Increasing the power efficiency of Bloom filters for network string matching

Although software based techniques are widely accepted in computer security systems, there is a growing interest to utilize hardware opportunities in order to compensate for the network bandwidth increases. Recently, hardware based virus protection systems have started to emerge. These type of hardware systems work by identifying the malicious content and removing it from the network streams. In principle, they make use of string matching. Bit by bit, they compare the virus signatures with the bit strings in the network. The Bloom filters are ideal data structures for string matching. Nonetheless, they consume large power when many of them used in parallel to match different virus signatures. In this paper, we propose a new type of Bloom filter architecture which exploits well-known pipelining technique. © 2006 IEEE

MULTI-GIGABIT PATTERN FOR DATA IN NETWORK SECURITY

In the current scenario network security is emerging the world. Matching large sets of patterns against an incoming stream of data is a fundamental task in several fields such as network security or computational biology. High-speed network intrusion detection systems (IDS) rely on efficient pattern matching techniques to analyze the packet payload and make decisions on the significance of the packet body. However, matching the streaming payload bytes against thousands of patterns at multi-gigabit rates is computationally intensive. Various techniques have been proposed in past but the performance of the system is reducing because of multi-gigabit rates.Pattern matching is a significant issue in intrusion detection systems, but by no means the only one. Handling multi-content rules, reordering, and reassembling incoming packets are also significant for system performance. We present two pattern matching techniques to compare incoming packets against intrusion detection search patterns. The first approach, decoded partial CAM (DpCAM), pre-decodes incoming characters, aligns the decoded data, and performs logical AND on them to produce the match signal for each pattern. The second approach, perfect hashing memory (PHmem), uses perfect hashing to determine a unique memory location that contains the search pattern and a comparison between incoming data and memory output to determine the match. The suggested methods have implemented in vhdl coding and we use Xilinx for synthesis