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Payload extractor design pattern2/18/2023 The authors first illustrate and discuss the techniques of various string pattern-matching algorithms. The characteristics of medical language are emphasized in this regard, the best algorithm of those reviewed is proposed, and detailed evaluations of time complexity for processing medical texts are provided. To choose the most appropriate algorithm, distinctive features of the medical language must be taken into account. The authors consider the problem of exact string pattern matching using algorithms that do not require any preprocessing. We demonstrate the feasibility of GPU regular expression matching by implementing it in the popular Snort intrusion detection system, which results to a 60% increase in the packet processing throughput. Our evaluation shows that regular expression matching on graphics hardware can result to a 48 times speedup over traditional CPU implementations and up to 16 Gbit/s in processing throughput. The significant spare computational power and data parallelism capabilities of modern GPUs permits the efficient matching of multiple inputs at the same time against a large set of regular expressions. In this paper we present the design, implementation and evaluation of a regular expression matching engine running on graphics processing units (GPUs). However, the flexible pattern matching functionality of regular expressions in these systems comes with significant overheads in terms of both memory and CPU cycles, since every byte of the inspected input needs to be processed and compared against a large set of regular expressions. The expressive power of regular expressions has been often exploited in network intrusion detection systems, virus scanners, and spam filtering applications. ![]() Finally, we prove the superiority of our signature maintenance method through comparison analysis against other existing methods on the basis of various evaluation metrics. We prove the feasibility of the proposed method by developing a prototype system and deploying it in a real operational network. Signature weight is periodically calculated and updated to adapt to the changes of network environment. For the signature maintenance, we define a new metric, the so-called signature weight, that reflects its potential ability to identify traffic. Our approach automatically constructs header signatures for traffic identification and only retains the most significant signatures in the signature repository to save memory space and to improve matching speed. In this article, we propose an efficient method for header signature maintenance. However, header signature–based identification does have a significant flaw in that the volume of header signatures increases rapidly over time as a number of applications emerge, evolve, and vanish. Header signature–based identification using the 3-tuple (Internet Protocol address, port number, and L4 protocol) within a packet header has garnered a lot of attention because it overcomes the limitations faced by the payload-based method, such as encryption, privacy concerns, and computational overhead. Various traffic identification methods have been proposed with the focus on application-level traffic analysis. The results of the mathematical and simulation modeling are compared. The general architecture of the DPI system is taken into account when building a simulation model. The DPI simulation model in GPSS World is briefly described. ![]() The formula of the final processing time of requests of the physical entity of DPI analyser is presented. The concept of the Ventcel-Ovcharov model with equal mutual assistance is to combine channels into groups for the joint service of requests. A mathematical model for calculating physical entity of DPI analyser in the DPI system, based on the model by Ventcel-Ovcharov, is provided. This paper describes a mathematical model for calculating the analysis time for a given number of processors in a specialized server of the DPI system. The develop formalization of the ratio of flows and packets. ![]() Some initial data for calculating DPI system based on traffic statistics have been formalized. This article describes the specialized servers that build up the DPI system architecture.
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