Characterizing the Performance of Network Intrusion Detection Sensors

Network intrusion detection systems (NIDS) are becoming an important tool for protecting critical information and infrastructure. The quality of a NIDS is described by the percentage of true attacks detected combined with the number of false alerts. However, even a high-quality NIDS algorithm is not effective if its processing cost is too high, since the resulting loss of packets increases the probability that an attack is not detected. This study measures and compares two major components of the NIDS processing cost on a number of diverse systems to pinpoint performance bottlenecks and to determine the impact of operating system and architecture differences. Results show that even on moderate-speed networks, many systems are inadequate as NIDS platforms. Performance depends not only on the processor performance, but to a large extent also on the memory system. Recent trends in processor microarchitecture towards deep pipelines have a negative impact on the systems NIDS capabilities, and multiprocessor architectures usually do not lead to significant performance improvements. Overall, these results provide valuable guidelines for NIDS developers and adopters for choosing a suitable platform, and highlight the need to consider processing cost when developing and evaluating NIDS techniques.