Efficient Multistriding of Large Non-deterministic Finite State Automata for Deep Packet Inspection

Multistride automata speed up input matching because each multistriding transformation halves the size of the input string, leading to a potential 2x speedup. However, up to now little effort has been spent in optimizing the building process of multistride automata, with the result that current algorithms cannot be applied to real-life, large automata such as the ones used in commercial IDSs, because the time and the memory space needed to create the new automaton quickly becomes unfeasible. In this paper, new algorithms for efficient building of multistride NFAs for packet inspection are presented, explaining how these new techniques can outperform the previous algorithms in terms of required time and memory usag

Scalable Algorithms for NFA Multi-Striding and NFA-Based Deep Packet Inspection on GPUs

Finite state automata (FSA) are used by many network processing applications to match complex sets of regular expressions in network packets. In order to make FSA-based matching possible even at the ever-increasing speed of modern networks, multi-striding has been introduced. This technique increases input parallelism by transforming the classical FSA that consumes input byte by byte into an equivalent one that consumes input in larger units. However, the algorithms used today for this transformation are so complex that they often result unfeasible for large and complex rule sets. This paper presents a set of new algorithms that extend the applicability of multi-striding to complex rule sets. These algorithms can transform non-deterministic finite automata (NFA) into their multi-stride form with reduced memory and time requirements. Moreover, they exploit the massive parallelism of graphical processing units for NFA-based matching. The final result is a boost of the overall processing speed on typical regex-based packet processing applications, with a speedup of almost one order of magnitude compared to the current state-of-the-art algorithms