000			02024nam a22002177a 4500
003			OSt
005			20220107122852.0
008			190704b xxu||||| |||| 00| 0 eng d
040			_cIIITMK
100			_aVijitha V (92217027) _916040
245			_aAnomaly detection in set-top boxes using machine learning technologies
300			_aMSC DA 2017-2019
500			_aAnomaly detection is a common goal shared by dierent domains. Anomalies are generally dened as an error or as an unexpected event in reality .It is the practice of identifying items or events that do not conform to an expected behavior or do not correlate with other items in a dataset. Anomalies are detected in performance data of set top boxes using both supervised and unsupervised machine learning Techniques.The eectiveness of each algorithm was evaluated and compared.Which have shown sucient performance and sensitivity. Anomalies are patterns in data that do not conform to a welldened notion of normal behavior.Anomaly detection is the process of identifying unexpected items or events in datasets,which dier from the norm. In contrast to standard classication tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of the dataset into account. This challenge is known as unsupervised anomaly detection and is addressed in many practical applications.This project Principal Component Analysis (PCA) for feature selection, then unsupervised machinelearning techniques like K-Means are applied on the detected anomalies to dene their classes.supervised machine learning techniques like KNN,OCSVM and Isolation forest. The results show that OCSVM mostly give better results than Isolation forest , but for certain anomalies KNN give the best results
502			_bMSC DA _c2017-2019 _dINT _eDr T K Manoj Kumar
650			_aANOMALY DETECTION _916041
650			_aISOLATION FOREST _916042
650			_aKNN _916043
650			_aOCSVM _916044
650			_aMACHINE LEARNING TECHNIQUES _916045
942			_2ddc _cPR
999			_c6539 _d6539