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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.
ICCAD serves EDA and design professionals, highlighting new challenges and innovative solutions for integrated circuit design technology and systems
As more and more of our data is stored remotely, accessing that data wherever and whenever it is needed is a critical concern. More concerning is managing the databanks and storage space necessary to enable cloud systems. Resource Management of Mobile Cloud Computing Networks and Environments reports on the latest advances in the development of computationally intensive and cloud-based applications. Covering a wide range of problems, solutions, and perspectives, this book is a scholarly resource for specialists and end-users alike making use of the latest cloud technologies.
The third SIAM International Conference on Data Mining provided an open forum for the presentation, discussion and development of innovative algorithms, software and theories for data mining applications and data intensive computation. This volume includes 21 research papers.
Time Series Analysis (TSA) and Applications offers a dense content of current research and development in the field of data science. The book presents time series from a multidisciplinary approach that covers a wide range of sectors ranging from biostatistics to renewable energy forecasting. Contrary to previous literatures on time, serious readers will discover the potential of TSA in areas other than finance or weather forecasting. The choice of the algorithmic transform for different scenarios, which is a key determinant in the application of TSA, can be understood through the diverse domain applications. Readers looking for deep understanding and practicability of TSA will be delighted. Early career researchers too will appreciate the technicalities and refined mathematical complexities surrounding TSA. Our wish is that this book adds to the body of TSA knowledge and opens up avenues for those who are looking forward to applying TSA in their own context.
We feel greatly honoured to have been assigned the job of organizing the AICTE Sponsored International Conference on Application of AI, ML, DL, Big Data on Recent Societal Issues (ICARSI’2023) on April 21 & April 22,2023 at Saveetha Engineering College. The international conference is a platform that brings together the brightest minds from across the globe to share their ideas and insights on the recent societal issues with Artificial intelligence, Machine Learning, Deep Learning, Big data and emerging technologies. With an aim to promote collaboration and foster innovation, this conference promises to be a melting pot of ideas and knowledge sharing.
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
The computer game industry is clearly growing in the direction of multiplayer, online games. Understanding the demands of games on IP (Internet Protocol) networks is essential for ISP (Internet Service Provider) engineers to develop appropriate IP services. Correspondingly, knowledge of the underlying network's capabilities is vital for game developers. Networking and Online Games concisely draws together and illustrates the overlapping and interacting technical concerns of these sectors. The text explains the principles behind modern multiplayer communication systems and the techniques underlying contemporary networked games. The traffic patterns that modern games impose on networks, and how network performance and service level limitations impact on game designers and player experiences, are covered in-depth, giving the reader the knowledge necessary to develop better gaming products and network services. Examples of real-world multiplayer online games illustrate the theory throughout. Networking and Online Games: Provides a comprehensive, cutting-edge guide to the development and service provision needs of online, networked games. Contrasts the considerations of ISPs (e.g. predicting traffic loads) with those of game developers (e.g. sources of lag/jitter), clarifying coinciding requirements. Explains how different technologies such as cable, ADSL (Asymmetric Digital Subscriber Line) and wireless, etc., affect online game-play experience, and how different game styles impose varying traffic dynamics and requirements on the network. Discusses future directions brought by emerging technologies such as UMTS (Universal Mobile Telephone Service), GPRS (General Packet Radio Service), Wireless LANs, IP service Quality, and NAPT/NAT (Network Address Port Translation/Network Address Translation) Illustrates the concepts using high-level examples of existing multiplayer online games (such as Quake III Arena, Wolfenstein Enemy Territory, and Half-Life 2). Networking and Online Games will be an invaluable resource for games developers, engineers and technicians at Internet Service Providers, as well as advanced undergraduate and graduate students in Electrical Engineering, Computer Science and Multimedia Engineering.
Structural health monitoring is an extremely important methodology in evaluating the 'health' of a structure by assessing the level of deterioration and remaining service life of civil infrastructure systems. This book reviews key developments in research, technologies and applications in this area of civil engineering. It discusses ways of obtaining and analysing data, sensor technologies and methods of sensing changes in structural performance characteristics. It also discusses data transmission and the application of both individual technologies and entire systems to bridges and buildings.With its distinguished editors and international team of contributors, Structural health monitoring of civil infrastructure systems is a valuable reference for students in civil and structural engineering programs as well as those studying sensors, data analysis and transmission at universities. It will also be an important source for practicing civil engineers and designers, engineers and researchers developing sensors, network systems and methods of data transmission and analysis, policy makers, inspectors and those responsible for the safety and service life of civil infrastructure. - Reviews key developments in research, technologies and applications - Discusses systems used to obtain and analyse data and sensor technologies - Assesses methods of sensing changes in structural performance
Use data analytics to drive innovation and value throughout your network infrastructure Network and IT professionals capture immense amounts of data from their networks. Buried in this data are multiple opportunities to solve and avoid problems, strengthen security, and improve network performance. To achieve these goals, IT networking experts need a solid understanding of data science, and data scientists need a firm grasp of modern networking concepts. Data Analytics for IT Networks fills these knowledge gaps, allowing both groups to drive unprecedented value from telemetry, event analytics, network infrastructure metadata, and other network data sources. Drawing on his pioneering experience applying data science to large-scale Cisco networks, John Garrett introduces the specific data science methodologies and algorithms network and IT professionals need, and helps data scientists understand contemporary network technologies, applications, and data sources. After establishing this shared understanding, Garrett shows how to uncover innovative use cases that integrate data science algorithms with network data. He concludes with several hands-on, Python-based case studies reflecting Cisco Customer Experience (CX) engineers’ supporting its largest customers. These are designed to serve as templates for developing custom solutions ranging from advanced troubleshooting to service assurance. Understand the data analytics landscape and its opportunities in Networking See how elements of an analytics solution come together in the practical use cases Explore and access network data sources, and choose the right data for your problem Innovate more successfully by understanding mental models and cognitive biases Walk through common analytics use cases from many industries, and adapt them to your environment Uncover new data science use cases for optimizing large networks Master proven algorithms, models, and methodologies for solving network problems Adapt use cases built with traditional statistical methods Use data science to improve network infrastructure analysisAnalyze control and data planes with greater sophistication Fully leverage your existing Cisco tools to collect, analyze, and visualize data