Searching for multiple string patterns in a stream of data is a computationally expensive task. The speed of the search pattern module determines the overall performance of deep packet inspection firewalls, intrusion detection systems (IDS), and intrusion prevention systems (IPS). For example, one open source IDS configured for 845 patterns, can sustain a throughput of only 50 Mbps running on a dual 1-GHz Pentium III system. Using such systems would not be practical for filtering high speed networks with over 1 Gbps traffic. Some of these systems are implemented with field programmable gate arrays (FPGA) so that they are fast and programmable. However, such FPGA filters tend to be too large to be mapped on to a single FPGA. By sharing the common sublogic in the design, we can effectively shrink the footprint of the filter. Then, for a large subset of the patterns, the logic area can be further reduced by using a memory based architecture. These design methods allow our filter for 2064 attack patterns to map onto a single Xilinx Spartan 3-XC3S2000 FPGA with a filtering rate of over 3 Gbps of network traffic.

An important goal for the smart grid is to give power grid operators like utilities a global view of the health of the power grid. As the smart grid is built-out, the size and complexity of the required communications network is increasing. In this paper we propose using software defined networks (SDN) for the smart grid, as SDN allow for a flexibly defined network (through programmability) along with superior performance and implementations based on industry standards. To demonstrate that SDN can be used as the underlying technology for power grid networking we have implemented a multi-rate, multicast network for phasor measurement unit (PMU) data. We compare the SDN solution to the conventional approach as well as two other advanced PMU networking methods, IP Multicast and GridStat and show that our method achieves lower latency and optimal network utilization.

The efficiency of the time synchronization service in wireless sensor networks is tightly connected to the design of the radio, the quality of the clocking hardware, and the synchronization algorithm employed. While improvements can be made on all levels of the system, over the last few years most work has focused on the algorithmic level to minimize message exchange and in radio architectures to provide accurate time-stamping mechanisms. Surprisingly, the influences of the underlying clock system and its impact on the overall synchronization accuracy has largely been unstudied. In this work, we investigate the impact of the clocking subsystem on the time synchronization service and address, in particular, the influence of changes in environmental temperature on clock drift in highly duty-cycled wireless sensor nodes. We also develop formulas that help the system architect choose the optimal resynchronization period to achieve a given synchronization accuracy. We find that the synchronization accuracy has a two region behavior. In the first region, the synchronization accuracy is limited by quantization error, while int he second region changes in environmental temperature impact the achievable accuracy. We verify our analytic results in simulation and real hardware experiments.

Most network routers and switches provide some protection against the network attacks. However, the rapidly increasing amount of damages reported over the past few years indicates the urgent need for tougher security. Deep-packet inspection is one of the solutions to capture packets that can not be identified using the traditional methods. It uses a list of signatures to scan the entire content of the packet, providing the means to filter harmful packets out of the network. Since one signature does not depend on the other, the filtering process has a high degree of parallelism. Most software and hardware deep-packet filters that are in use today execute the tasks under Von Neuman architecture. Such architecture can not fully take advantage of the parallelism. For instance, one of the most widely used network intrusion-detection systems, Snort, configured with 845 patterns, running on a dual 1-GHz Pentium III system, can sustain a throughput of only 50 Mbps. The poor performance is because of the fact that the processor is programmed to execute several tasks sequentially instead of simultaneously. We designed scalable deep-packet filters on field-programmable gate arrays (FPGAs) to search for all data-independent patterns simultaneously. With FPGAs, we have the ability to reprogram the filter when there are any changes to the signature set. The smallest full-pattern matcher implementation for the latest Snort NIDS fits in a single 400k Xilinx FPGA (Spartan 3-XC3S400) with a sustained throughput of 1.6 Gbps. Given a larger FPGA, the design can scale linearly to support a greater number of patterns, as well as higher data throughput.