learning Methods PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download An Anomaly Detection Model Utilizing Attributes of Low Powered Networks, IEEE 802.15.4e/TSCH and Machine Learning Methods PDF full book. Access full book title An Anomaly Detection Model Utilizing Attributes of Low Powered Networks, IEEE 802.15.4e/TSCH and Machine Learning Methods by Sajeeva Salgadoe. Download full books in PDF and EPUB format.
Author: Sajeeva Salgadoe
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
The rapid growth in sensors, low-power integrated circuits, and wireless communication standards has enabled a new generation of applications based on ultra-low powered wireless sensor networks. These are employed in many environments including health-care, industrial automation, smart building and environmental monitoring. According to industry experts, by the year 2020, over 20 billion low powered, sensor devices will be deployed and an innumerable number of data objects will be created. The objective of this work is to investigate the feasibility and analyze optimal methods of using low powered wireless characteristics, attributes of communication protocols and machine learning techniques to determine traffic anomalies in low powered networks. Traffic anomalies can be used to detect security violations as well as network performance issues. Both live and simulated data have been used with four machine learning methods, to examine the relationship between performance and the various factors and methods. Several factors including the number of nodes, sample size, noise influence, model aging process and classification algorithm are investigated against performance accuracy using data collected from an operational wireless network, comprising more than one hundred nodes, during a six-month period. An important attribute of this work is that the proposed model is able to implement in any low powered network, regardless of the software and hardware architecture of individual nodes (as long as the network complies with an open standard communication mechanism). Furthermore, the experiment portion of this work includes over 80 independent experiments to evaluate the behaviour of various attributes of low powered networks. Machine learning models trained using carefully selected input features and other factors including adequate training samples and classification algorithm are able to detect traffic anomalies of low powered wireless networks with over 95% accuracy. Furthermore, in this work, a framework for an aggregated classification model has been evaluated and the experiment results confirm a further improvement of the prediction accuracy and a reduction of both false positive and negative rates in comparison to basic classification models.
Author: Sajeeva Salgadoe
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
The rapid growth in sensors, low-power integrated circuits, and wireless communication standards has enabled a new generation of applications based on ultra-low powered wireless sensor networks. These are employed in many environments including health-care, industrial automation, smart building and environmental monitoring. According to industry experts, by the year 2020, over 20 billion low powered, sensor devices will be deployed and an innumerable number of data objects will be created. The objective of this work is to investigate the feasibility and analyze optimal methods of using low powered wireless characteristics, attributes of communication protocols and machine learning techniques to determine traffic anomalies in low powered networks. Traffic anomalies can be used to detect security violations as well as network performance issues. Both live and simulated data have been used with four machine learning methods, to examine the relationship between performance and the various factors and methods. Several factors including the number of nodes, sample size, noise influence, model aging process and classification algorithm are investigated against performance accuracy using data collected from an operational wireless network, comprising more than one hundred nodes, during a six-month period. An important attribute of this work is that the proposed model is able to implement in any low powered network, regardless of the software and hardware architecture of individual nodes (as long as the network complies with an open standard communication mechanism). Furthermore, the experiment portion of this work includes over 80 independent experiments to evaluate the behaviour of various attributes of low powered networks. Machine learning models trained using carefully selected input features and other factors including adequate training samples and classification algorithm are able to detect traffic anomalies of low powered wireless networks with over 95% accuracy. Furthermore, in this work, a framework for an aggregated classification model has been evaluated and the experiment results confirm a further improvement of the prediction accuracy and a reduction of both false positive and negative rates in comparison to basic classification models.
Author: Dhruba Kumar Bhattacharyya
Publisher: CRC Press
ISBN: 146658209X
Category : Computers
Languages : en
Pages : 364
Get Book Here
Book Description
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 behavi
Author: Danfeng (Daphne)Yao
Publisher: Springer Nature
ISBN: 3031023544
Category : Computers
Languages : en
Pages : 157
Get Book Here
Book Description
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.
Author: Shi Jin
Publisher: Springer Nature
ISBN: 3030336646
Category : Technology & Engineering
Languages : en
Pages : 155
Get Book Here
Book Description
This book tackles important problems of anomaly detection and health status analysis in complex core router systems, integral to today’s Internet Protocol (IP) networks. The techniques described provide the first comprehensive set of data-driven resiliency solutions for core router systems. The authors present an anomaly detector for core router systems using correlation-based time series analysis, which monitors a set of features of a complex core router system. They also describe the design of a changepoint-based anomaly detector such that anomaly detection can be adaptive to changes in the statistical features of data streams. The presentation also includes a symbol-based health status analyzer that first encodes, as a symbol sequence, the long-term complex time series collected from a number of core routers, and then utilizes the symbol sequence for health analysis. Finally, the authors describe an iterative, self-learning procedure for assessing the health status. Enables Accurate Anomaly Detection Using Correlation-Based Time-Series Analysis; Presents the design of a changepoint-based anomaly detector; Includes Hierarchical Symbol-based Health-Status Analysis; Describes an iterative, self-learning procedure for assessing the health status.
Author: Mehdi Roopaei
Publisher: CRC Press
ISBN: 1351119001
Category : Computers
Languages : en
Pages : 236
Get Book Here
Book Description
This book provides a comprehensive overview of the research on anomaly detection with respect to context and situational awareness that aim to get a better understanding of how context information influences anomaly detection. In each chapter, it identifies advanced anomaly detection and key assumptions, which are used by the model to differentiate between normal and anomalous behavior. When applying a given model to a particular application, the assumptions can be used as guidelines to assess the effectiveness of the model in that domain. Each chapter provides an advanced deep content understanding and anomaly detection algorithm, and then shows how the proposed approach is deviating of the basic techniques. Further, for each chapter, it describes the advantages and disadvantages of the algorithm. The final chapters provide a discussion on the computational complexity of the models and graph computational frameworks such as Google Tensorflow and H2O because it is an important issue in real application domains. This book provides a better understanding of the different directions in which research has been done on deep semantic analysis and situational assessment using deep learning for anomalous detection, and how methods developed in one area can be applied in applications in other domains. This book seeks to provide both cyber analytics practitioners and researchers an up-to-date and advanced knowledge in cloud based frameworks for deep semantic analysis and advanced anomaly detection using cognitive and artificial intelligence (AI) models.
Author: Yihua Liao
Publisher:
ISBN:
Category :
Languages : en
Pages : 230
Get Book Here
Book Description
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: Arun Abhishek Imayakumar
Publisher:
ISBN:
Category : Anomaly detection (Computer security)
Languages : en
Pages :
Get Book Here
Book Description
Sensor measurements of distribution system are uncertain due to sensor malfunctions, communication failure and cyber attacks. This thesis aims to perform anomaly detection on measurements utilizing data-driven approaches. The measurements considered are individual smart meter real power measurements and network-wide primary voltage magnitudes. Anomaly detection in individual smart meter measurements using gaussian probabilistic thresholds is explored. It flags non-anomalous data as verified by the comparison of smart meter real power and individual appliance consumption. To perform a real-time comparison for detection, Non-Intrusive Load Monitoring (NILM) is needed, which is difficult due to the associated consumer privacy issues. Alternatively, forecasting can be used for anomaly detection. So, single layer neural network models such as Multi-Layer Perceptron (MLP), and Long Short Term Memory (LSTM) with different features are tried. Even in training data, a poor performance is seen in these models, due to the smart meter profile variability. Hence, aggregated smart meter forecasting using neural networks can be used to detect anomaly in such aggregated measurements with a reasonable accuracy. Network-wide primary voltage measurements are correlated for a phase of feeder for different buses at a given time-step; this is extensively validated empirically. To leverage this, Principal Component Analysis (PCA) is used to reduce the dimensionality of this input data. Further, residual and subspace based methods are explored for network-level anomaly detection and identification. The results for the residual approach on missing and bad data cases are detailed for IEEE 13 bus and IEEE 8500 node test feeders. It is validated through simulations that residual-based approach on subspace projection matrix for the measurement data successfully performs anomaly detection and identification for primary network voltage measurements for the selected test cases. Further research is needed to validate the applicability and accuracy of the proposed framework during changes in the system operating conditions (topology changes, capacitor bank switching, etc.), and on real-world measurements form sensors deployed in the field.
Author: Jugal Kalita
Publisher:
ISBN:
Category :
Languages : en
Pages : 366
Get Book Here
Book Description
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: Kelvin Khuong Mai
Publisher:
ISBN:
Category : Anomaly detection (Computer security)
Languages : en
Pages :
Get Book Here
Book Description
Critical infrastructures such as power grids are facing clear and present danger from cyber threats and attacks. A case in point is the 2016 Ukraine0́9s power grid attack, which has confirmed that cyber adversaries could cause persistent blackout using malware. This incident occurred despite the fact that there have been significant cyber security research efforts and improvements made to the power grid in the last two decades to protect and defend against cyber attacks. Therefore, cyber attack mitigation efforts such as intrusion detection in general or anomaly detection in particular remain a formidable challenge. Yet, anomaly detection also face a challenge of its own, a high false positive rate. Another challenge in protecting power grids from cyber attack comes from the fact that heightened security concerns have limited cyber security researchers from having access to an operational power grid network, as a result, numerous previous research works have been based on testbeds, simulated models, or a single substation of the power grid. To address these challenges, this work presents: (1) The first in-depth characterization of a large-scale realworld federated bulk power grid that utilizes IEC 60870-5-104 Supervisory Control and Data Acquisition (SCADA) protocol to control and monitor physical processes of the grid; (2) A connection-scoped anomaly detection system (CS-ADS) that unifies both traditional and modern machine learning models with integrated expert domain knowledge into one design. The design intent of this CS-ADS is to reduce false positive rate while increasing true positive rate. To evaluate the proposed CS-ADS, real-world ICS/SCADA malware will be employed as an adversary model.
Author:
Publisher:
ISBN:
Category : Computer networks
Languages : en
Pages : 322
Get Book Here
Book Description
The intrusion detection in computer networks is a complex research problem, which requires the understanding of computer networks and the mechanism of intrusions, the configuration of sensors and the collected data, the selection of the relevant attributes, and the monitor algorithms for online detection. It is critical to develop general methods for data dimension reduction, effective monitoring algorithms for intrusion detection, and means for their performance improvement. This dissertation is motivated by the timely need to develop statistics-based machine learning methods for effective detection of computer network anomalies. Three fundamental research issues related to data dimension reduction, control charts design and performance improvement have been addressed accordingly. The major research activities and corresponding contributions are summarized as follows:(1) Filter and Wrapper models are integrated to extract a small number of the informative attributes for computer network intrusion detection. A two-phase analyses method is proposed for the integration of Filter and Wrapper models. The proposed method has successfully reduced the original 41 attributes to 12 informative attributes while increasing the accuracy of the model. The comparison of the results in each phase shows the effectiveness of the proposed method.(2) Supervised kernel based control charts for anomaly intrusion detection. We propose to construct control charts in a feature space. The first contribution is the use of multi-objective Genetic Algorithm in the parameter pre-selection for SVM based control charts. The second contribution is the performance evaluation of supervised kernel based control charts.(3) Unsupervised kernel based control charts for anomaly intrusion detection. Two types of unsupervised kernel based control charts are investigated: Kernel PCA control charts and Support Vector Clustering based control charts. The applications of SVC based control charts on computer networks audit data are also discussed to demonstrate the effectiveness of the proposed method. Although the developed methodologies in this dissertation are demonstrated in the computer network intrusion detection applications, the methodologies are also expected to be applied to other complex system monitoring, where the database consists of a large dimensional data with non-Gaussian distribution.
