A Survey of String Matching Algorithms

ABSTRACT The concept of string matching algorithms are playing an important role of string algorithms in finding a place where one or several strings (patterns) are found in a large body of text (e.g., data streaming, a sentence, a paragraph, a book, etc.). Its application covers a wide range, including intrusion detection Systems (IDS) in computer networks, applications in bioinformatics, detecting plagiarism, information security, pattern recognition, document matching and text mining. In this paper we present a short survey for well-known and recent updated and hybrid string matching algorithms. These algorithms can be divided into two major categories, known as exact string matching and approximate string matching. The string matching classification criteria was selected to highlight important features of matching strategies, in order to identify challenges and vulnerabilities

Bloom Filters Optimized Wu-Manber for Intrusion Detection

With increasing number and severity of attacks, monitoring ingress and egress network traffic is becoming essential everyday task. Intrusion detection systems are the main tools for capturing and searching network traffic for potential harm. Signature -based intrusion detection systems are the most widely used, and they simply use a pattern matching algorithms to locate attack signatures in intercepted network traffic. Pattern matching algorithms are very expensive in terms of running time and memory usage, leaving intrusion detection systems unable to detect attacks in real-time. We propose a Bloom filters optimized Wu-Manber pattern matching algorithm to speed up intrusion detection. The Bloom filter programs the hash table into a vector, which is quickly queried to exclude unnecessary searches. On average hash table searches are avoided 10.6% of the time. The proposed algorithm achieves a best -case speedup of 66% and worst -case speedup of 33% over Wu-Manber at the cost of 0.33% memory usage increase

Bloom Filters Optimized Wu-Manber for Intrusion Detection

With increasing number and severity of attacks, monitoring ingress and egress network traffic is becoming essential everyday task. Intrusion detection systems are the main tools for capturing and searching network traffic for potential harm. Signature-based intrusion detection systems are the most widely used, and they simply use a pattern matching algorithms to locate attack signatures in intercepted network traffic. Pattern matching algorithms are very expensive in terms of running time and memory usage, leaving intrusion detection systems unable to detect attacks in real-time. We propose a Bloom filters optimized Wu-Manber pattern matching algorithm to speed up intrusion detection. The Bloom filter programs the hash table into a vector, which is quickly queried to exclude unnecessary searches. On average hash table searches are avoided 10.6% of the time. The proposed algorithm achieves a best-case speedup of 66% and worst-case speedup of 33% over Wu-Manber at the cost of 0.33% memory usage increase