Anomaly Detection: Techniques and Applications


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Series: Computer Science, Technology and Applications

BISAC: COM014000

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 suspicion by differing significantly from the majority of data is called anomaly detection.

With progress in technology and the widespread use of data for the purpose of business, spam faced by individuals and companies is 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 Contents


Chapter 1. Secured and Automated Key Establishment and Data Forwarding Scheme for the Internet of Things
(N. V. Kousik, R. Arshath Raja, N. Yuvaraj and S. Anbu Chelian – Associate Professor, School of Computing and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India, et al.)

Chapter 2. A Study of Enhanced Anomaly Detection Techniques Using Evolutionary-Based Optimization for Improved Detection Accuracy
(Vidhya Sathish, PhD, and Sheik Abdul Khader, PhD – Assistant Professor, Shri Krishnaswamy College for Women, Chennai, et al.)

Chapter 3. Anomaly Detection and Applications
(Huichen Shu, PhD – Clausthal University of Technology, Germany)

Chapter 4. An Evolutionary Study on SIoT (Social Internet of Things)
(Dinesh Mavaluru and Jayabrabu Ramakrishnan – Department of Information Technology, College of Computing and Informatics, Saudi Electronic University, Saudi Arabia)

Chapter 5. A Critical Study on Advanced Machine Learning Classification of Human Emotional State Recognition Using Facial Expressions
(Jayabrabu Ramakrishnan, PhD and Dinesh Mavaluru, PhD – Department of Information Technology and Security, College of Computer Science and Information Technology, Jazan University, Saudi Arabia)

Chapter 6. Anomaly Detection for Data Aggregation in Wireless Sensor Networks
(Beski Prabaharan (Associate Professor, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab) and Saira Banu (Professor, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India))

Chapter 7. Algorithm for Real Time Anomalous User Detection from Call Detail Record
(Saira Banu, PhD, and Beski Prabaharan, PhD – Associate Professor, Department of Information Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India, et al.)

Chapter 8. Secured Transactions from the Anomaly User Using 2 Way SSL
(Dr. Syed Mustafa and Mr. Madhivanan – Professor and Head, Department of Information Science and Engineering, HKBK College of Engineering, Bangalore, Affiliated to Visvesvaraya Technological University, India, et al.)



“A lot of learning happens by observing what happens normally. In many cases, identifying what happens in unusual contexts may also provide insight. In the computational space, “anomaly detection” in various available data is used in various practical applications, with real implications on people’s daily lives. Anomalies refer to data deviations from a normal state of observed behaviors, beyond particular parameters. Anomalies are those data points at the far ends of the min-max range, the ends of the normal curve, the isolate datapoints in scatterplots, the unclustered datapoints in a 2D or 3D data representation. With multidimensional data, these are the datapoints that do not cluster. Saira Banu Atham, Shriram Raghunathan, Dinesh Mavaluru, and A. Syed Mustafa are co-editors of Anomaly Detection: Techniques and Applications, which highlights some of the techniques and technologies to achieve anomaly detection in various systems… Read more >>>– Shalin Hai-Jew, Instructional Designer/Researcher, Kansas State University. Published in C2C Digital Magazine (Fall 2021 / Winter 2022).

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