NETWORK INTRUSION DETECTION IN BIG DATASET USING SPARK AND WEKA

Due to rapid growth in network applications, Network attacks and intrusions have also been increased. Nowadays Intrusion detection systems require efficient and improved detection mechanism which could detect intrusive activities and serious threat to network security. Nowadays, huge amount of data is flowing every second; hence intrusion detection task became tedious. In our research work, we have compared and evaluatedthe performance of supervised data mining techniques on UNSW-NB15network dataset and Netflow records for various attacks using two different huge sizes of datasets on Weka and standalone cluster separately using Spark.