As traffic volumes and the types of analysis grow, network intrusion detection systems (NIDS) face a continuous scaling challenge. Management realities, however, limit NIDS hardware upgrades to occur typically once every 3-5 years. Given that traffic patterns can change dramatically, this leaves a significant scaling challenge in the interim. This motivates the need for practical solutions that can help administrators better utilize and augment their existing NIDS infrastructure. To this end, we design a general architecture for network-wide NIDS deployment that leverages three scaling opportunities: on-path distribution to split responsibilities, replicating traffic to NIDS clusters, and aggregating intermediate results to split expensive NIDS processing. The challenge here is to balance both the compute load across the network and the total communication cost incurred via replication and aggregation. We implement a backwards-compatible mechanism to enable existing NIDS infrastructure to leverage these benefits. Using emulated and trace-driven evaluations on several real-world network topologies, we show that our proposal can substantially reduce the maximum computation load, provide better resilience under traffic variability, and offer improved detection coverage.
Take REF as example, it proposes a general IDS architecture with splitting responsibility to other nodes by on-path distribution, replicating traffic to off-path nodes (e.g. IDS clusters) and aggregating result to split costly IDS processing.
New opportunities for load balancing in network-wide intrusion detection systems
CoNEXT '12
Computer Science