ACTOR CRITIC APPROACH BASED ANOMALY DETECTION FOR EDGE COMPUTING ENVIRONMENTS

Abstract

The pivotal role of data security in mobile edge-computing environments forms the foundation for the proposed work. Anomalies and outliers in the sensory data due to network attacks will be a prominent concern in real time. Sensor samples will be considered from a set of sensors at a particular time instant as far as the confidence level on the decision remains on par with the desired value. A “true” on the hypothesis test eventually means that the sensor has shown signs of anomaly or abnormality and samples have to be immediately ceased from being retrieved from the sensor. A deep learning Actor-Criticbased Reinforcement algorithm proposed will be able to detect anomalies in the form of binary indicators and hence decide when to withdraw from receiving further samples from specific sensors. The posterior trust value influences the value of the confidence interval and hence the probability of anomaly detection. The paper exercises a single-tailed normal function to determine the range of the posterior trust metric. The decision taken by the prediction model will be able to detect anomalies with a good percentage of anomaly detection accuracy