End To End Anomaly Detection In Stream Data
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End To End Anomaly Detection In Stream Data
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Author : Zahra Zohrevand
language : en
Publisher:
Release Date : 2020
End To End Anomaly Detection In Stream Data written by Zahra Zohrevand and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Nowadays, huge volumes of data are generated with increasing velocity through various systems, applications, and activities. This increases the demand for stream and time series analysis to react to changing conditions in real-time for enhanced efficiency and quality of service delivery as well as upgraded safety and security in private and public sectors. Despite its very rich history, time series anomaly detection is still one of the vital topics in machine learning research and is receiving increasing attention. Identifying hidden patterns and selecting an appropriate model that fits the observed data well and also carries over to unobserved data is not a trivial task. Due to the increasing diversity of data sources and associated stochastic processes, this pivotal data analysis topic is loaded with various challenges like complex latent patterns, concept drift, and overfitting that may mislead the model and cause a high false alarm rate. Handling these challenges leads the advanced anomaly detection methods to develop sophisticated decision logic, which turns them into mysterious and inexplicable black-boxes. Contrary to this trend, end-users expect transparency and verifiability to trust a model and the outcomes it produces. Also, pointing the users to the most anomalous/malicious areas of time series and causal features could save them time, energy, and money. For the mentioned reasons, this thesis is addressing the crucial challenges in an end-to-end pipeline of stream-based anomaly detection through the three essential phases of behavior prediction, inference, and interpretation. The first step is focused on devising a time series model that leads to high average accuracy as well as small error deviation. On this basis, we propose higher-quality anomaly detection and scoring techniques that utilize the related contexts to reclassify the observations and post-pruning the unjustified events. Last but not least, we make the predictive process transparent and verifiable by providing meaningful reasoning behind its generated results based on the understandable concepts by a human. The provided insight can pinpoint the anomalous regions of time series and explain why the current status of a system has been flagged as anomalous. Stream-based anomaly detection research is a principal area of innovation to support our economy, security, and even the safety and health of societies worldwide. We believe our proposed analysis techniques can contribute to building a situational awareness platform and open new perspectives in a variety of domains like cybersecurity, and health.
Anomaly Detection And Complex Event Processing Over Iot Data Streams
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Author : Patrick Schneider
language : en
Publisher: Academic Press
Release Date : 2022-01-07
Anomaly Detection And Complex Event Processing Over Iot Data Streams written by Patrick Schneider and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-07 with Computers categories.
Anomaly Detection and Complex Event Processing over IoT Data Streams: With Application to eHealth and Patient Data Monitoring presents advanced processing techniques for IoT data streams and the anomaly detection algorithms over them. The book brings new advances and generalized techniques for processing IoT data streams, semantic data enrichment with contextual information at Edge, Fog and Cloud as well as complex event processing in IoT applications. The book comprises fundamental models, concepts and algorithms, architectures and technological solutions as well as their application to eHealth. Case studies, such as the bio-metric signals stream processing are presented –the massive amount of raw ECG signals from the sensors are processed dynamically across the data pipeline and classified with modern machine learning approaches including the Hierarchical Temporal Memory and Deep Learning algorithms. The book discusses adaptive solutions to IoT stream processing that can be extended to different use cases from different fields of eHealth, to enable a complex analysis of patient data in a historical, predictive and even prescriptive application scenarios. The book ends with a discussion on ethics, emerging research trends, issues and challenges of IoT data stream processing. Provides the state-of-the-art in IoT Data Stream Processing, Semantic Data Enrichment, Reasoning and Knowledge Covers extraction (Anomaly Detection) Illustrates new, scalable and reliable processing techniques based on IoT stream technologies Offers applications to new, real-time anomaly detection scenarios in the health domain
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.
Anomaly Detection In Streaming Data
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Author : Pascal Weiß
language : en
Publisher:
Release Date : 2016
Anomaly Detection In Streaming Data written by Pascal Weiß and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Intelligent Information And Database Systems
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Author : Ngoc Thanh Nguyen
language : en
Publisher: Springer Nature
Release Date :
Intelligent Information And Database Systems written by Ngoc Thanh Nguyen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Extreme Value Theory
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Author : Laurens de Haan
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-12-09
Extreme Value Theory written by Laurens de Haan and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-12-09 with Mathematics categories.
Focuses on theoretical results along with applications All the main topics covering the heart of the subject are introduced to the reader in a systematic fashion Concentration is on the probabilistic and statistical aspects of extreme values Excellent introduction to extreme value theory at the graduate level, requiring only some mathematical maturity
Machine Learning For Streaming Data With Python
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Author : Joos Korstanje
language : en
Publisher: Packt Publishing
Release Date : 2022-07-15
Machine Learning For Streaming Data With Python written by Joos Korstanje and has been published by Packt Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-15 with categories.
Apply machine learning to streaming data with the help of practical examples, and deal with challenges that surround streaming Key Features: Work on streaming use cases that are not taught in most data science courses Gain experience with state-of-the-art tools for streaming data Mitigate various challenges while handling streaming data Book Description: Streaming data is the new top technology to watch out for in the field of data science and machine learning. As business needs become more demanding, many use cases require real-time analysis as well as real-time machine learning. This book will help you to get up to speed with data analytics for streaming data and focus strongly on adapting machine learning and other analytics to the case of streaming data. You will first learn about the architecture for streaming and real-time machine learning. Next, you will look at the state-of-the-art frameworks for streaming data like River. Later chapters will focus on various industrial use cases for streaming data like Online Anomaly Detection and others. As you progress, you will discover various challenges and learn how to mitigate them. In addition to this, you will learn best practices that will help you use streaming data to generate real-time insights. By the end of this book, you will have gained the confidence you need to stream data in your machine learning models. What You Will Learn: Understand the challenges and advantages of working with streaming data Develop real-time insights from streaming data Understand the implementation of streaming data with various use cases to boost your knowledge Develop a PCA alternative that can work on real-time data Explore best practices for handling streaming data that you absolutely need to remember Develop an API for real-time machine learning inference Who this book is for: This book is for data scientists and machine learning engineers who have a background in machine learning, are practice and technology-oriented, and want to learn how to apply machine learning to streaming data through practical examples with modern technologies. Although an understanding of basic Python and machine learning concepts is a must, no prior knowledge of streaming is required.
Using End To End Bandwidth Estimates For Anomaly Detection Beyond Enterprise Boundaries
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Author :
language : en
Publisher:
Release Date : 2010
Using End To End Bandwidth Estimates For Anomaly Detection Beyond Enterprise Boundaries written by 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.
Visual Analytics Of Anomaly Detection In Large Data Streams
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Author : Ming C. Hao
language : en
Publisher:
Release Date : 2012
Visual Analytics Of Anomaly Detection In Large Data Streams written by Ming C. Hao and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
Outlier Detection For Temporal Data
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Author : Manish Gupta
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Outlier Detection For Temporal Data written by Manish Gupta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
Outlier (or anomaly) detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Initial research in outlier detection focused on time series-based outliers (in statistics). Since then, outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, network data, time series data, spatial data, and spatio-temporal data. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. A large number of applications generate temporal datasets. For example, in our everyday life, various kinds of records like credit, personnel, financial, judicial, medical, etc., are all temporal. This stresses the need for an organized and detailed study of outliers with respect to such temporal data. In the past decade, there has been a lot of research on various forms of temporal data including consecutive data snapshots, series of data snapshots and data streams. Besides the initial work on time series, researchers have focused on rich forms of data including multiple data streams, spatio-temporal data, network data, community distribution data, etc. Compared to general outlier detection, techniques for temporal outlier detection are very different. In this book, we will present an organized picture of both recent and past research in temporal outlier detection. We start with the basics and then ramp up the reader to the main ideas in state-of-the-art outlier detection techniques. We motivate the importance of temporal outlier detection and brief the challenges beyond usual outlier detection. Then, we list down a taxonomy of proposed techniques for temporal outlier detection. Such techniques broadly include statistical techniques (like AR models, Markov models, histograms, neural networks), distance- and density-based approaches, grouping-based approaches (clustering, community detection), network-based approaches, and spatio-temporal outlier detection approaches. We summarize by presenting a wide collection of applications where temporal outlier detection techniques have been applied to discover interesting outliers. Table of Contents: Preface / Acknowledgments / Figure Credits / Introduction and Challenges / Outlier Detection for Time Series and Data Sequences / Outlier Detection for Data Streams / Outlier Detection for Distributed Data Streams / Outlier Detection for Spatio-Temporal Data / Outlier Detection for Temporal Network Data / Applications of Outlier Detection for Temporal Data / Conclusions and Research Directions / Bibliography / Authors' Biographies