Measuring the Propagation of Information in Partial Evaluation

We present the first measurement-based analysis of the information propagated by a partial evaluator. Our analysis is based on measuring implementations of string-matching algorithms, based on the observation that the sequence of character comparisons accurately reflects maintained information. Notably, we can easily prove matchers to be different and we show that they display more variety and finesse than previously believed. As a consequence, we are able to pinpoint differences and inaccuracies in many results previously considered equivalent. Our analysis includes a framework that lets us obtain string matchers - notably the family of Boyer-Moore algorithms - in a systematic formalism-independent way from a few information-propagation primitives. By leveraging the existing research in string matching, we show that the landscape of information propagation is non-trivial in the sense that small changes in information propagation may dramatically change the properties of the resulting string matchers. We thus expect that this work will prove useful as a test and feedback mechanism for information propagation in the development of advanced program transformations, such as GPC or Supercompilation

Factor oracle : a new structure for pattern matching

International audienceWe introduce a new automaton on a word p, sequence of letters taken in an alphabet Σ, that we call factor oracle. This automaton is acyclic, recognizes at least the factors of p, has m+1 states and a linear number of transitions. We give an on-line construction to build it. We use this new structure in string matching algorithms that we conjecture optimal according to the experimental results. These algorithms are as effecient as the ones that already exist using less memory and being more easy to implement

Approximate String Matching With Dynamic Programming and Suffix Trees

The importance and the contribution of string matching algorithms to the modern society cannot be overstated. From basic search algorithms such as spell checking and data querying, to advanced algorithms such as DNA sequencing, trend analysis and signal processing, string matching algorithms form the foundation of many aspects in computing that have been pivotal in technological advancement. In general, string matching algorithms can be divided into the categories of exact string matching and approximate string matching. We study each area and examine some of the well known algorithms. We probe into one of the most intriguing data structure in string algorithms, the suffix tree. The lowest common ancestor extension of the suffix tree is the key to many advanced string matching algorithms. With these tools, we are able to solve string problems that were, until recently, thought intractable by many. Another interesting and relatively new data structure in string algorithms is the suffix array, which has significant breakthroughs in its linear time construction in recent years. Primarily, this thesis focuses on approximate string matching using dynamic programming and hybrid dynamic programming with suffix tree. We study both approaches in detail and see how the merger of exact string matching and approximate string matching algorithms can yield synergistic results in our experiments

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

Analysis of string-searching algorithms on biological sequence databases

String-searching algorithms are used to find the occurrences of a search string in a given text. The advent of digital computers has stimulated the development of string-searching algorithms for various applications. Here, we report the performance of all string-searching algorithms on widely used biological sequence databases containing the building blocks of nucleotides (in the case of nucleic acid sequence database) and amino acids (in the case of protein sequence database). The biological sequence databases used in the present study are Protein Information Resource (PIR), SWISSPROT, and amino acid and nucleotide sequences of all genomes available in the genome database. The average time taken for different search-string lengths considered for study has been taken as an indicator of performance for comparison between various methods

High performance stride-based network payload inspection

There are two main drivers for network payload inspection: malicious data, attacks, virus detection in Network Intrusion Detection System (NIDS) and content detection in Data Leakage Prevention System (DLPS) or Copyright Infringement Detection System (CIDS). Network attacks are getting more and more prevalent. Traditional network firewalls can only check the packet header, but fail to detect attacks hidden in the packet payload. Therefore, the NIDS with Deep Packet Inspection (DPI) function has been developed and widely deployed. By checking each byte of a packet against the pattern set, which is called pattern matching, NIDS is able to detect the attack codes hidden in the payload. The pattern set is usually organized as a Deterministic Finite Automata (DFA). The processing time of DFA is proportional to the length of the input string, but the memory cost of a DFA is quite large. Meanwhile, the link bandwidth and the traffic of the Internet are rapidly increasing, the size of the attack signature database is also growing larger and larger due to the diversification of the attacks. Consequently, there is a strong demand for high performance and low storage cost NIDS. Traditional softwarebased and hardware-based pattern matching algorithms are have difficulty satisfying the processing speed requirement, thus high performance network payload inspection methods are needed to enable deep packet inspection at line rate. In this thesis, Stride Finite Automata (StriFA), a novel finite automata family to accelerate both string matching and regular expression matching, is presented. Compared with the conventional finite automata, which scan the entire traffic stream to locate malicious information, the StriFA only needs to scan samples of the traffic stream to find the suspicious information, thus increasing the matching speed and reducing memory requirements. Technologies such as instant messaging software (Skype, MSN) or BitTorrent file sharing methods, allow convenient sharing of information between managers, employees, customers, and partners. This, however, leads to two kinds of major security risks when exchanging data between different people: firstly, leakage of sensitive data from a company and, secondly, distribution of copyright infringing products in Peer to Peer (P2P) networks. Traditional DFA-based DPI solutions cannot be used for inspection of file distribution in P2P networks due to the potential out-of-order manner of the data delivery. To address this problem, a hybrid finite automaton called Skip-Stride-Neighbor Finite Automaton (S2NFA) is proposed to solve this problem. It combines benefits of the following three structures: 1) Skip-FA, which is used to solve the out-of-order data scanning problem; 2) Stride-DFA, which is introduced to reduce the memory usage of Skip-FA; 3) Neighbor-DFA which is based on the characteristics of Stride-DFA to get a low false positive rate at the additional cost of a small increase in memory consumption