Hardware acceleration of regular expression matching is key to meeting the throughput requirements of state-of-the-art network intrusion detection systems (NIDSs) dictated by fast growing link speeds. This paper presents extensions to a programmable state machine, called B-FSM, which was initially optimized for string matching. These extensions enable direct support in hardware for essential regular expression features, such as character classes and case insensitivity. Moreover, they also allow the exploitation of regular expression properties that show up at the data structure level as common transitions shared between multiple states, resulting in storage reductions of up to 95% for five NIDS pattern sets analyzed. Additional instruction support based on a flexible integration within the B-FSM data structure increases the processing capabilities and enables the scaling to larger pattern collections. The new IBM Power Edge of NetworkTMprocessor employs the B-FSM technology to provide scanning capabilities at typical rates of 20-40 Gb/s.

The grid cells of the rat medial entorhinal cortex (MEC) show an increased firing frequency when the position of the animal correlates with multiple regions of the environment that are arranged in regular triangular grids. Here, we describe an artificial neural network based on a twisted torus topology, which allows for the generation of regular triangular grids. The association of the activity of pre-defined hippocampal place cells with entorhinal grid cells allows for a highly robust-to-noise calibration mechanism, suggesting a role for the hippocampal back-projections to the entorhinal cortex.