Anomaly detection in set-top boxes using machine learning technologies (Record no. 6539)

MARC details
000 -LEADER
fixed length control field 02024nam a22002177a 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220107122852.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 190704b xxu||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Transcribing agency IIITMK
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Vijitha V (92217027)
9 (RLIN) 16040
245 ## - TITLE STATEMENT
Title Anomaly detection in set-top boxes using machine learning technologies
300 ## - PHYSICAL DESCRIPTION
Extent MSC DA 2017-2019
500 ## - GENERAL NOTE
General note Anomaly detection is a common goal shared by dierent domains. Anomalies<br/>are generally dened as an error or as an unexpected event in reality .It is<br/>the practice of identifying items or events that do not conform to an expected<br/>behavior or do not correlate with other items in a dataset. Anomalies are<br/>detected in performance data of set top boxes using both supervised and unsupervised<br/>machine learning Techniques.The eectiveness of each algorithm<br/>was evaluated and compared.Which have shown sucient performance and<br/>sensitivity. Anomalies are patterns in data that do not conform to a welldened<br/>notion of normal behavior.Anomaly detection is the process of identifying<br/>unexpected items or events in datasets,which dier from the norm.<br/>In contrast to standard classication tasks, anomaly detection is often applied<br/>on unlabeled data, taking only the internal structure of the dataset into<br/>account. This challenge is known as unsupervised anomaly detection and<br/>is addressed in many practical applications.This project Principal Component<br/>Analysis (PCA) for feature selection, then unsupervised machinelearning<br/>techniques like K-Means are applied on the detected anomalies to dene<br/>their classes.supervised machine learning techniques like KNN,OCSVM and<br/>Isolation forest. The results show that OCSVM mostly give better results than<br/>Isolation forest , but for certain anomalies KNN give the best results
502 ## - DISSERTATION NOTE
Degree type MSC DA
Name of granting institution 2017-2019
Year degree granted INT
-- Dr T K Manoj Kumar
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element ANOMALY DETECTION
9 (RLIN) 16041
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Topical term or geographic name entry element ISOLATION FOREST
9 (RLIN) 16042
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element KNN
9 (RLIN) 16043
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Topical term or geographic name entry element OCSVM
9 (RLIN) 16044
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element MACHINE LEARNING TECHNIQUES
9 (RLIN) 16045
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
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    Dewey Decimal Classification     IIITM-K Kerala University of Digital Sciences, Innovation and Technology Knowledge Centre   04/07/2019   R-1566 04/07/2019 04/07/2019 Project Reports