| Popis: |
The enormous growth of network traffic, in conjunction with the need to monitor even larger and more capillary network deployments, poses a significant scalability challenge to the network monitoring process. We believe that a promising way to address this challenge consists in rethinking monitoring tasks as partially performed inside the network itself. Indeed, in-network monitoring devices, such as traffic capturing probes, may be instructed to perform intelligent processing and filtering mechanisms, so that the amount of data ultimately delivered to central monitoring entities can be significantly reduced to that strictly necessary for a more careful and fine-grained data inspection. In such a direction, this chapter focuses on the design and implementation of an hardware-based front-end pre-filter for the topmost known Snort Intrusion Detection System (IDS). Motivated by the practical impossibility to pack a large amount of legacy Snort rules over a resource-constrained hardware device, we specifically address the question on how Snort rules should be adapted and simplified so that they can be supported over a commercial, low-end, Field Programmable Gate Array (FPGA) board, meanwhile providing good filtering performance. Focusing on about one thousand Snort rules randomly drawn from the complete rule set, we experimentally determine how these rules can be simplified meanwhile retaining a comparable detection performance with respect to the original, non adapted, rules, when applied over a “training” dataset composed of a relatively large traffic trace collected from a regional ISP backbone link. We then validate the performance of the adapted rules against additional collected traffic traces. We show that about 1000 adapted Snort rules can be supported over a low-end FPGA based Snort pre-filter, with 93% data reduction efficiency. |
