Pattern Matching Algorithm Research Articles

Reducing false rate packet recognition using Dual Counting Bloom Filter

Distributed Denial of Service (DDoS) attacks are a serious threat to Internet security. A lot of research effort focuses on having detection and prevention methods on the victim server side or source side. The Bloom filter is a space-efficient data structure used to support pattern matching problems. The filter is utilised in network applications for deep packet inspection of headers and contents and also looks for predefined strings to detect irregularities. In intrusion detection systems, the accuracy of pattern matching algorithms is crucial for dependable detection of matching pairs, and its complexity usually poses a critical performance bottleneck. In this paper, we will propose a novel Dual Counting Bloom Filter (DCBF) data structure to decrease false detection of matching packets applicable for the $$\textit{SACK}^2$$ algorithm. A theoretical evaluation will determine the false rate probability of detection and requirements for increased memory. The proposed approach significantly reduces the false rate compared to previously published results. The results indicate that the increased complexity of the DCBF does not affect efficient implementation of hardware for embedded systems that are resource constrained. The experimental evaluation was performed using extensive simulations based on real Internet traces of a wide area network link, and it was subsequently proved that DCBF significantly reduces the false rate.

A Person Authentication System Based on RFID Tags and a Cascade of Face Recognition Algorithms

Face recognition represents an appealing solution for biometrics-based unobtrusive and flexible person authentication. However, most solutions proposed in the literature suffer from some significant drawbacks, such as high computational complexity, the need for a centralized biometrics database (which is not desirable due to widespread international provisions discouraging collections of sensitive personal data), and limited scalability on a large number of enrolled subjects. We propose a novel person authentication solution based on a cascade of face recognition and pattern matching algorithms that not only provides high reliability and robustness against impostors but also stores in a personal radio frequency identification (RFID) tag all the needed individual biometrics information of the user, who therefore always remains in control, and has the exclusive availability, of such sensitive data. This paper describes the proposed approach, called RFaceID, and discusses its performance in terms of the ratio between false acceptance rate and false rejection rate and in terms of authentication time when applied to the VidTIMIT, Extended Yale B, and Mobile Biometry (MOBIO) widely adopted face databases.

Complexity of Matching Sets of Two-Dimensional Patterns by Two-Dimensional On-Line Tessellation Automaton

We study the two-dimensional pattern matching implemented using the deterministic two-dimensional on-line tessellation automaton. This restricted two-dimensional cellular automaton is able to simulate the Baker–Bird algorithm, which was proposed as the first algorithm for the two-dimensional pattern matching. We explore capabilities of this automaton to carry out the matching task against an arbitrary set of equal-sized patterns. To measure amount of resources needed to accomplish it, we introduce the pattern complexity of a picture language. We show that this complexity ranges from a constant to exponential one. All of these are illustrated by giving examples of two-dimensional on-line tessellation automata matching sets of patterns, describing general techniques of how to construct them and proving lower bounds on the pattern complexity of some picture languages as well as operations over them.

A Method to Improve High-Resolution Sea Ice Drift Retrievals in the Presence of Deformation Zones

Retrievals of sea ice drift from synthetic aperture radar (SAR) images at high spatial resolution are valuable for understanding kinematic behavior and deformation processes of the ice at different spatial scales. Ice deformation causes temporal changes in patterns observed in sequences of SAR images; which makes it difficult to retrieve ice displacement with algorithms based on correlation and feature identification techniques. Here, we propose two extensions to a pattern matching algorithm, with the objective to improve the reliability of the retrieved sea ice drift field at spatial resolutions of a few hundred meters. Firstly, we extended a reliability assessment proposed in an earlier study, which is based on analyzing texture and correlation parameters of SAR image pairs, with the aim to reject unreliable pattern matches. The second step is specifically adapted to the presence of deformation features to avoid the erasing of discontinuities in the drift field. We suggest an adapted detection scheme that identifies linear deformation features (LDFs) in the drift vector field, and detects and replaces outliers after considering the presence of such LDFs in their neighborhood. We validate the improvement of our pattern matching algorithm by comparing the automatically retrieved drift to manually derived reference data for three SAR scenes acquired over different sea ice covered regions.

TrackCC: A Practical Wireless Indoor Localization System Based on Less-Expensive Chips.

This paper aims at proposing a new wireless indoor localization system (ILS), called TrackCC, based on a commercial type of low-power system-on-chip (SoC), nRF24LE1. This type of chip has only l output power levels and acute fluctuation for a received minimum power level in operation, which give rise to many practical challenges for designing localization algorithms. In order to address these challenges, we exploit the Markov theory to construct a -sized state transition matrix to remove the fluctuation, and then propose a priority-based pattern matching algorithm to search for the most similar match in the signal map to estimate the real position of unknown nodes. The experimental results show that, compared to two existing wireless ILSs, LANDMARC and SAIL, which have meter level positioning accuracy, the proposed TrackCC can achieve the decimeter level accuracy on average in both line-of-sight (LOS) and non-line-of-sight (NLOS) senarios.

Development of thin skin mimicking bilayer solid tissue phantoms for optical spectroscopic studies.

In vivo spectroscopic measurements have the proven potential to provide important insight about the changes in tissue during the development of malignancies and thus help to diagnose tissue pathologies. Extraction of intrinsic data in the presence of varying amounts of scatterers and absorbers offers great challenges in the development of such techniques to the clinical level. Fabrication of optical phantoms, tailored to the biochemical as well as morphological features of the target tissue, can help to generate a spectral database for a given optical spectral measurement system. Such databases, along with appropriate pattern matching algorithms, could be integrated with in vivo measurements for any desired quantitative analysis of the target tissue. This paper addresses the fabrication of such soft, photo stable, thin bilayer phantoms, mimicking skin tissue in layer dimensions and optical properties. The performance evaluation of the fabricated set of phantoms is carried out using a portable fluorescence spectral measurement system. The alterations in flavin adenine dinucleotide (FAD)-a tissue fluorophore that provides important information about dysplastic progressions in tissues associated with cancer development based on changes in emission spectra-fluorescence with varied concentrations of absorbers and scatterers present in the phantom are analyzed and the results are presented. Alterations in the emission intensity, shift in emission wavelength and broadening of the emission spectrum were found to be potential markers in the assessment of biochemical changes that occur during the progression of dysplasia.

A Flexible Pattern-Matching Algorithm for Network Intrusion Detection Systems Using Multi-Core Processors

As part of network security processes, network intrusion detection systems (NIDSs) determine whether incoming packets contain malicious patterns. Pattern matching, the key NIDS component, consumes large amounts of execution time. One of several trends involving general-purpose processors (GPPs) is their use in software-based NIDSs. In this paper, we describe our proposal for an efficient and flexible pattern-matching algorithm for inspecting packet payloads using a head-body finite automaton (HBFA). The proposed algorithm takes advantage of multi-core GPP parallelism and single-instruction multiple-data operations to achieve higher throughput compared to that resulting from traditional deterministic finite automata (DFA) using the Aho-Corasick algorithm. Whereas the head-body matching (HBM) algorithm is based on pre-defined DFA depth value, our HBFA algorithm is based on head size. Experimental results using Snort and ClamAV pattern sets indicate that the proposed algorithm achieves up to 58% higher throughput compared to its HBM counterpart.

An algorithmic framework for frequent intraday pattern recognition and exploitation in forex market

We present a knowledge discovery-based framework that is capable of discovering, analyzing and exploiting new intraday price patterns in forex markets, beyond the well-known chart formations of technical analysis. We present a novel pattern recognition algorithm for Pattern Matching, that we successfully used to construct more than 16,000 new intraday price patterns. After processing and analysis, we extracted 3518 chart formations that are capable of predicting the short-term direction of prices. In our experiments, we used forex time series from 8 paired-currencies in various time frames. The system computes the probabilities of events such as "within next 5 periods, price will increase more than 20 pips". Results show that the system is capable of finding patterns whose output signals (tested on unseen data) have predictive accuracy which varies between 60 and 85% depending on the type of pattern. We test the usefulness of the discovered patterns, via implementation of an expert system using a straightforward strategy based on the direction and the accuracy of the pattern predictions. We compare our method against three standard trading techniques plus a "random trader," and we also test against the results presented in two recently published studies. Our framework performs very well against all systems we directly compare , and also, against all other published results.

Estimation of myocardial deformation using correlation image velocimetry

BackgroundTagged Magnetic Resonance (tMR) imaging is a powerful technique for determining cardiovascular abnormalities. One of the reasons for tMR not being used in routine clinical practice is the lack of easy-to-use tools for image analysis and strain mapping. In this paper, we introduce a novel interdisciplinary method based on correlation image velocimetry (CIV) to estimate cardiac deformation and strain maps from tMR images.MethodsCIV, a cross-correlation based pattern matching algorithm, analyses a pair of images to obtain the displacement field at sub-pixel accuracy with any desired spatial resolution. This first time application of CIV to tMR image analysis is implemented using an existing open source Matlab-based software called UVMAT. The method, which requires two main input parameters namely correlation box size (CB) and search box size (SB), is first validated using a synthetic grid image with grid sizes representative of typical tMR images. Phantom and patient images obtained from a Medical Imaging grand challenge dataset (http://stacom.cardiacatlas.org/motion-tracking-challenge/) were then analysed to obtain cardiac displacement fields and strain maps. The results were then compared with estimates from Harmonic Phase analysis (HARP) technique.ResultsFor a known displacement field imposed on both the synthetic grid image and the phantom image, CIV is accurate for 3-pixel and larger displacements on a 512 × 512 image with (CB,SB)=(25,55) pixels. Further validation of our method is achieved by showing that our estimated landmark positions on patient images fall within the inter-observer variability in the ground truth. The effectiveness of our approach to analyse patient images is then established by calculating dense displacement fields throughout a cardiac cycle, and were found to be physiologically consistent. Circumferential strains were estimated at the apical, mid and basal slices of the heart, and were shown to compare favorably with those of HARP over the entire cardiac cycle, except in a few (∼4) of the segments in the 17-segment AHA model. The radial strains, however, are underestimated by our method in most segments when compared with HARP.ConclusionsIn summary, we have demonstrated the capability of CIV to accurately and efficiently quantify cardiac deformation from tMR images. Furthermore, physiologically consistent displacement fields and circumferential strain curves in most regions of the heart indicate that our approach, upon automating some pre-processing steps and testing in clinical trials, can potentially be implemented in a clinical setting.

EPMA: Efficient pattern matching algorithm for DNA sequences

To solve, manage and analyze biological problems using computer technology is called bioinformatics. With the emergent evolution in computing era, the volume of biological data has increased significantly. These large amounts of data have increased the need to analyze it in reasonable space and time. DNA sequences contain basic information of species, and pattern matching between different species is an important and challenging issue to cope with. There exist generalized string matching and some specialized DNA pattern matching algorithms in the literature. There is still need to develop fast and space efficient pattern matching algorithms that consider new hardware development. In this paper, we present a novel DNA sequences pattern matching algorithm called EPMA. The proposed algorithm utilizes fixed length 2-bits binary encoding, segmentation and multi-threading. The idea is to find the pattern with multiple searcher agents concurrently. The proposed algorithm is validated with comparative experimental results. The results show that the new algorithm is a good candidate for DNA sequence pattern matching applications. The algorithm effectively utilizes modern hardware and will help researchers in the sequence alignment, short read error correction, phylogenetic inference etc. Furthermore, the proposed method can be extended to generalized string matching and their applications.

Spatial pyramid deep hashing for large-scale image retrieval

Effective feature representations and similarity measurements are crucial for large-scale image retrieval, and conventional methods often learn hash functions from a predefined hand-crafted feature space. Meanwhile, the spatial structure in raw images always lost in most previous methods. Encouraged by the recent advances in convolutional neural networks (CNNs), a novel Spatial Pyramid Deep Hashing (SPDH) algorithm is developed for the task of fast image retrieval. In our SPDH algorithm, the CNN with a spatial pyramid pooling and a locally-connected layer with binary activation functions is utilized to build the end-to-end relation between the raw image data and the binary hashing codes for fast indexing. Different from the fully-connected layer, the locally-connected layer can consider each local spatial bin as an independent unit and only connect the local bin to preserve the spatial pyramid structure for hash codes. The learning of both the hash function and the feature representations are jointly optimized via backward propagation with classification or similarity loss function on the large-scale labeled dataset such as ImageNet. Moreover, a spatial pyramid binary pattern matching algorithm is developed to achieve partial local similar matching among the images. Our experimental results have shown that our SPDH method can outperform several state-of-the-art hashing algorithms on the CIFAR-10, SIVAL and the Oxford buildings datasets.

Hybrid mechanism towards network packet early acceptance and rejection for unified threat management

Recent network architectures utilise many types of security appliances to combat blended attacks. However, managing multiple separate security appliances can be overwhelming, inefficient and expensive. Thus, multiple security features are needed to be integrated into unified security architecture resulting in an unified threat management system (UTM). In most current UTM systems, whenever a security feature is needed, the corresponding module is just ‘attached or added on’. This approach of adding on may reduce the UTM performance dramatically, especially when security features such as IDS/IPS are enabled. In this study, a hybrid mechanism is proposed to solve UTM redundant packet classification problem. The mechanism is based on the use of splay tree filters and pattern-matching algorithms to enhance packet filtering and deep packet inspection (DPI) performance. The proposed mechanism uses network traffic statistics to dynamically optimise the order of the splay tree filters, allowing early acceptance and rejection of network packets. In addition, DPI signature rules are reordered according to their matching frequencies, allowing early packets acceptance. The authors demonstrate the merit of their mechanism through simulations performed on firewall and snort as independent packet manipulation systems compared with the proposed hybrid mechanism that uses unified communication between them.

Length-Bounded Hybrid CPU/GPU Pattern Matching Algorithm for Deep Packet Inspection

Since frequent communication between applications takes place in high speed networks, deep packet inspection (DPI) plays an important role in the network application awareness. The signature-based network intrusion detection system (NIDS) contains a DPI technique that examines the incoming packet payloads by employing a pattern matching algorithm that dominates the overall inspection performance. Existing studies focused on implementing efficient pattern matching algorithms by parallel programming on software platforms because of the advantages of lower cost and higher scalability. Either the central processing unit (CPU) or the graphic processing unit (GPU) were involved. Our studies focused on designing a pattern matching algorithm based on the cooperation between both CPU and GPU. In this paper, we present an enhanced design for our previous work, a length-bounded hybrid CPU/GPU pattern matching algorithm (LHPMA). In the preliminary experiment, the performance and comparison with the previous work are displayed, and the experimental results show that the LHPMA can achieve not only effective CPU/GPU cooperation but also higher throughput than the previous method.

Multimodal Speech Capture System for Speech Rehabilitation and Learning.

Speech-language pathologists (SLPs) are trained to correct articulation of people diagnosed with motor speech disorders by analyzing articulators' motion and assessing speech outcome while patients speak. To assist SLPs in this task, we are presenting the multimodal speech capture system (MSCS) that records and displays kinematics of key speech articulators, the tongue and lips, along with voice, using unobtrusive methods. Collected speech modalities, tongue motion, lips gestures, and voice are visualized not only in real-time to provide patients with instant feedback but also offline to allow SLPs to perform post-analysis of articulators' motion, particularly the tongue, with its prominent but hardly visible role in articulation. We describe the MSCS hardware and software components, and demonstrate its basic visualization capabilities by a healthy individual repeating the words "Hello World." A proof-of-concept prototype has been successfully developed for this purpose, and will be used in future clinical studies to evaluate its potential impact on accelerating speech rehabilitation by enabling patients to speak naturally. Pattern matching algorithms to be applied to the collected data can provide patients with quantitative and objective feedback on their speech performance, unlike current methods that are mostly subjective, and may vary from one SLP to another.

Finding Biomass Degrading Enzymes Through an Activity-Correlated Quantitative Proteomics Platform (ACPP).

The microbial secretome, known as a pool of biomass (i.e., plant-based materials) degrading enzymes, can be utilized to discover industrial enzyme candidates for biofuel production. Proteomics approaches have been applied to discover novel enzyme candidates through comparing protein expression profiles with enzyme activity of the whole secretome under different growth conditions. However, the activity measurement of each enzyme candidate is needed for confident "active" enzyme assignments, which remains to be elucidated. To address this challenge, we have developed an Activity-Correlated Quantitative Proteomics Platform (ACPP) that systematically correlates protein-level enzymatic activity patterns and protein elution profiles using a label-free quantitative proteomics approach. The ACPP optimized a high performance anion exchange separation for efficiently fractionating complex protein samples while preserving enzymatic activities. The detected enzymatic activity patterns in sequential fractions using microplate-based assays were cross-correlated with protein elution profiles using a customized pattern-matching algorithm with a correlation R-score. The ACPP has been successfully applied to the identification of two types of "active" biomass-degrading enzymes (i.e., starch hydrolysis enzymes and cellulose hydrolysis enzymes) from Aspergillus niger secretome in a multiplexed fashion. By determining protein elution profiles of 156 proteins in A. niger secretome, we confidently identified the 1,4-α-glucosidase as the major "active" starch hydrolysis enzyme (R = 0.96) and the endoglucanase as the major "active" cellulose hydrolysis enzyme (R = 0.97). The results demonstrated that the ACPP facilitated the discovery of bioactive enzymes from complex protein samples in a high-throughput, multiplexing, and untargeted fashion. Graphical Abstract ᅟ.

Localization Performance Analysis for Passive UHF RFID Technology Employing Directional Antennas and Omnidirectional Antennas

By the advantages of small size, low cost and easy deployment, passive Ultra High Frequency Radio Frequency Identification (UHF RFID) technology has become one of the most popular indoor localization technologies. As the most common measurement information, Received Signal Strength Indicator (RSSI) is severely affected by the complex indoor environments, like multipath propagation, Non-Line-of-Sight propagation, noise, etc. It eventually reduces location estimation accuracy. To improve the accuracy of RSSI-based algorithms, pattern matching algorithms are utilized with extra reference tags at fixed and known locations. One of the typical pattern matching algorithms is k-Nearest Neighbor (k-NN) algorithm. There is a problem for pattern matching algorithms utilized into real applications. That is the usually utilized RFID antenna is directional in industries while the algorithms are tested with omnidirectional antennas in experimental tests during research. In this paper, we take k-NN as an example to further analyze the performance difference between these two kinds of antennas. Experimental results show that the localization performance of k-NN algorithms with directional antennas utilized is worse than the results with omnidirectional antennas. But the performance is no significant difference.

Pattern matching algorithms for encrypted non-static texts

We consider the problem of pattern matching on encrypted texts. In this problem, users transfer the encrypted text and patterns to a third party, and the third party searches for all the occurrences of the patterns in the encrypted text; the matching results are encrypted such that only some specified users can read. The third party cannot access all the plain texts including the pattern, the text, and the matching result. In our problem, the texts and patterns are not static, and the pre-processing on the text is the encryption, not including building the index and others; any pre-processing on the patterns is allowed. We present pattern matching algorithms for ciphertexts of the fully homomorphic encryption. The time for decrypting the matching results is shorter than that of decrypting-then-matching of the ciphertext. We implement the pattern matching algorithms using HElib library. Several experiments are conducted to test the performance of the algorithm.

Multithreaded Sliding Window Approach to Improve Exact Pattern Matching Algorithms

In this paper an efficient pattern matching ap-proach, based on a multithreading sliding window technique, is proposed to improve the efficiency of the common sequential exact pattern matching algorithms including: (i) Brute Force, (ii) Knuth-Morris-Pratt and (iii) Boyer-Moore. The idea is to divide the text under-search into blocks, each block is allocated one or two threads running concurrently. Reported experimental results indicated that the proposed approach improves the performance of the well-known pattern matching algorithms, in terms of search time, especially when the searched patterns are located at the middle or at the end of the text.

Similarity Analysis-Based Indoor Localization Algorithm With Backscatter Information of Passive UHF RFID Tags

Passive radio frequency identification (RFID) is a low cost and low complexity localization technology. With introduction of reference tags, RFID technology can provide on-line reference information for pattern matching localization algorithms. One of the most popular algorithms is $k$ -nearest neighbor algorithm. While how to decide the $K$ value in estimation is still a problem. In addition, traditional received signal strength indicator (RSSI)-based algorithms suffer from multipath effects, it is difficult to further improve the localization accuracy. In this paper, we analyze the similarity of backscatter signals and present a novel range based indoor localization method, SAIL. According to the propagation distances of signals, reference tags that have similar distances to a reader antenna can be considered as a group. Inspired by the idea of grouping, clustering algorithm is utilized to get candidate tags for ranging. This method does not need to fix the number of reference tags in estimation. Besides RSSI, the RFID reader can also extract phase of signals coming from backscattered signals of responding tags. This guides us to combine RSSI and phase in similarity measure for clustering. In the simulation and experimental tests, SAIL is superior to other RFID schemes considering cost, location estimation error, and flexibility.

Pattern matching algorithms for encrypted non-static texts

Pattern matching algorithms for encrypted non-static texts