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Author: Muriel Pellissier
Publisher: LAP Lambert Academic Publishing
ISBN: 9783659517549
Category :
Languages : en
Pages : 128
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Book Description
Nowadays, huge quantities of data can be easily accessible, but all these data are not useful if we do not know how to process them efficiently and how to extract easily relevant information from a large quantity of data. The anomaly detection techniques are used in many domains in order to help to process the data in an automated way. The anomaly detection techniques depend on the application domain, on the type of data, and on the type of anomaly. For this study we are interested only in sequential data. A sequence is an ordered list of items, also called events. Identifying irregularities in sequential data is essential for many application domains like DNA sequences, system calls, user commands, banking transactions etc. This book presents a new approach for identifying and analyzing irregularities in sequential data. This anomaly detection technique can detect anomalies in sequential data where the order of the items in the sequences is important. Moreover, our technique does not consider only the order of the events, but also the position of the events within the sequences.
Author: Muriel Pellissier
Publisher: LAP Lambert Academic Publishing
ISBN: 9783659517549
Category :
Languages : en
Pages : 128
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Book Description
Nowadays, huge quantities of data can be easily accessible, but all these data are not useful if we do not know how to process them efficiently and how to extract easily relevant information from a large quantity of data. The anomaly detection techniques are used in many domains in order to help to process the data in an automated way. The anomaly detection techniques depend on the application domain, on the type of data, and on the type of anomaly. For this study we are interested only in sequential data. A sequence is an ordered list of items, also called events. Identifying irregularities in sequential data is essential for many application domains like DNA sequences, system calls, user commands, banking transactions etc. This book presents a new approach for identifying and analyzing irregularities in sequential data. This anomaly detection technique can detect anomalies in sequential data where the order of the items in the sequences is important. Moreover, our technique does not consider only the order of the events, but also the position of the events within the sequences.
Author: Esra Negris Yolacan
Publisher:
ISBN:
Category : Anomaly detection (Computer security)
Languages : en
Pages : 141
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Book Description
Anomaly detection has been used in a wide range of real world problems and has received significant attention in a number of research fields over the last decades. Anomaly detection attempts to identify events, activities, or observations which are measurably different than an expected behavior or pattern present in a dataset. This thesis focuses on a specific set of techniques targeting the detection of anomalous behavior in a discrete, symbolic, and sequential dataset. Since profiling complex sequential data is still an open problem in anomaly detection, and given that the rate of production of sequential data in fields ranging from finance to homeland security is exploding, there is a pressing need to develop effective detection algorithms that can handle patterns in sequential information flows. In this thesis, we address context-aware multi-class anomaly detection as applied to discrete sequences and develop a context learning approach using an unsupervised learning paradigm. We begin the anomaly detection process by applying our approach to differentiate normal behavior classes (contexts) before attempting to model normal behavior. This approach leads to stronger learning on each class by taking advantage of the power of advanced models to identify normal behavior of the sequence classes. We evaluate our discrete sequence-based anomaly detection framework using two illustrative applications: 1) System call intrusion detection and 2) Crowd anomaly detection. We also evaluate how clustering can guide our context-aware methodology to positively impact the anomaly detection rate. In this thesis, we utilize a Hidden Markov Model (HMM) to perform anomaly detection. A HMM is the simplest dynamic Bayesian network. A HMM is a Markov model which can be used when the states are not observable, but observed data is dependent on these hidden states. While there has been a large amount of prior work utilizing Hidden Markov Models (HMMs) for anomaly detection, the proposed models became overly complex when attempting to improve the detection rate, while reducing the false detection rate. We apply HMMs to perform anomaly detection on discrete sequential data. We utilize multiple HMMs, one for each context class. We demonstrate our multi-HMM approach to system call anomalies in cyber security and provide results in the presence of anomalies. Applying process trace analysis with multi-HMMs, system call anomaly detection achieves better results using better tuned model settings and a less complex structure to detect anomalies. To evaluate the extensibility of our approach, we consider a second application, crowd behavior analytics. We attempt to classify crowd behavior and treat this as an anomaly detection problem on sequential data. We convert crowd video data into a discrete/symbolic sequence of data. We apply computer vision techniques to generate features from objects, and use these features for frame-based representations to model the behavior of the crowd in a video stream. We attempt to identify anomalous behavior of a crowd in a scene by applying machine learning techniques to understand what it means for a video stream to be identified as "normal". The results of applying our context-aware multi-HMMs approach to crowd analytics show the generality of our anomaly detection approach, and the power of our context-learning approach.
Author: Matthew Moocarme
Publisher: Packt Publishing Ltd
ISBN: 1800200226
Category : Computers
Languages : en
Pages : 601
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Book Description
Get started with TensorFlow fundamentals to build and train deep learning models with real-world data, practical exercises, and challenging activities Key FeaturesUnderstand the fundamentals of tensors, neural networks, and deep learningDiscover how to implement and fine-tune deep learning models for real-world datasetsBuild your experience and confidence with hands-on exercises and activitiesBook Description Getting to grips with tensors, deep learning, and neural networks can be intimidating and confusing for anyone, no matter their experience level. The breadth of information out there, often written at a very high level and aimed at advanced practitioners, can make getting started even more challenging. If this sounds familiar to you, The TensorFlow Workshop is here to help. Combining clear explanations, realistic examples, and plenty of hands-on practice, it'll quickly get you up and running. You'll start off with the basics – learning how to load data into TensorFlow, perform tensor operations, and utilize common optimizers and activation functions. As you progress, you'll experiment with different TensorFlow development tools, including TensorBoard, TensorFlow Hub, and Google Colab, before moving on to solve regression and classification problems with sequential models. Building on this solid foundation, you'll learn how to tune models and work with different types of neural network, getting hands-on with real-world deep learning applications such as text encoding, temperature forecasting, image augmentation, and audio processing. By the end of this deep learning book, you'll have the skills, knowledge, and confidence to tackle your own ambitious deep learning projects with TensorFlow. What you will learnGet to grips with TensorFlow's mathematical operationsPre-process a wide variety of tabular, sequential, and image dataUnderstand the purpose and usage of different deep learning layersPerform hyperparameter-tuning to prevent overfitting of training dataUse pre-trained models to speed up the development of learning modelsGenerate new data based on existing patterns using generative modelsWho this book is for This TensorFlow book is for anyone who wants to develop their understanding of deep learning and get started building neural networks with TensorFlow. Basic knowledge of Python programming and its libraries, as well as a general understanding of the fundamentals of data science and machine learning, will help you grasp the topics covered in this book more easily.
Author: Sid Ryan
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
In many real-world applications, the characteristics of data change over time. This behavior is known as concept drift. Maintaining optimal algorithms and their hyperparameters in such applications becomes cumbersome, as models become outdated very quickly. Although the data often consists of one-dimensional streams (e.g. collected by activity logs, sensors and mobile devices), in a higher level the aggregated sources produce multiple streams. Machine learning, therefore, requires univariate and multivariate analysis of long term dependencies to create valuable insights. In this thesis, we assess hundreds of combinations of data characteristics and methods in sequential data. Particularly we use real-life anomalous instances in the network traffic domain and to increase complexity we combine it with synthesized drifting data. From our preliminary evaluation of conventional machine learning, meta-learning and deep learning methods and comparing their generalization performance in the presence of concept drift, the results show that deep learning outperforms all other tested methods. Although, one-dimensional Convolutional Neural Networks (1D-CNN) produced the highest performance in image classification, similar to other models, they are able to label if sliding windows are anomalous or not. However, in majority of real-life applications, it is crucial to find individual instances that resulted in an anomalous pattern. Therefore, we introduce a method to transform the representation of the data to tensors of two dimensional images, enabling modern deep learning methods to become directly applicable to sequential data. We propose Sequential Mask Convolutional Neural Network (SMCNN) pinpoints the location of anomalous patterns. SMCNN model transforms sequential data by means of a specialized filter that produces flexible shape forms and detects multiple types of outliers simultaneously. In addition, to solve the issue of high ratio of False Positive in the unsupervised Generative Adversarial Networks (GAN) in concept drifts, we introduce a method for finding optimal sliding windows that automatically removes normal repetitive patterns. We introduce DriftGAN architecture that discriminates between normal and anomalous patterns. Our SMCNN and DriftGAN methods significantly outperform prior endeavours and provide high generalization capabilities on a wide array of one-dimensional data characteristics with repetitive nature.
Author: Chandripal Budnarain
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
With recent successes of recurrent neural networks (RNNs) for machine translation, and handwriting recognition tasks, we hypothesize that RNN approaches might be best suited for unsupervised anomaly detection in time series. In this thesis, we first contribute a comprehensive comparative evaluation of RNN-based deep learning methods for anomaly detection across a wide array of popular deep neural network architectures. In our second major contribution we observe that a key gap of deep learning based anomaly detection methods is the inability to identify portions of the data that led to the detected anomaly. To address this, we propose a novel explainability approach that aims to pinpoint regions of an input that lead to the detected anomaly. In sum, this thesis not only advances the state-of-the-art in deep learning based anomaly detection for time series data but it also contributes novel methods for producing explanations and evaluating explanation quality of anomaly detectors.
Author: Shi Jin
Publisher: Springer Nature
ISBN: 3030336646
Category : Technology & Engineering
Languages : en
Pages : 155
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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: D. Hawkins
Publisher: Springer
ISBN:
Category : Juvenile Nonfiction
Languages : en
Pages : 208
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Book Description
General theoretical principles; A single outlier in normal samples; The gamma distribution; Multiple outliers; Non-parametric tests; Outliers from the linear model; Multivariate outlier detection; Bayesian approach to outliers; Miscellaneous topics.
Author: Saira Banu
Publisher: Nova Science Publishers
ISBN: 9781536192643
Category : Anomaly detection (Computer security)
Languages : en
Pages : 0
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Book Description
When information in the data warehouse is processed, it follows a definite pattern. An unexpected deviation in the data pattern from the usual behavior is called an anomaly. The anomaly in the data is also referred to as noise, outlier, spammer, deviations, novelties and exceptions. Identification of the rare items, events, observations, patterns which raise suspension by differing significantly from the majority of data is called anomaly detection. With progress in the technologies and the widespread use of data for the purpose for business the increase in the spams faced by the individuals and the companies are increasing day by day. This noisy data has boomed as a major problem in various areas such as Internet of Things, web service, Machine Learning, Artificial Intelligence, Deep learning, Image Processing, Cloud Computing, Audio processing, Video Processing, VoIP, Data Science, Wireless Sensor etc. Identifying the anomaly data and filtering them before processing is a major challenge for the data analyst. This anomaly is unavoidable in all areas of research. This book covers the techniques and algorithms for detecting the deviated data. This book will mainly target researchers and higher graduate learners in computer science and data science.
Author: Xiaochun Wang
Publisher: Springer Nature
ISBN: 9819730236
Category :
Languages : en
Pages : 396
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Book Description
Author: Kishan G. Mehrotra
Publisher: Springer
ISBN: 3319675265
Category : Computers
Languages : en
Pages : 229
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Book Description
This book provides a readable and elegant presentation of the principles of anomaly detection,providing an easy introduction for newcomers to the field. A large number of algorithms are succinctly described, along with a presentation of their strengths and weaknesses. The authors also cover algorithms that address different kinds of problems of interest with single and multiple time series data and multi-dimensional data. New ensemble anomaly detection algorithms are described, utilizing the benefits provided by diverse algorithms, each of which work well on some kinds of data. With advancements in technology and the extensive use of the internet as a medium for communications and commerce, there has been a tremendous increase in the threats faced by individuals and organizations from attackers and criminal entities. Variations in the observable behaviors of individuals (from others and from their own past behaviors) have been found to be useful in predicting potential problems of various kinds. Hence computer scientists and statisticians have been conducting research on automatically identifying anomalies in large datasets. This book will primarily target practitioners and researchers who are newcomers to the area of modern anomaly detection techniques. Advanced-level students in computer science will also find this book helpful with their studies.
