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| Author | : Nurul Asyikin Samsuddin |
| Publisher | : |
| Total Pages | : 148 |
| Release | : 2013 |
| Genre | : Anomaly detection (Computer security) |
| ISBN | : |
| Author | : Nurul Asyikin Samsuddin |
| Publisher | : |
| Total Pages | : |
| Release | : 2013 |
| Genre | : Anomaly detection (Computer security) |
| ISBN | : |
| Author | : Danfeng (Daphne)Yao |
| Publisher | : Springer Nature |
| Total Pages | : 157 |
| Release | : 2022-06-01 |
| Genre | : Computers |
| ISBN | : 3031023544 |
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 | : Saira Banu |
| Publisher | : Nova Science Publishers |
| Total Pages | : 0 |
| Release | : 2021 |
| Genre | : Anomaly detection (Computer security) |
| ISBN | : 9781536192643 |
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 | : Brian Buzzelli |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 175 |
| Release | : 2022-10-19 |
| Genre | : Computers |
| ISBN | : 1098136896 |
Data quality will either make you or break you in the financial services industry. Missing prices, wrong market values, trading violations, client performance restatements, and incorrect regulatory filings can all lead to harsh penalties, lost clients, and financial disaster. This practical guide provides data analysts, data scientists, and data practitioners in financial services firms with the framework to apply manufacturing principles to financial data management, understand data dimensions, and engineer precise data quality tolerances at the datum level and integrate them into your data processing pipelines. You'll get invaluable advice on how to: Evaluate data dimensions and how they apply to different data types and use cases Determine data quality tolerances for your data quality specification Choose the points along the data processing pipeline where data quality should be assessed and measured Apply tailored data governance frameworks within a business or technical function or across an organization Precisely align data with applications and data processing pipelines And more
| Author | : Abdelkader Hameurlain |
| Publisher | : Springer Nature |
| Total Pages | : 139 |
| Release | : 2020-08-12 |
| Genre | : Computers |
| ISBN | : 3662621991 |
The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing (e.g., computing resources, services, metadata, data sources) across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 43rd issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains five revised selected regular papers. Topics covered include classification tasks, machine learning algorithms, top-k queries, business process redesign and a knowledge capitalization framework.
| Author | : V. Çağrı Güngör |
| Publisher | : CRC Press |
| Total Pages | : 406 |
| Release | : 2017-12-19 |
| Genre | : Computers |
| ISBN | : 1466500522 |
The collaborative nature of industrial wireless sensor networks (IWSNs) brings several advantages over traditional wired industrial monitoring and control systems, including self-organization, rapid deployment, flexibility, and inherent intelligent processing. In this regard, IWSNs play a vital role in creating more reliable, efficient, and productive industrial systems, thus improving companies’ competitiveness in the marketplace. Industrial Wireless Sensor Networks: Applications, Protocols, and Standards examines the current state of the art in industrial wireless sensor networks and outlines future directions for research. What Are the Main Challenges in Developing IWSN Systems? Featuring contributions by researchers around the world, this book explores the software and hardware platforms, protocols, and standards that are needed to address the unique challenges posed by IWSN systems. It offers an in-depth review of emerging and already deployed IWSN applications and technologies, and outlines technical issues and design objectives. In particular, the book covers radio technologies, energy harvesting techniques, and network and resource management. It also discusses issues critical to industrial applications, such as latency, fault tolerance, synchronization, real-time constraints, network security, and cross-layer design. A chapter on standards highlights the need for specific wireless communication standards for industrial applications. A Starting Point for Further Research Delving into wireless sensor networks from an industrial perspective, this comprehensive work provides readers with a better understanding of the potential advantages and research challenges of IWSN applications. A contemporary reference for anyone working at the cutting edge of industrial automation, communication systems, and networks, it will inspire further exploration in this promising research area.
| Author | : Theodor Borangiu |
| Publisher | : Springer Nature |
| Total Pages | : 556 |
| Release | : 2021-03-02 |
| Genre | : Technology & Engineering |
| ISBN | : 3030693732 |
The scientific theme of the book concerns “Manufacturing as a Service (MaaS)” which is developed in a layered cloud networked manufacturing perspective, from the shop floor resource sharing model to the virtual enterprise collaborative model, by distributing the cost of the manufacturing infrastructure - equipment, software, maintenance, networking - across all customers. MaaS is approached in terms of new models of service-oriented, knowledge-based manufacturing systems optimized and reality-aware, that deliver value to customer and manufacturer via Big data analytics, Internet of Things communications, Machine learning and Digital twins embedded in Cyber-Physical System frameworks. From product design to after-sales services, MaaS relies on the servitization of manufacturing operations such as: Design as a Service, Predict as a Service or Maintain as a service. The general scope of the book is to foster innovation in smart and sustainable manufacturing and logistics systems and in this context to promote concepts, methods and solutions for the digital transformation of manufacturing through service orientation in holonic and agent-based control with distributed intelligence. The book’s readership is comprised by researchers and engineers working in the manufacturing value chain area who develop and use digital control solutions in the ‘Industry of the Future’ vision. The book also addresses to master and Ph.D. students enrolled in Engineering Sciences programs.
| Author | : Hadis Karimipour |
| Publisher | : Springer Nature |
| Total Pages | : 328 |
| Release | : 2020-07-23 |
| Genre | : Computers |
| ISBN | : 3030455416 |
This book presents a comprehensive overview of security issues in Cyber Physical Systems (CPSs), by analyzing the issues and vulnerabilities in CPSs and examining state of the art security measures. Furthermore, this book proposes various defense strategies including intelligent attack and anomaly detection algorithms. Today’s technology is continually evolving towards interconnectivity among devices. This interconnectivity phenomenon is often referred to as Internet of Things (IoT). IoT technology is used to enhance the performance of systems in many applications. This integration of physical and cyber components within a system is associated with many benefits; these systems are often referred to as Cyber Physical Systems (CPSs). The CPSs and IoT technologies are used in many industries critical to our daily lives. CPSs have the potential to reduce costs, enhance mobility and independence of patients, and reach the body using minimally invasive techniques. Although this interconnectivity of devices can pave the road for immense advancement in technology and automation, the integration of network components into any system increases its vulnerability to cyber threats. Using internet networks to connect devices together creates access points for adversaries. Considering the critical applications of some of these devices, adversaries have the potential of exploiting sensitive data and interrupting the functionality of critical infrastructure. Practitioners working in system security, cyber security & security and privacy will find this book valuable as a reference. Researchers and scientists concentrating on computer systems, large-scale complex systems, and artificial intelligence will also find this book useful as a reference.
| Author | : Kim Phuc Tran |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2022 |
| Genre | : |
| ISBN | : 9783030838201 |
This book introduces the latest research on advanced control charts and new machine learning approaches to detect abnormalities in the smart manufacturing process. By approaching anomaly detection using both statistics and machine learning, the book promotes interdisciplinary cooperation between the research communities, to jointly develop new anomaly detection approaches that are more suitable for the 4.0 Industrial Revolution. The book provides ready-to-use algorithms and parameter sheets, enabling readers to design advanced control charts and machine learning-based approaches for anomaly detection in manufacturing. Case studies are introduced in each chapter to help practitioners easily apply these tools to real-world manufacturing processes. The book is of interest to researchers, industrial experts, and postgraduate students in the fields of industrial engineering, automation, statistical learning, and manufacturing industries.
