Rule Generation Algorithm Research Articles

Investigation of associative rule search method for detection of cyber incidents in information management systems and security events using CICIDS2018 test data set

Automated rule generation for cyber incident identification in information management and security event systems (SIEM, SYSTEM, etc.) plays a crucial role in modern cyberspace defense, where data volumes are exponentially increasing, and the complexity and speed of cyber-attacks are constantly rising. This article explores approaches and methods for automating the process of cyber incident identification rule generation to reduce the need for manual work and ensure flexibility in adapting to changes in threat models. The research highlights the need for utilizing modern techniques of Intelligent Data Analysis (IDA) to process large volumes of data and formulate behavior rules for systems and activities in information systems. The conclusion emphasizes the necessity of integrating multiple research directions, including analyzing existing methods and applying IDA algorithms to search for associative rules from large datasets. Key challenges addressed include the complexity of data modeling, the need to adapt to changes in data from dynamic cyber attack landscapes, and the speed of rule generation algorithms for their identification. The issue of the "dimensionality curse" and the identification of cybersecurity event sequences over time, particularly relevant to SIEM, are discussed. The research objective is defined as the analysis and evaluation of various mathematical methods for automated associative rule generation to identify cyber incidents in SIEM. The most effective strategies for enhancing the efficiency of associative rule generation and their adaptation to the dynamic change of the cybersecurity system state are identified to strengthen the protection of information infrastructure.

Identifying miRNA as biomarker for breast cancer subtyping using association rule

— This paper presents a comprehensive study focused on breast cancer subtyping, utilizing a multifaceted approach that integrates feature selection, machine learning classifiers, and miRNA regulatory networks. The feature selection process begins with the CFS algorithm, followed by the Apriori algorithm for association rule generation, resulting in the identification of significant features tailored to Luminal A, Luminal B, HER-2 enriched, and Basal-like subtypes. The subsequent application of Random Forest (RF) and Support Vector Machine (SVM) classifiers yielded promising results, with the SVM model achieving an overall accuracy of 76.60 % and the RF model demonstrating robust performance at 80.85 %. Detailed accuracy metrics revealed strengths and areas for refinement, emphasizing the potential for optimizing subtype-specific recall. To explore the regulatory landscape in depth, an analysis of selected miRNAs was conducted using MIENTURNET, a tool for visualizing miRNA-target interactions. While FDR analysis raised concerns for HER-2 and Basal-like subtypes, Luminal A and Luminal B subtypes showcased significant miRNA-gene interactions. Functional enrichment analysis for Luminal A highlighted the role of Ovarian steroidogenesis, implicating specific miRNAs such as hsa-let-7c-5p and hsa-miR-125b-5p as potential diagnostic biomarkers and regulators of Luminal A breast cancer. Luminal B analysis uncovered associations with the MAPK signaling pathway, with miRNAs like hsa-miR-203a-3p and hsa-miR-19a-3p exhibiting potential diagnostic and therapeutic significance. In conclusion, this integrative approach combines machine learning techniques with miRNA analysis to provide a holistic understanding of breast cancer subtypes. The identified miRNAs and associated pathways offer insights into potential diagnostic biomarkers and therapeutic targets, contributing to the ongoing efforts to improve breast cancer diagnostics and personalized treatment strategies.

Proposal and Definition of an Intelligent Clinical Decision Support System Applied to the Prediction of Dyspnea after 12 Months of an Acute Episode of COVID-19.

Long COVID is a condition that affects a significant proportion of patients who have had COVID-19. It is characterised by the persistence of associated symptoms after the acute phase of the illness has subsided. Although several studies have investigated the risk factors associated with long COVID, identifying which patients will experience long-term symptoms remains a complex task. Among the various symptoms, dyspnea is one of the most prominent due to its close association with the respiratory nature of COVID-19 and its disabling consequences. This work proposes a new intelligent clinical decision support system to predict dyspnea 12 months after a severe episode of COVID-19 based on the SeguiCovid database from the Álvaro Cunqueiro Hospital in Vigo (Galicia, Spain). The database is initially processed using a CART-type decision tree to identify the variables with the highest predictive power. Based on these variables, a cascade of expert systems has been defined with Mamdani-type fuzzy-inference engines. The rules for each system were generated using the Wang-Mendel automatic rule generation algorithm. At the output of the cascade, a risk indicator is obtained, which allows for the categorisation of patients into two groups: those with dyspnea and those without dyspnea at 12 months. This simplifies follow-up and the performance of studies aimed at those patients at risk. The system has produced satisfactory results in initial tests, supported by an AUC of 0.75, demonstrating the potential and usefulness of this tool in clinical practice.

Concise rule induction algorithm based on one-sided maximum decision tree approach

As the importance of machine learning tools for decision support continues to grow, interpretability has emerged as a key factor. Rule-based classification algorithms, such as decision trees and rule induction, enable high local interpretability by providing transparent reasoning rules in an IF-THEN format. In this context, it is essential to provide concise and clear rules and conditions to achieve high local interpretability. This study proposes a novel Concise Algorithm, designed to effectively remove irrelevant conditions from classification rules. We present a framework incorporating the Concise Algorithm, which employs the One-Sided-Maximum decision tree algorithm for rule generation, followed by the application of the Concise Algorithm to remove irrelevant conditions. This proposed framework produces a rule-based classification model that exhibits an enhanced predictive performance-interpretability trade-off compared to benchmark methods (CART, Ripper, CN2, and modified One-Sided-Maximum), as demonstrated by empirical tests conducted on 19 UCI datasets. A case study focusing on the breast-cancer-wisconsin dataset provides a comprehensive analysis of the rule and condition generation processes.

Multi-Converter UPQC Optimization for Power Quality Improvement Using Beetle Swarm-Based Butterfly Optimization Algorithm

The primary goal of this paper is to design a Multi-Converter Unified Power Quality Conditioner (MC-UPQC). The proposed method is solved based on the Synchronous Reference Frame (SRF) theory. The MC-UPQC involves two series Voltage Source Converters (VSCs) for power transfer between feeders to eliminate voltage sag, swell, interruption, and transient response in the system. The proposed model adopts control strategies based on an optimized Fuzzy Logic Controller (FLC) in SRF, utilizing a hybrid metaheuristic algorithm called Beetle Swarm-based Butterfly Optimization Algorithm (BS-BOA), which combines Beetle Swarm Optimization (BSO) and Butterfly Optimization Algorithm (BOA) for membership limit optimization and control rule generation. The major benefit of the optimized FLC-based MC-UPQC is its quick behavior in minimizing Total Harmonic Distortion (THD) in source and load side voltages and currents. Simulation and MATLAB environment are used for the entire system implementation. The comparative analysis of the proposed controller for MC-UPQC is performed against conventional controllers to validate its effectiveness. Quantitative data related to the main research outcomes, such as THD of source voltage and current, and dynamic behavior of the system, are included. The main benefit of the study is the significant reduction in THD and improved dynamic performance of the system.

Dynamic Weights Based Risk Rule Generation Algorithm for Incremental Data of Customs Declarations

Aimed at shortcomings, such as fewer risk rules for assisting decision-making in customs entry inspection scenarios and relying on expert experience generation, a dynamic weight assignment method based on the attributes of customs declaration data and an improved dynamic-weight Can-Tree incremental mining algorithm are proposed. In this paper, we first discretize the customs declaration data, and then form composite attributes by combining and expanding the attributes, which is conducive to generating rules with risk judgment significance. Then, weights are determined according to the characteristics and freshness of the customs declaration data, and the weighting method is applied to the Can-Tree algorithm for incremental association rule mining to automatically and efficiently generate risk rules. By comparing FP-Growth and traditional Can-Tree algorithms experimentally, the improved dynamic-weight Can-Tree incremental mining algorithm occupies less memory space and is more time efficient. The introduction of dynamic weights can visually distinguish the importance level of customs declaration data and mine more representative rules. The dynamic weights combine confidence and elevation to further improve the accuracy and positive correlation of the generated rules.

An assessment of machine learning algorithms for healthcare analysis based on improved MapReduce

Peoples who're specially affected with heart sickness and it's far one of the man-kill illnesses in the international level. Most of researchers to awareness at the prediction, clustering, rule generation, decision tree and machine learning algorithm for figuring out and predicting the danger of the sufferers primarily based on the medical information. The overall performances of the crucial functions are based on the machine-learning concept. By studying the algorithm, the researcher can pick out the time and reminiscence wanted for the execution. As such, there are many different types of machine learning algorithms are categorized into three important classifications namely unsupervised learning, supervised learning and reinforcement learning. Unsupervised learning consists of all varieties of clustering algorithms at the same time as supervised learning algorithm consists of all of the category strategies. But the author is considered the two algorithms are Supervised and Unsupervised learning algorithm to examine the overall performance. This research paper includes the six elements to evaluate the overall performance of K-Means, Navie-Bayes and enhanced PSNB-IMR Algorithm with various parameters.

An efficient classification rule generation for coronary artery disease diagnosis using a novel discrete equilibrium optimizer algorithm

Many machine learning-based methods have been widely applied to Coronary Artery Disease (CAD) and are achieving high accuracy. However, they are black-box methods that are unable to explain the reasons behind the diagnosis. The trade-off between accuracy and interpretability of diagnosis models is important, especially for human disease. This work aims to propose an approach for generating rule-based models for CAD diagnosis. The classification rule generation is modeled as combinatorial optimization problem and it can be solved by means of metaheuristic algorithms. Swarm intelligence algorithms like Equilibrium Optimizer Algorithm (EOA) have demonstrated great performance in solving different optimization problems. Our present study comes up with a Novel Discrete Equilibrium Optimizer Algorithm (NDEOA) for the classification rule generation from training CAD dataset. The proposed NDEOA is a discrete version of EOA, which use a discrete encoding of a particle for representing a classification rule; new discrete operators are also defined for the particle’s position update equation to adapt real operators to discrete space. To evaluate the proposed approach, the real world Z-Alizadeh Sani dataset has been employed. The proposed approach generate a diagnosis model composed of 17 rules, among them, five rules for the class “Normal” and 12 rules for the class “CAD”. In comparison to nine black-box and eight white-box state-of-the-art approaches, the results show that the generated diagnosis model by the proposed approach is more accurate and more interpretable than all white-box models and are competitive to the black-box models. It achieved an overall accuracy, sensitivity and specificity of 93.54%, 80% and 100% respectively; which show that, the proposed approach can be successfully utilized to generate efficient rule-based CAD diagnosis models.

Set Representation for Rule Generation Algorithms

The task of mining the association rule has become one of the most widely used discovery pattern methods in Knowledge Discovery in Databases (KDD). One such task is to represent the itemset in the memory. The representation of the itemset largely depend on the type of data structure that is used for storing them. Computing the process of mining the association rule im- pacts the memory and time requirement of the itemset. With the increase in the dimensionality of data and datasets, mining such large volume of datasets will be difficult since all these itemsets cannot be placed in the main memory. As representation of an itemset greatly affects the efficiency of the rule mining association, a compact and compress representation of an itemset is needed. In this paper, a set representation is introduced which is more memory and cost efficient. Bitmap representation takes one byte for an element but the set representation uses one bit. The set representation is being incorporated in Apriori Algorithm. Set representation is also being tested for different rule generation algorithms. The complexities of these different rule generation algorithms using set representation are being compared in terms of memory and time execution.

Proposição de modelo para a predição de classes de sustentabilidade por meio de apoio à decisão multicritério e inteligência artificial

Abstract: The current study proposes a novel prediction model of sustainability classes for electricity distribution companies in Brazil, based on sustainability indicators, aiming at a more effective risk management for a certain company among their competitors. Because such indicators are based on quantitative and qualitative measures and are very likely to incur imprecisions in their measures, the model to be proposed is based on a Multicriteria Decision Support, Rough Sets Theory, which allows the mathematical treatment of those imprecisions, and Artificial Intelligence, in this case, Machine Learning by rules inference. Consequently, decision tables are generated with condition attributes, sustainability indicators, and decision attributes, sustainability classes: high, medium or low. As a result, it is possible to predict sustainability classes based in temporal series of indicators and rules inference from decision tables, using RoughSets package in R and the jMAF software, demonstrating the use of five rule generation algorithms and their respective accuracies.

Using Conditional Random Fields to Optimize a Self-Adaptive Bell–LaPadula Model in Control Systems

Once defined, the access control policies and regulations would never be changed in a running and state transition process. However, it will give attackers the possibility of discovering vulnerabilities in the system, and the control systems lack the ability of dynamic perception of security state and risk, causing the systems to be exposed to risks. In this article, a dynamic Bell-LaPadula (BLP) model is proposed. The conditional random field (CRF) is introduced into the BLP model to optimize the rules. First, the model formalizes the security attributes, states of system, transition rules, and constraint models on the basis of the state transition of CRFs. After the historical system access logs are processed as the original dataset, a feature selection method is proposed to extract the requests and current states as feature vectors. Second, this article presents a rules training algorithm based on L-BFGS to implement the study and training of datasets, and then marks the logs in the test set through Viterbi algorithm automatically. On the base of these, a rule generation algorithm is proposed to dynamically adjust the access control rules based on the current security status and events of the system. Third, the security of CRFs-BLP is proved by theoretical analysis. Finally, the validity and accuracy of the model are verified by estimating the value of the precision, recall, and F1-score. As the system threats are shown to be decreased obviously from these experiments, this dynamic model can decrease the vulnerabilities and risk effectively.

Design of Associate Content Based Classifier (ACBC) for Malicious URL Prediction by Rule Generation Algorithm

Recently, the internet is becoming as the most effective tool to interact with many foreign societies especially during COVID-19 pandemic. Moreover, the digital platform is increasing in many developing countries and at the same time, the chance of fraudulence is also increasing day by day. In the digital world, phishing assaults are emerging as the most common type of social engineering attack. Currently, many websites are targeting to acquire the confidential data, which is stored in websites. Recently, the classification techniques are employed to detect the phishing websites. Many tools are used for anti-phishing purposes; they are blacklist and antivirus software. The confidential data in a fake surrounding has intended the category of leaked data due to the action of attackers. In this scenario, machine learning method is observed as a very effective to classify the phishing and non-phishing web (Uniform Resource Locator) URLs. This classification struggles in classifying the leaked data content-based challenge. Therefore, the proposed algorithm is associated with the content-based classification method along with the rule-based generator algorithm. This research article integrates the content-based classification with a rule-based generator algorithm to improve the overall performance of the system. The updated public online repository called Mendeley dataset is used in the proposed research work. The proposed algorithm is used in 7k phishing and real websites content data for performing feature extraction. The extracted feature is then analyzed with our proposed algorithm to provide better prediction accuracy. Also, the proposed work has concluded that, the associate algorithm has achieved better accuracy, when compared to other existing methods.

A novel energy estimation model for constraint based task offloading in mobile cloud computing

The utility of mobile applications has been increased enormously due to the advancements in science and technology to assist the users for various purposes. The main intention of integrating cloud services and resources with the mobile application is to reduce battery usage and to improve the efficiency of mobile devices. Thus the process of shifting a task that can be run on the cloud resources for assistance is referred to as task offloading. The process of task offloading is critical in the field of Mobile Cloud Computing. The major issue that pairs with task offloading are the communication cost estimation of the devices. To overcome the above-mentioned issues and to create an effective task offloading model, A Novel Mobile Cloud Computing framework called Rule Generation based Energy Estimation Model (RG-EEM) is designed. The energy required for executing the task in the local mobile device and cloud is estimated by implementing an energy estimation algorithm. Then a novel constraints specific rule generation algorithm is used to estimate the task execution time and memory utilization of the task, from which the decision has to be taken for offloading or local execution by considering all the possible affecting characteristics. Further, a novel task clustering and scheduling algorithm are implemented to execute the task in the cloud server effectively which will help in allocating similar tasks to a particular virtual machine in the cloud. The RG-EEM algorithm will also help in partitioning the task and parallel execution. The effectiveness of the RG-EEM technique is evaluated using the parametric measures and compared with the existing techniques.

A novel framework of fuzzy oblique decision tree construction for pattern classification

In this paper, some significant efforts on fuzzy oblique decision tree (FODT) have been done to improve classification accuracy and decrease tree size. Firstly, to eliminate data redundancy and improve classification efficiency, a forward greedy fast feature selection algorithm based on neighborhood rough set (NRS_FS_FAST) is introduced. Then, a new fuzzy rule generation algorithm (FRGA) is proposed to generate fuzzy rules. These fuzzy rules are used to construct leaf nodes for each class in each layer of the FODT. Different from the traditional axis-parallel decision trees and oblique decision trees, the FODT takes dynamic mining fuzzy rules as decision functions. Moreover, the parameter δ, which can control the size of the tree, is optimized by genetic algorithm. Finally, a series of comparative experiments are carried out with five traditional decision trees (C4.5, Best First Tree (BFT), amulti-class alternating decision tree (LAD), Simple Cart (SC), Naive Bayes Tree (NBT)), and recently proposed decision trees (FRDT, HHCART, and FMMDT-HB) on UCI machine learning datasets. The experimental results demonstrate that the FODT exhibits better performance on classification accuracy and tree size than the chosen benchmarks.

JSON-LD Based Web API Semantic Annotation Considering Distributed Knowledge

Based on semantically annotated Web APIs, automatic Web API composition can be implemented easily. The operation can greatly improve efficiency of building a software system. However, in real world, semantic annotation for Web APIs will encounter various difficulties, because of their distribution and function diversity, such as disunited API description formats, response result with complex structure, shortage of business domain ontologies, semantic conflicts among distributed knowledge, and so on. To solve these difficulties, we propose a JSON-LD based Web API semantic annotation approach (JWASA). JWASA can assist professional developers to semi-automatically complete semantic annotation of Web APIs. A common Web API description ontology is firstly defined, including necessary vocabularies about invocation information, functional semantics, and non-functional features. Then, JWASA automatically converts a Web API description into a document in an united JSON format, and assist developers to semi-automatically embed semantic information of crucial elements of the API by means of a lightweight Linked Data format JSON-LD. Meanwhile, a semantic annotation specification is proposed to deal with various complex situations in Web API description, e.g: too many response parameters, no request parameters, etc. In addition, to improve efficiency of annotation, JWASA provides some extra operations, including automatic new ontology or vocabulary creation, automatic functional semantics extraction etc. Also, JWASA provides semi-automatically bridge rule generation algorithm, which can infer implied relationships among vocabularies (e.g: sub-class, super-class, equality). JWASA focuses on the semantic annotation of functionality of Web APIs, and can create effective semantic Web APIs for future API automatic composition. We implement a prototype system and carry out a series of experiments to evaluate JWASA on real Web APIs crawled from Internet. Experiments show that our approach is effective and efficient.

An Effectual Ga Based Association Rule Generation and Fuzzy Svm Classification Algorithm for Predicting Students Performance

This investigation provides outcome of utilizing educational data mining [EDM] to design academic performance of students from real time and online dataset collected from colleges. Data mining is determined to examine non-academic and academic data; this model utilizes a classification approach termed as Fuzzy SVM classification with Genetic algorithm to attain effectual understanding of association rule in enrolment and to evaluate data quality for classification, which is identified as prediction task of performance and academic status based on low academic performance. This model attempts to predict student’s performance in grading system. Academic and student records attained from process were considered to train models estimated using cross-validation and formerly records from complete academic performance. Simulation was performed in MATLAB environment and show that academic status prediction is enhanced while hybrid dataset are added. The accuracy was compared with the existing models and shows better trade off than those methods.

On the use of hierarchical fuzzy inference systems (HFIS) in expert-based landslide susceptibility mapping: the central part of the Rif Mountains (Morocco)

The main purpose of this study is to investigate the use of hierarchical fuzzy inference systems (HFISs) in expert-based landslide susceptibility mapping in a data-scarce region. Taounate-Ain Aicha and Tahar Souk regions in the central part of the Rif Mountains in Morocco were selected as the case study area. The research was performed in three main stages: (i) the landslide inventory of the region was produced and the conditioning factors were evaluated; (ii) the theoretical background for HFIS was introduced; and (iii) different types of structures and methods of HFIS were investigated in the construction of expert-based models; the landslide information was only used for validation of the expert-based models in this stage. Regarding the inference methods, the defuzzification-free hierarchical fuzzy system (DF-HFS) has not only the remarkable advantage of low cost of computation but also preservation of information. The most successful result was acquired from the model developed using HFISs, which was designed by considering a defuzzification-free hybrid structure with standard membership functions. This research is the first study in which HFISs are evaluated in expert-based landslide susceptibility mapping. HFISs and the rule-generation algorithm implemented in this study will allow fuzzy systems to be applied effectively not only in landslide susceptibility mapping but also in other geoscientific and geo-engineering solutions.

Synchronization and identification of nonlinear systems by using a novel self-evolving interval type-2 fuzzy LSTM-neural network

Nonlinear systems widely exist in the real world. Researches on the synchronization and identification of nonlinear systems have both theoretical and practical interests. However, since the pseudo-random and parameter sensitivity, some nonlinear dynamics, such as chaotic systems, are difficult to achieve the required regression approximation. By means of the fuzzy-neural structure and long short-term memory (LSTM) mechanism, this paper proposes a novel inference structure of the self-evolving interval type-2 fuzzy LSTM-neural network (eIT2FNN-LSTM) for the synchronization and identification of nonlinear dynamics. In order to tackle with the time-dependency data generated by nonlinear systems, recurrent neural fuzzy systems with LSTM structure is introduced into type-2 fuzzy neural networks, where, by means of gate mechanism, a recurrent structure in the time dimension by feeding the rule firing strength of each rule back to itself is implemented. Besides, an online rule generation algorithm based on dynamic density clustering is utilized to achieve structural updates, which can meet the need of high frequency data process in reality. By incorporating direct adaptive interval type-2 LSTM fuzzy control scheme and sliding mode approach, two chaotic systems with external disturbance noise or/and random-varying parameters can be synchronized based on Lyapunov stability criterion, where two methods, i.e., the gradient training and particle swarm optimization (PSO) algorithm are used. In order to further verify the universality of the proposed scheme, besides nonlinear system, real-life datasets are also utilized to verify the effectiveness of the proposed fuzzy-neural inference system.

Qualitative analysis of big data in the service sectors

ABSTRACT Nowadays, service sectors are facing a data tsunami. Previous studies on service sectors have been conducted using questionnaire surveys and have been subjectively analyzed using statistical analysis techniques. Such techniques make it difficult for non-statisticians to integrally explore the overall nature of questionnaire data in the big data paradigm. To further discover the quantitative and qualitative nature of a data set, granularity computing is used to make up for the weaknesses of statistical techniques and a rough set (RS) based solution approach is proposed. The Multi-Value Rule Generation (MVRG) algorithm is developed to analyze questionnaire data and deal with the roughness problem of multiple-values in outcome features. The rules resulting from the MVRG algorithm exhibit both the relationships between dependent and independent variables and the content of the relationships. Rules, rather than numerical charts, can be understood by non-statisticians. Two cases of tourism and hospitality are restudied and comparisons between the proposed approach and traditional analytical techniques are made to validate the complementary benefits of traditional statistical analysis. This comparison shows that the proposed solution approach provides further hidden knowledge behind the data set. The MVRG algorithm can complement statistical methods in finding hidden knowledge and providing comprehensive rules to non-statisticians.

An effective weighted rule-based method for entity resolution

Entity resolution is an important task in data cleaning to detect records that belong to the same entity. It has a critical impact on digital libraries where different entities share the same name without any identifier key. Conventional methods adopt similarity measures and clustering techniques to reveal the records of a specific entity. Due to the lack of performance, recent methods build rules on records’ attributes with distinct values for entities to overcome some drawbacks. However, they use inadequate attributes and ignore common and empty attributes values which affect the quality of entity resolution. In this paper, we define a multi-attributes weighted rule system (MAWR) that investigates all values of records’ attributes in order to represent the difficult record-entity mapping. Then, we propose a rule generation algorithm based on this system. We also propose an entity resolution algorithm (MAWR-ER) depending on the generated rules to identify entities. We verify our method on real data, and the experimental results prove the effectiveness and efficiency of our proposed method.