Abstract. The constant increase in link speeds and number of threats poses challenges to network intrusion detection systems (NIDS), which must cope with higher traffic throughput and perform even more complex per-packet processing. In this paper, we present an intrusion detection system based on the Snort open-source NIDS that exploits the underutilized computational power of modern graphics cards to offload the costly pattern matching operations from the CPU, and thus increase the overall processing throughput. Our prototype system, called Gnort, achieved a maximum traffic processing throughput of 2.3 Gbit/s using synthetic network traces, while when monitoring real traffic using a commodity Ethernet interface, it outperformed unmodified Snort by a factor of two. The results suggest that modern graphics cards can be used effectively to speed up intrusion detection systems, as well as other systems that involve pattern matching operations.

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Abstract. The expressive power of regular expressions has been often exploited in network intrusion detection systems, virus scanners, and spam filtering applications. However, the flexible pattern matching functionality of regular expressions in these systems comes with significant overheads in terms of both memory and CPU cycles, since every byte of the inspected input needs to be processed and compared against a large set of regular expressions. In this paper we present the design, implementation and evaluation of a regular expression matching engine running on graphics processing units (GPUs). The significant spare computational power and data parallelism capabilities of modern GPUs permits the efficient matching of multiple inputs at the same time against a large set of regular expressions. Our evaluation shows that regular expression matching on graphics hardware can result to a 48 times speedup over traditional CPU implementations and up to 16 Gbit/s in processing throughput. We demonstrate the feasibility of GPU regular expression matching by implementing it in the popular Snort intrusion detection system, which results to a 60% increase in the packet processing throughput.

Worms are self-replicating malicious programs that represent a major security threat for the Internet, as they can infect and damage a large number of vulnerable hosts at timescales where human responses are unlikely to be effective. Sophisticated worms that use precomputed hitlists of vulnerable targets are especially hard to contain, since they are harder to detect, and spread at rates where even automated defenses may not be able to react in a timely fashion.This paper examines a new proactive defense mechanism called Network Address Space Randomization (NASR) whose objective is to harden networks specifically against hitlist worms. The idea behind NASR is that hitlist information could be rendered stale if nodes are forced to frequently change their IP addresses. NASR limits or slows down hitlist worms and forces them to exhibit features that make them easier to contain at the perimeter. We explore the design space for NASR and present a prototype implementation as well as preliminary experiments examining the effectiveness and limitations of the approach.

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