Correlation Rules Research Articles

A Study of Pattern Prediction in the Monitoring Data of Earthen Ruins with the Internet of Things.

An understanding of the changes of the rammed earth temperature of earthen ruins is important for protection of such ruins. To predict the rammed earth temperature pattern using the air temperature pattern of the monitoring data of earthen ruins, a pattern prediction method based on interesting pattern mining and correlation, called PPER, is proposed in this paper. PPER first finds the interesting patterns in the air temperature sequence and the rammed earth temperature sequence. To reduce the processing time, two pruning rules and a new data structure based on an R-tree are also proposed. Correlation rules between the air temperature patterns and the rammed earth temperature patterns are then mined. The correlation rules are merged into predictive rules for the rammed earth temperature pattern. Experiments were conducted to show the accuracy of the presented method and the power of the pruning rules. Moreover, the Ming Dynasty Great Wall dataset was used to examine the algorithm, and six predictive rules from the air temperature to rammed earth temperature based on the interesting patterns were obtained, with the average hit rate reaching 89.8%. The PPER and predictive rules will be useful for rammed earth temperature prediction in protection of earthen ruins.

A high-level domain-specific language for SIEM (design, development and formal verification)

Organizations deploy security information and event management (SIEM) systems for centralized management of security events. The real-time security monitoring capability of the SIEM depends on the correlation process where events data are matched against the security rules. Most SIEM systems use general purpose languages to define security rules. Creating new rules in general purpose languages require excellent programming skills in the proprietary language and intimate knowledge of events. This paper introduces a high-level domain-specific language (HDSL) which simplifies rule creation for the SIEM system. We formally specify the HDSL with extended Backus–Naur form grammar in another tool for language recognition according to the model driven engineering approach. In our implementation framework, the rules defined in the HDSL are converted in the standard event processing language. For evaluation purpose, the converted security rules are tested on the service real-time data security analytics. The results indicate that the rules are converted accurately and generate alarms when specific attacks are detected. For checking correctness of the HDSL, formal verification is carried out using satisfiability modulo theory and Z3 solver. The results are evaluated under diverse attack scenarios, which reveal that HDSL is functioning correctly. The HDSL enhances the SIEM correlation capabilities by providing a tranquil approach for writing the correlation rules.

A sliding window-based multi-stage clustering and probabilistic forecasting approach for large multivariate time series data

ABSTRACTTime series data analysis, such as temporal pattern recognition and trend forecasting, plays an increasingly significant part in temporal data statistics and analytics. Yet challenges still exist in the efficiency of pattern extracting and trend prediction for large multivariate time series. The paper proposes a multi-stage clustering approach towards multivariate time series by using dynamic sliding time windows. The segmented multivariate time series are clustered separately in each time window to product first-stage clustering centres, and which are used to generate second-stage clustering results involving all time windows. The method can simplify large scale multivariate time series mining problems through multi-stage clustering on multiple sliding time windows thus achieve improved efficiency. Based on the clustering outcomes, a correlation rules mining method is given to discover frequent patterns in the time series and generate self-correlation rules. Then, the paper presents a probabilistic forecasting model that leverages the extracted rules to make short-term predictions. Finally, experiments are presented to show the efficiency and effectiveness of the proposed approach.

A Correlation Analysis Model of Fault Location of Distribution System Based on RS-IA Data Mining

Fault location is an effective way to ensure quickly power recovery after a failure in power distribution system. Bad operation environment of FTU, damaged elements or lost information usually cause variations in fault information for correlation analysis in fault location of power distribution system. A data mining (DM) correlation analysis model based on rough sets (RS) theory and immune algorithm (IA) is proposed in this paper. Firstly, using RS theory to extract domain knowledge, a set of the given variant fault pattern is converted into a decision table in RS. Secondly, an attribute reduction of the decision table is made by using the IA theory, and the intrinsic correlation rules between input vector (condition attribute) and output vector (decision attribute) are mined. Then, the data mining method is used to deal with the distortion of FTU real-time input information. According to the current limit information sequence of section switches, line fault states in each section are judged to realize the fault location in power distribution network. Finally, the feasibility and effectiveness of the fault location model of distribution network based on RS-IA data mining model is verified by simulation.

Human skin detection through correlation rules between the YCb and YCr subspaces based on dynamic color clustering

This paper presents a novel rule-based skin detection method that works in the YCbCr color space. The method is based on correlation rules that evaluate the combinations of chrominance values to identify the skin pixels in the YCb and YCr subspaces. The correlation rules depend on the shape and size of dynamically generated skin color clusters, which are computed on a statistical basis in the YCb and YCr subspaces for each single image, and represent the areas that include most of the candidate skin pixels. Comparisons with six well-known rule-based methods in literature carried out on four publicly available databases show that the proposed method outperforms the others in terms of quantitative performance evaluation parameters. Moreover, the qualitative analysis shows that the method achieves satisfactory results also in critical scenarios, including severe variations in illumination conditions.

A Novel Deep Web Data Mining Algorithm based on Multi-Agent Information System and Collaborative Correlation Rule

Along with the rapid advancement of Internet technology and machine learning science, the data mining techniques have been widely applied on the web page information pattern analysis issues. To enhance the traditional mining algorithms theoretically and numerically, we propose the novel deep web data mining algorithm based on multi-agent information system and collaborative correlation rule in this manuscript. Firstly, we review the latest web mining methodologies to serve as the comparison objects. Then, we introduce the revised agent based algorithm. MAS consists of more than one agent, MAS using parallel distributed processing technology and modular design thought and the complex system is divided into relatively independent agent subsystem. Later, we combine the AdaBoost method to propose the collaborative correlation rule. As the combination, we use the mentioned two techniques to form the optimized and enhanced deep web data mining algorithm with the implementation of programming languages. The experimental result proves the feasibility of our approach and compared with other contemporary state-of-the-art algorithms, our method outperforms and achieves better accuracy with low time-consuming.

RETRACTED ARTICLE: A new approach to eliminating EOG artifacts from the sleep EEG signals for the automatic sleep stage classification

Interference between EEG and EOG signals has been studied heavily in clinical EEG signal processing applications. But, in automatic sleep stage classification studies these effects are generally ignored. Thus, the objective of this study was to eliminate EOG artifacts from the EEG signals and to see the effects of this process. We proposed a new scheme in which EOG artifacts are separated from electrode or other line artifacts by a correlation and discrete wavelet transform-based rule. Also, to discriminate the situation of EEG contamination to EOG from EOG contamination to EEG, we introduced another rule and integrated this rule to our proposed method. The proposed method was also evaluated under two different circumstances: EOG–EEG elimination along the whole 0.3–35 Hz power spectrum and EOG–EEG elimination with discrete wavelet transform in 0–4 Hz frequency range. To see the consequences of EOG–EEG elimination in these circumstances, we classified pure EEG and artifact-eliminated EEG signals for each situation with artificial neural networks. The results on 11 subjects showed that pure EEG signals gave a mean classification accuracy of 60.12 %. The proposed EOG elimination process performed in 0–35 Hz frequency range resulted in a classification accuracy of 63.75 %. Furthermore, conducting EOG elimination process by using 0–4 Hz DWT detail coefficients caused this accuracy to be raised to 68.15 %. By comparing the results obtained from all applications, we concluded that an improvement about 8.03 % in classification accuracy with regard to the uncleaned EEG signals was achieved.

Presenting decision tree for best mixing rules and Z-factor correlations and introducing novel correlation for binary mixtures

The significance of gas compressibility factor in petroleum engineering encourages the researchers to employ the most accurate and precise methods for estimation of this factor. Commonly, empirical correlations due to their simplicity have been referred more than other approaches for prediction of Z-factor. There is no clear and reliable report to address an appropriate combination of correlation and mixing rule for each type of gas. In the present study, combination of several empirical correlations and mixing rules is examined and a decision tree is constructed to suggest best combination for each gas system. For this reason, 2329 experimental data were used for analysis. According to the results, Leland–Mueller mixing rule/Sanjari and Lay correlation is the best combination for sour and natural gas. Also, Van Ness–Abbot mixing rule/Hall–Yarborough correlation, Stewart–Burkhardt–Voo mixing rule/Heidarian correlation and Satter–Campbell mixing rule/Papay correlation are the most appropriate combination for gas condensate, binary and ternary mixtures respectively. For binary mixtures, a robust and novel empirical correlation was developed based on Kay mixing rule to estimate Z-factor. The results employed how good the new correlation is in agreement with the experimental data with significant R-squared 0.9843.

Application of Improved BP Neural Network with Correlation Rules in Network Intrusion Detection

To detect various network attacks in real time, this paper developed a network intrusion detection system based on artificial neural network. This paper first introduced the recent development of neural network, BP algorithm and structure of a simple perceptron. Then, this paper developed an improved BP neural network algorithm to detect anomaly network traffic with adjusted correlation rules. Finally, the network intrusion system in this paper was tested in a real network situation; the improved BP algorithm neural network with adjusted correlation rules shows a reduction in total error and increment in alarm rate compared to the traditional basic BP algorithm model.

A Study of 15 Singlet Rydberg Series of HeH and Their Correlation with the Rydberg Series of Li

We theoretically study 37 Rydberg states of HeH derivable from the He(1s 2) + H(nl) configuration with n ≤ 5 along with three states with n = 6. The multireference configuration interaction method is used to calculate their electronic energy curves (EECs) for the internuclear distance R from 0 to 28a 0, which are regarded as the united‐atom (UA) and separated‐atoms (SA) limits, respectively. The 15 Rydberg series (nsσ, npσ, ndσ, nfσ, ngσ, ndδ, nfδ, ngδ, ngγ, npπ, ndπ, nfπ, ngπ, nfϕ, and ngϕ) are identified. Bingel's perturbation theory is used to explain the role of the core part and the Rydberg orbitals in the behavior of EECs at small R. Smith's theory of diabatization is directly solved to obtain the diabatic quantum defect curves (QDCs). From the correlations in diabatic QDCs between the UA and SA limits, the following order of orbital energies in the equilibrium region is found: npσ < nsσ < npπ < ndσ < ndπ < nfσ < nfπ < nfδ < nfϕ < ndδ, except 5dδ < 5fϕ. This order is identical to that in one‐electron molecular systems at small R, except for the nsσ and npπ series. The correlation rules are n UA = n SA + 1 for the npσ series and n UA = n SA for other series; additionally, l UA = l SA for all cases except for the 2pσ − σ1s correlation. A reversal of the diabatic dipole moment at avoided crossing points of the nsσ and npσ series is observed, and explained by the behaviors of ns and np wavefunctions of H at small R.

Frame synchronisation based on compressed sensing with correlation rule

The research of frame synchronisation, which processing is based on the sampling at Nyquist rate in traditional method, has been become the hot topic over the past few decades. In the light of compressed sensing theory that sampling received signals below the Nyquist rate, a frame synchronisation method with correlation rule is proposed in this paper. Firstly, the synchronisation metrics of frame synchronisation is expressed as compressed mode by developing the sparsity of the synchronisation metric amplitudes over observation window. The measurement matrix for compressive sampling is then projected, and compressive sampling for the received signal is employed according to the projected measurement matrix. Finally, we reconstruct the synchronisation metrics from the compressive sampling by utilising compressive sampling matching pursuit CoSaMP algorithm and obtain the estimate of frame synchronisation with the reconstructed synchronisation metrics. Compared to the traditional methods, the analysis and simulation results show that the sampling rate in proposed method is significantly reduced while obtaining comparative correct synchronisation probability when received signal-to-noise ratio SNR is relatively high. Simultaneously, for the reason that the correct synchronisation probability is not particularly sensitive to the iteration number of the CoSaMP algorithm for frame synchronisation, low reconstruction complexity is also obtained in the proposed method.

Frame synchronisation based on compressed sensing with correlation rule

Frame synchronisation based on compressed sensing with correlation rule

Prefetching Scheme for Massive Spatiotemporal Data in a Smart City

Employing user access patterns to develop a prefetching scheme can effectively improve system I/O performance and reduce user access latency. For massive spatiotemporal data, traditional pattern mining methods fail to directly reflect the spatiotemporal correlation and transition rules of user access, resulting in poor prefetching performance. This paper proposed a prefetching scheme based on spatial-temporal attribute prediction, named STAP. It maps the history of user access requests to the spatiotemporal attribute domain by analyzing the characteristics of spatiotemporal data in a smart city. According to the spatial locality and time stationarity of user access, correlation analysis is performed and variation rules are identified for the history of user access requests. Further, the STAP scheme mines the user access patterns and constructs a predictive function to predict the user's next access request. Experimental results show that the prefetching scheme is simple yet effective; it achieves a prediction accuracy of 84.3% for access requests and reduces the average data access response time by 44.71% compared with the nonprefetching scheme.

Intrusion detection in federated clouds

In order to overcome the provisioning and scalability limits of a single cloud, cloud federation appears as the computing model in which multiple resources from independent cloud providers can be shared to create large-scale distributed virtual clusters. On the other hand, these complex architectures become an attractive target for distributed denial of service (DDoS) attacks. Although federated cloud environments have large amount of resources and profound dynamic allocation capability, which can be used to face DDoS attacks, they are however vulnerable to attacks that aim at compromising the service level agreements. In this paper, we investigate the key research topics for supporting distributed intrusion detection in a federated cloud environment. We propose a scalable intrusion detection solution, which can be used by cloud providers to protect the federated cloud infrastructure, as well as offered to the cloud service providers to monitor the hosted applications. We present a multi-layer architecture, which exploits a publish/subscribe middleware to collect and share security information in the federated cloud infrastructure. Moreover, we present an open-source framework, which provides features and interfaces to develop and deploy security components, as well as to define customised event correlation rules used to detect possible inter-cloud attacks.

Intrusion detection in federated clouds

In order to overcome the provisioning and scalability limits of a single cloud, cloud federation appears as the computing model in which multiple resources from independent cloud providers can be shared to create large-scale distributed virtual clusters. On the other hand, these complex architectures become an attractive target for distributed denial of service DDoS attacks. Although federated cloud environments have large amount of resources and profound dynamic allocation capability, which can be used to face DDoS attacks, they are however vulnerable to attacks that aim at compromising the service level agreements. In this paper, we investigate the key research topics for supporting distributed intrusion detection in a federated cloud environment. We propose a scalable intrusion detection solution, which can be used by cloud providers to protect the federated cloud infrastructure, as well as offered to the cloud service providers to monitor the hosted applications. We present a multi-layer architecture, which exploits a publish/subscribe middleware to collect and share security information in the federated cloud infrastructure. Moreover, we present an open-source framework, which provides features and interfaces to develop and deploy security components, as well as to define customised event correlation rules used to detect possible inter-cloud attacks.

Neighborhood scheme selection for classification with SCRD method

When using Spatial Correlation Rule with Distance (SCRD) the selection of the neighborhood scheme influences classification accuracy. Spatial dependency in different situations remains at various distances, so, according to this, in applications it is important to choose a suitable neighborhood scheme. In the earlier papers of the authors, the nearest neighbor scheme was used. In this paper, several different neighborhood schemes are examined by large experiment.

Bound excitons and many-body effects in x-ray absorption spectra of azobenzene-functionalized self-assembled monolayers

We study x-ray absorption spectra of azobenzene-functionalized self-assembled monolayers (SAMs), investigating excitations from the nitrogen K edge. Azobenzene with H-termination and functionalized with CF3 groups is considered. The Bethe-Salpeter equation is employed to compute the spectra, including excitonic effects, and to determine the character of the near-edge resonances. Our results indicate that core-edge excitations are intense and strongly bound: Their binding energies range from about 6 to 4 eV, going from isolated molecules to densely-packed SAMs. Electron-hole correlation rules these excitations, while the exchange interaction plays a negligible role.

A Case Study on Converged Security with Event Correlation of Physical and Information Security

Today’s security initiatives have encouraged incorporation of physical security and information security into converged security for greater effectiveness and capabilities. However, efforts for converging security have largely limited to the issues of the organizational structure with respect to streamlining processes and abstract frameworks for security management. To go beyond just a buzz word of converged security, it should be necessary to bring significant technical merits from this convergence. In this work, we consider “event correlations” that examine any associations between events coming from the above two distinctive worlds to provide greater capabilities for preventing unauthorized access to high-security computers, as a tangible step towards convergence of security. For this purpose, we introduce our approach using event categorization that maps physical events to a finite number of classes (five) instead of considering event types individually for feasibility, and also show how to define correlation rules with the categories. In addition, we present our prototype system that implements the incorporation of two typical physical security entities: a door/gate access control and a video surveillance system. Our exploration presented in this paper would be beneficial for guiding future development of a diverse range of converged security functions.

3D Human behavior recognition based on binocular vision and face–hand feature

To improve traditional video surveillance systems’ performance on human behavior recognition, a new method based on binocular vision was researched. This paper proposed a human behavior recognition method based on binocular stereo vision and human face–hand feature. Firstly, the paper got extrinsic and intrinsic parameters of the two cameras through binocular stereo vision calibration, then the paper located face and hands region through human face–hand feature segmentation method, and next then for getting the 3D information of face and hands by binocular stereo vision theory, the paper proposed a feature block matching algorithm based on local gray area correlation and epipolar constraint rule to match corresponding feature regional centers. And finally, the paper completed the analysis of the human upper body poses, 3D spatial moving and the measurement of moving velocity based on 3D information of face and hands. Experimental results showed that the method can successfully recognize human behaviors, and it can solve the occlusion and multi-direction movement problem of traditional video surveillance system. Our behavior recognition method is straightforward and robust. The uniqueness is different from traditional machine learning and classification framework about human behavior recognition.

Molecular structure of one-electron diatomic molecules subject to plasma screening and its effect on the dynamics

SynopsisThe effect of plasma screening on the electronic and vibrational structure of one-electron diatomic molecules is analyzed by including screened Coulomb interactions among all their particles in the solution of the Schrödinger equation. The breakdown of the well-known separability of the Schrödinger equation in confocal elliptical coordinates, the lost of long-range Coulomb interaction and the non-degeneracy in both the united atom and separated atom limits bring new elements into the analysis of the modified binding energies (electronic and vibrational), adiabatic correlation rules, molecular Stark mixings due to polarization effects, unexpected presence of molecular shape resonances and changes in non-adiabatic and radiative couplings upon the variation of the screening strength.