Biblio

Packet classification is a key function in network security systems in SDN, which detect potential threats by matching the packet header bits and a given rule set. It needs to support multi-dimensional fields, large rule sets, and high throughput. Bit Vector-based packet classification methods can support multi-field matching and achieve a very high throughput, However, the range matching is still challenging. To address issue, this paper proposes a Range Supported Bit Vector (RSBV) algorithm for processing the range fields. RSBV uses specially designed codes to store the pre-computed results in memory, and the result of range matching is derived through pipelined Boolean operations. Through a two-dimensional modular architecture, the RSBV can operate at a high clock frequency and line-rate processing can be guaranteed. Experimental results show that for a 1K and 512-bit OpenFlow rule set, the RSBV can sustain a throughput of 520 Million Packets Per Second.

Deep packet inspection (DPI) is a critical component to prevent intrusion detection. This requires a detailed analysis of each network packet header and body. Although this is often done on dedicated high-power servers in most networked systems, mobile systems could potentially be vulnerable to attack if utilized on an unprotected network. In this case, having DPI hardware on the mobile system would be highly beneficial. Unfortunately, DPI hardware is generally area and power consuming, making its implementation difficult in mobile systems. We developed a memristor crossbar-based approach, inspired by memristor crossbar neuromorphic circuits, for a low-power, low-area, and high-throughput DPI system that examines both the header and body of a packet. Two key types of circuits are presented: static pattern matching and regular expression circuits. This system is able to reduce execution time and power consumption due to its high-density grid and massive parallelism. Independent searches are performed using low-power memristor crossbar arrays giving rise to a throughput of 160Gbps with no loss in the classification accuracy.