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Deep packet inspection (DPI) acts as a tool to control and classify incoming network traffic depending on users,content, applications and becomes a very important aspect of every network today. Instead of utilising only incoming packet header information, Internet service providers utilise DPI for security purposes, flow management, and routing. The high‐performance challenges that face DPI techniques to manage line‐speed packet processing with restricted available memory are the increasing traffic volumes and link rates. Bloom (BF) and quotient (QF) filters are effective randomised data structures for performing approximate membership queries. They have been employed in many networking applications including DPI. Nevertheless, they consume more calculations which degrade the system performance. Consequently, in this paper, the authors propose a new variant of membership query data structure, called quotient‐based Cuckoo filter (QCF), that reflects a merging process between QF and CF to minimise the calculation overhead and employing it in a DPI system. QCF employs only two hash functions and consumes less calculation overhead than BF, QF, and CF. The implementation results show that using QCF as an identification tool in a DPI system result in a time improvement up to 77% over CF and up to 98% over BF and QF.

Deep packet inspection (DPI) represents the major process in network intrusion detection and prevention systems. In DPI each security threat is represented as a signature, and the payload of every incoming data packet is matched against the set of current signatures. Moreover, DPI is also used for other networking applications such as packet classification, quality of service techniques, protocol identification and so on. DPI exhausts extra central processing unit and memory resources, and as a result, several attempts have been proposed to improve this process. In this study, the authors proposed a fast two‐dimensional (2D) filter with low false positive (FP) rate for DPI purposes. It consists of 2D array that employs single hash function and has very low FP rate. Using this filter as an identification tool in a DPI technique will result in more accurate and higher throughput than other systems that employ Bloom (BFs) and quotient filters (QFs). Our experiments show that the proposed solution has time improvement up to 94% over others that employ BFs or QFs and the achieved average throughput is 1.8 Gbps.