Open AccessDEVELOPING A FASTER PATTERN MATCHING ALGORITHMS FOR INTRUSION DETECTION SYSTEM
Author(s) -
Ibrahim Obeidat,
Mazen Ibrahim AlZubi
Publication year - 2019
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.18.3.1520
Subject(s) - computer science , string searching algorithm , malware , pattern matching , matching (statistics) , algorithm , intrusion detection system , string (physics) , data mining , system call , process (computing) , host (biology) , field (mathematics) , artificial intelligence , computer security , operating system , mathematics , ecology , statistics , biology , pure mathematics , mathematical physics
Fast pattern matching algorithms mostly used by IDS, which are considered one of the important systems used to monitor and analyze host and network traffic. Their main function is to detect various types of malicious and malware files by examining incoming and outgoing data through the network. As the network speed growing, the malicious behavior and malware files are increasing; the pattern matching algorithms must be faster. In this research paper we are presenting a new method of pattern matching, which could be a platform for enhancement in the future. In this field, researchers spared no efforts to introduce fast algorithms for pattern matching. The Most popular algorithms are Boyer-Moore, Aho–Corasick, Naïve String search, Rabin Karp String Search and Knuth–Morris–Pratt. Based on studying these techniques we are developing algorithms that process the text data, using different algorithm technique and then we’ll test the performance and compare the processing time with the fastest proven pattern matching algorithms available. Document the result and draw the overall conclusion.