Published Paper Details:

This paper gives an overview about how the anomalies are detected in the network data containing the bytes sent and received and session duration while a bit of data is being transferred. The anomalies in the data play an important role in detecting security breaches, performance issues and abnormal behaviours while the network is being transmitted. The identification of anomalies face challenges, particularly when it comes in finding and managing false positives i.e., incorrectly flagged activities as abnormal or malicious. This paper aims in detecting all the anomalies in the given data and finding false positives in the data set. This paper gives different views about the different methods to detect anomalies in the given network data based on the false positive rates. We discuss different algorithms that are used in this detection including machine-learning algorithms. Here we also discuss the causes, and additionally we display the graph of the anomalies in the data. The analysis is used for improving the effectiveness of Intrusion Detection Systems for future.