Anomaly detection in set-top boxes using machine learning technologies

Anomaly 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

MSC DA 2017-2019 INT Dr T K Manoj Kumar