How Big Data can detect network anomalies based on the IP Size distribution

Conventional intrusion and detection methods to evaluate network anomalies have several impasses for large-scale datasets over ultra-blazing speed networks with disparate sources of data coming in with high-velocity and high-volume. Machine learning and artificial intelligence techniques mine the massive network datasets with IP size distribution can perform dichotomy of flow-based network traffic to diagnose the network anomalies as an effective solution. The simplex and similar size of the IP distribution with same attributes hitting the flow-based analysis on regular time intervals display the symptoms of network anomalies. Various flow-based monitoring tools such as nProbe and FlowMon Probe detect these intrusions on gigabit-sized networks. Two key detection techniques of NetFlow-based on large-scale and high-speed networks are: a) the misuse intrusion method; b) network anomaly detection method.