Large-Scale Network Intrusion Detection Based on Distributed Learning Algorithm

As network traffic bandwidth is increasing at an exponential rate, it's impossible to keep up with the speed of networks by just increasing the speed of processors. Besides, increasingly complex intrusion detection methods only add further to the pressure on Network Intrusion Detection System (NIDS) platforms, so the continuous increasing speed and throughput of network poses new challenges to NIDS. To make NIDS usable in Gigabit Ethernet, the ideal policy is using a load balancer to split the traffic data and forward those to different detection sensors, which can analyze the splitting data in parallel.