NDLI: Pattern matching algorithms for intrusion detection and prevention system: A comparative analysis

Content Provider	IEEE Xplore Digital Library
Author	Gupta, V. Singh, M. Bhalla, V.K.
Copyright Year	2014
Description	Author affiliation: Comput. Sci. & Eng., Thapar Univ., Patiala, India (Gupta, V.; Singh, M.; Bhalla, V.K.)
Abstract	Intrusion Detection and Prevention Systems (IDPSs) are used to detect malicious activities of intruders and also prevent from the same. These systems use signatures of known attacks to detect them. Signatures are identified through pattern matching algorithm which is the heart of IDPSs. Due to technological advancements, network speed is increasing day by day, so pattern matching algorithm to be used in IDPS should be fast enough so as to match the network speed. Therefore choice of pattern matching algorithm is the critical to the performance of IDS and IPS. Several pattern matching algorithms exist in literature, but which pattern matching algorithm will give best performance for IDPS is not known at hand. So in this work four pattern matching algorithms namely Brute-force, RabinKarp, Boyer-Moore and Knuth-Morris-Pratt has been selected for the analysis. These single keyword matching algorithms are mainly used. Performance of pattern matching algorithms is analyzed in terms of run time by varying number of patterns and by varying size of network captured (pcap) file.
Starting Page	50
Ending Page	54
File Size	398315
Page Count	5
File Format	PDF
ISBN	9781479930784
e-ISBN	9781479930807
DOI	10.1109/ICACCI.2014.6968595
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-24
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Rabin-Karp Protocols Boyer-Moore Databases Pattern Matching Knuth-Morris-Pratt (KMP) Force Intrusion Detection and Prevention Systems (IDPSs) Intrusion detection Pattern matching
Content Type	Text
Resource Type	Article

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