Description
Book SynopsisWhen 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.
Table of ContentsPreface; Secured and Automated Key Establishment and Data Forwarding Scheme for the Internet of Things; A Study of Enhanced Anomaly Detection Techniques Using Evolutionary-Based Optimization for Improved Detection Accuracy; Anomaly Detection and Applications; An Evolutionary Study on SIoT (Social Internet of Things); A Critical Study on Advanced Machine Learning Classification of Human Emotional State Recognition Using Facial Expressions; Anomaly Detection for Data Aggregation in Wireless Sensor Networks; Algorithm for Real Time Anomalous User Detection from Call Detail Record; Secured Transactions from the Anomaly User Using 2 Way SSL; Index.
