Performance Analysis of the Single Pattern Matching Algorithm Based on Prefix Searching

Pattern matching algorithms have gained a lot of importance in computer science, primarily because they are used in various domains such as computational biology, video retrieval, intrusion detection systems, and fraud detection. Finding one or more patterns in a given text is known as pattern matching. Two important things that are used to judge how well exact pattern matching algorithms work are the total number of attempts and the character comparisons that are made during the matching process. The primary focus of our proposed method is reducing the size of both components wherever possible. Despite sprinting, hash-based pattern matching algorithms may have hash collisions. The Efficient Hashing Method (EHM) algorithm is improved in this research. Despite the EHM algorithm’s effectiveness, it takes a lot of time in the preprocessing phase, and some hash collisions are generated. A novel hashing method has been proposed, which has reduced the preprocessing time and hash collision of the EHM algorithm. We devised the Hashing Approach for Pattern Matching (HAPM) algorithm by taking the best parts of the EHM and Quick Search (QS) algorithms and adding a way to avoid hash collisions. The preprocessing step of this algorithm combines the bad character table from the QS algorithm, the hashing strategy from the EHM algorithm, and the collision-reducing mechanism. To analyze the performance of our HAPM algorithm, we have used three types of datasets: E. coli, DNA sequences, and protein sequences. We looked at six algorithms discussed in the literature and compared our proposed method. The Hash-q with Unique FNG (HqUF) algorithm was only compared with E. coli and DNA datasets because it creates unique bits for DNA sequences. Our proposed HAPM algorithm also overcomes the problems of the HqUF algorithm. The new method beats older ones regarding average runtime, number of attempts, and character comparisons for long and short text patterns, though it did worse on some short patterns.

The need for efficient intrusion detection systems increases every day to protect network traffic against emerging attacks. Unfortunately, increasing network speeds and number of signatures makes it harder for the existing signature-based intrusion detection systems to keep up. This makes those systems the weak link and the bottleneck which decreases the overall network performance. Researchers found that 30%–60% of the overall processing time of signature-based intrusion detection systems is spent on pattern matching operations [1]. In this paper, we present a novel and fast software-based pattern matching algorithm to reduce the number of times to perform pattern matching. This new algorithm introduces an exclusion-inclusion filter programmed only with signatures prefixes. It filters out the clean traffic without requiring pattern matching and weeds out suspicious packets to be searched using a specially modified Wu-Manber pattern matching algorithm. The exclusion-inclusion filter is a modified Bloom filter that produces a list of probable matching signatures for each suspect packet. The remaining few suspicious packets are searched only for the probable matches. Compared to the Wu-Manber algorithm used in intrusion detection systems, the experimental results indicate a speed up of 3.4 times on average, 5.5 times for regular traffic, and 1.6 times for worst case traffic. The memory overhead added by the algorithm was limited to 0.11%.

Intrusion detection is very important for network security. In this study, the structure and design of the Snort system were introduced briefly, and then the ability of data acquisition was improved by a third-party interface. In the part of intrusion detection, the pattern matching algorithm was improved to improve the detection effect of the system. The experimental results showed that the data packet capture ability of the improved system was significantly improved, and the packet loss rate was 97.41% lower than that of the ordinary system; in the intrusion detection, the detection efficiency was kept at 75 M/s, which was significantly higher than other algorithms; for 20 attack traffic, the improved system could realize all alarms, and the maximum response time was only 0.3 s. The experimental results show that the improved Snort system is effective in intrusion prevention and it is worth to be widely used in practice.

Pattern matching is essential to a wide range of applications such as network intrusion detection, virus scanning, etc. Pattern matching algorithms normally rely on state machines to detect predefined patterns. Recently, parallel pattern matching engines, based on ASICs, FPGAs or network processors, perform matching with multiple state machines. The state migration in the matching procedure incurs intensive memory accesses. Thus, it is critical to minimize the storage of state machines such that they can be fit in on-chip or other fast memory modules to achieve high-speed pattern matching. This paper proposes novel optimization techniques, namely state re-labeling and memory partition, to reduce state machine storage. The paper also presents architectural designs based on the optimization strategy. We evaluate our design using realistic pattern sets, and the results show state machine memory reduction up to 80.1%.

EPMA: Efficient pattern matching algorithm for DNA sequences

Journal Article A Digest and Pattern Matching-Based Intrusion Detection Engine Get access Zhongqiang Chen, Zhongqiang Chen * 1Yahoo! Inc., Santa Clara, CA 95054, USA *Corresponding author: zqchen@yahoo-inc.com Search for other works by this author on: Oxford Academic Google Scholar Yuan Zhang, Yuan Zhang 2Department of Mathematics, Florida State University, Tallahassee, FL 32306, USA Search for other works by this author on: Oxford Academic Google Scholar Zhongrong Chen, Zhongrong Chen 3ProMetrics Inc., King of Prussia, PA 19406, USA Search for other works by this author on: Oxford Academic Google Scholar Alex Delis Alex Delis 4Department of Informatics and Telecommunications, University of Athens, Athens, 15784, Greece Search for other works by this author on: Oxford Academic Google Scholar The Computer Journal, Volume 52, Issue 6, August 2009, Pages 699–723, https://doi.org/10.1093/comjnl/bxp026 Published: 15 April 2009 Article history Received: 05 October 2008 Revision received: 14 March 2009 Published: 15 April 2009

Machine learning (ML) is being used to improve intrusion detection mechanisms and identification in cyber security. Network data volume scaling (with the help of Machine learning) — Automated analysis and pattern recognition for large amounts of network-data, thereby detection of anomalies / potentially malicious activities that escape current rule-based techniques. By training ML models on historical data, these models can learn benign network behavior as well the anomalies in them that may result from malicious activities. The purpose of this essay/report is to begin taking a look under the hood at how ML can be used for security threat detection and analysis in-networking on an ongoing basis. This paper covers the automation of ML algorithms to enhance network security. Given the current state of electronic threats and their evolution, traditional security methods are typically insufficient. ML can analyze large volumes of data, learn patterns from it and this makes it suitable to complement network defense mechanisms. It then discusses different ML applications in network cybersecurity: intrusion detection, anomaly detection, spam and malware analysis; which the paper characterizes. It analyzes the potential, benefits and constrains of major ML methods in network security like supervised learning; unsupervised learning and reinforcement-learning. Finally, this paper represents recent progress in the use and impact of ML techniques along with case studies.The paper discusses the existing difficulties in the field such as the necessity for datasets and the vulnerability of machine learning models to adversarial attacks.T he paper also highlights avenues for exploration by focusing on developing scalable security solutions based on machine learning that are resilient and flexible.The goal of this examination is to offer both researchers and industry professionals valuable perspectives into the opportunities and obstacles linked to utilizing machine learning, in the domain of network security. ML methods have potential to improve network security by addressing the challenges posed by the increasing cyber threats that traditional security measures struggle to combat effectively over time. One key strength of ML lies in its capacity to analyze datasets and identify intricate patterns efficiently. The research paper delves into applications of ML in enhancing network security. The list covers security tools like Intrusion Detection Systems (IDS) examining malware and phishing attempts as well as anomalies in network activity and user behavior analysis (UEBA). The study explores both supervised and unsupervised learning methods. How they are used for quick threat detection and response in real time scenarios.You will find case studies and recent developments that showcase the implementation and effectiveness of these strategies.In addition the article delves into the obstacles linked to using machine learning techniques in network security including the necessity, for datasets, The paper's goal is to give an enlightening summary to scholars and practitioners about how machine learning can be applied to network security in order to provide solutions that are robust, adaptive, and scalable. To this end, it touches on several relevant aspects. One is the threat posed by adversarial attacks on the sorts of models that are likely to be used in this context. Another is the imperative, deriving from both adversarial threat and model drift, that models needed in this context be available in a form usable for continuous update. Keywords: Network Security, machine learning (ML), Intrusion Detection Systems (IDS), entity behavior analytics (UEBA).

Deep learning-based feature extraction and optimizing pattern matching for intrusion detection using finite state machine

Object-oriented application framework is one of the most important implementations of object-oriented software engineering. Normally, a user takes several months of learning in order to become highly productive in using a specific object-oriented application framework. Without proper documentation, frameworks are not very usable to framework users. Currently available framework documentation approaches are not very effective for new framework users, and this scenario tends to discourage new users in using frameworks. The main objective of this paper is to propose and implement an intelligent framework documentation approach that integrates case-based learning (CBL) with genetic algorithm (GA) and Knuth-Morris-Pratt (KMP) pattern matching algorithm with the intention of making learning a framework more effective. GA assists in optimizing the search process and performs machine learning. Within the GA, nearest neighbor algorithm is used in determining the most similar recorded case that can be used in solving the new case. A new case is retained in the case base for future retrievals. A framework user is allowed to select from a list of features provided by the framework that he or she is interested in learning, and the system will give an example of application related to the selected features. This paper concludes with a prototype that implements the intelligent framework documentation approach.

Pattern matching with wildcards is very important in many fields such as information retrieval and bioinformatics. Suffix trees are used in pattern matching with variable length wildcards. But the construction of a suffix tree needs significant time and space overload. This paper presents a new pattern matching algorithm, PST, based on multiple suffix trees. The PST algorithm uses a cutting process to divide a string S into several parts firstly, and then establishes a suffix tree for each part of S respectively. If multiple patterns are to be retrieved, the suffix trees should be adjusted according to the cutting points: prefix sequence deletion and suffix sequence addition; prefix sequence addition and suffix sequence deletion. Theoretical analysis and experiments show that the PST algorithm can decrease the time and space overload than other peers.

Discontinuous and Pattern Matching algorithm to measure deformation having discontinuities

A fast pattern matching algorithm with multi-byte search unit for high-speed network security

Generation of Pattern-Matching Algorithms by Extended Regular Expressions

Many network security applications such as Intrusion Detection System (IDS), Firewall and Data Loss Prevention System (DLPS) are based on deep packet inspection, in this packets header as well as payload of the packets are checked with predefined attack signature to identify whether it contains malicious traffic or not. To perform this checking different pattern matching methods are used by NIDS. The most popular method to implement pattern matching is to use of Finite Automata (FA). Generally, regular expressions are used to represent most of the attack signatures defined by NIDS. They are implemented using finite automata, which takes the payload of packet as input string. However, existing approaches of Finite Automata (FA), both deterministic finite automata (DFA) and non-deterministic finite automata (NFA) for pattern matching are having their own advantages and some drawbacks. The DFA based pattern matching methods are fast enough but require more memory. However, NFA based pattern matching methods are comparatively takes less memory but the speed of matching is very slow, to overcome these drawbacks of finite automata there are many approaches have been proposed. This paper discuses comparative study of some Finite Automata (FA) based techniques for pattern matching in network intrusion detection system (NIDS).

This project explored the use of quantum-assisted algorithms for pattern matching in sub-atomic physics experiments. Pattern matching algorithms are commonly employed to prune data of random noise and to help discriminate between signals generated by particle tracks of interest and signals generated by background events. The quantum-assisted algorithms explored in this project were based on an Ising formulation of quantum associative model (QAMM) recall and quantum content-addressable memory (QCAM) recall. The recall is performed by comparing a probe pattern with those stored in a library of patterns encoded in the QAMM/QCAM model. The classification accuracy of QAMM and QCAM recall was determined as a function of detector resolution, noise, and efficiency and pattern density, where pattern density is defined as the ratio of the number of reference signal patterns encoded in the library to each pattern’s length. We found that QAMM achieved high classification accuracy when applied to datasets with low pattern density. QCAM achieved high classification accuracy for datasets with high pattern density and was found to be more robust to detector noise. The project methodology and results are described in detail in our arXiv preprint (arXiv:2011.11848) . This project was conducted by scientists at the Johns Hopkins University Applied Physics Laboratory and Oak Ridge National Laboratory from August 2018 to August 2020 and was supported by DOE grant DE-SC0019497.