An improved algorithm for fuzzy data mining for intrusion detection

Association rule mining is a helpful tool to discover relations between items in transactions. But in some scenarios, it is also interesting to consider not only the presence of items, but the absence of them. In this paper, we introduce a methodology to obtain fuzzy association rules involving absent items. Additionally, our proposal is based on restriction level sets, a recent representation of fuzziness that extends that of fuzzy sets, and introduces some new operators, covering some misleading results obtained from usual fuzzy operators as, for example, negation. In our methodology, we define new measures for fuzzy association rules as RL-numbers, as well as we propose a new way of summarizing the resulting set of fuzzy association rules, distributed in restriction levels.

In real-world applications, transactions are typically represented by quantitative data. Thus, fuzzy association rule mining algorithms have been proposed to handle these quantitative transactions. In addition, items generally have certain lifespans or temporal periods in which they exist in a database. Therefore, fuzzy temporal association rule mining algorithms have also been proposed in the literature. A key factor in the acquisition of fuzzy temporal association rules (FTARs) is the design of appropriate membership functions. Because current approaches have been designed to generate membership functions for mining fuzzy association rules (FARs) in market-basket analysis, in this paper, we propose a membership function tuning mechanism for a fuzzy temporal association rule mining algorithm. The proposed approach modifies an existing cluster-based method to generate unique membership functions that are specifically tailored to each item in a dataset. Two factors are utilized to decide the appropriate membership functions of each item: (1) the density similarity among intervals corresponding to the density similarity within intervals, and (2) the information closeness within an interval corresponding to the similarity in the number of data points between intervals. A parameter θ is used to indicate the relative importance of these two factors. As a result, the membership functions are generated based on the quantitative ranges of individual items, and the generated membership functions of items are different in terms of the values of each interval and the number of intervals. The generated membership functions are subsequently used in a fuzzy temporal association rule mining algorithm. Computational experiments were conducted on both a synthetic dataset and a real-world one to demonstrate the effectiveness of the proposed approach.

Detection of abnormal traffic and network intrusions based on multiple fuzzy rules

Applying classical association rule extraction framework on fuzzy data sets leads to an unmanageably highly sized association rule sets -compounded with an information loss due to the discretization operation -that often constitutes a hamper towards an efficient exploitation of the mined knowledge. To overcome such drawback, we advocate the extraction and the exploitation of compact and informative generic basis of fuzzy association rules. This generic basis constitutes a compact nucleus of fuzzy association rules. In addition, we introduce an axiomatic system to ensure the derivation mechanism of all the remaining rules. Obtained preliminary results are very encouraging and they highlight a very important reduction of the number of the extracted fuzzy association rules without information loss.

Association rules and fuzzy association rules are vastly studied topics. Various measures for quantifying a quality of a (fuzzy) association rule were proposed in the past. In this article, we survey existing and propose some new quality measures for the whole rule bases of fuzzy association rules.

Intrusion detection system (IDS) is used to find malicious connections and protect networks from external or internal attacks. Various fuzzy or fuzzy intelligence approaches have been proposed in the development of IDS. In particular, the fuzzy interpolation technique guarantees the performance of IDS where only a sparse rule base is available. Furthermore, backward fuzzy interpolation allows interpolation to be carried out when certain antecedents of observation variables are absent, whereas conventional methods do not work. In this paper, a novel fuzzy association rules based classification intrusion detection system framework enhanced by a hierarchical bidirectional fuzzy rule interpolation technique is proposed for designing an IDS. Hierarchical bidirectional fuzzy rule interpolation is also employed to refine fuzzy rule base while which exists some consistency. This framework uses fuzzy association rules for building classifiers, and allows the generation of security alerts from situations which are not directly covered, missing values, or existing inconsistency by knowledge base. The proposed method is herein applied through integration with the Snort software to demonstrate the efficacy of this proposed approach.

The central point in designing a fuzzy system for modeling and control is the generation of a set of efficient and effective fuzzy rules. Generally, fuzzy rules can be generated either from heuristics or from experimental data. Heuristic fuzzy rule generation, as discussed in Chapter 1, consists of the following main steps: Determination of the structure of the fuzzy systems. This includes mainly the selection of the inputs and outputs. Usually, a multi-input multi-output fuzzy systems can be decomposed into a number of multi-input single-output fuzzy systems. Definition of linguistic terms and their fuzzy membership function for each fuzzy variables. If a Mamdani fuzzy system is designed, each input and output can be seen as a linguistic variable. Therefore, it is necessary to define the linguistic terms (e.g., Small, Medium, Large etc.) and their fuzzy membership function. Details about the definition of a linguistic variable is presented in Section 1.2.1. Determination of fuzzy inference method. For example, for fuzzy mapping rules, the fuzzy Cartesian product can be used to define the fuzzy relation. Refer to Section 1.2.2. Defuzzification. If the Mamdani type fuzzy rules are used, the fuzzy output derived from the fuzzy rule base should be defuzzified. Refer also to Section 1.2.2 for different defuzzification methods. If the Takagi-Sugeno fuzzy rules are used, the defuzzification is included in the fuzzy inference and a crisp output is obtained directly.

Following an investigation undertaken by the publisher, we have determined that this paper was accepted on the basis of a compromised peer review process. We hereby retract the paper. The corresponding author has been notified of the retraction. The retraction statement can be found here: https://doi.org/10.1520/JTE20269996. Aiming at the problems of poor effect, low precision, and a long time frame in the current data mining analysis algorithm of the network information resource description framework (RDF), a data mining analysis algorithm of the network information RDF based on fuzzy association rules is proposed. Using association rule mining technology combined with fuzzy set theory, a fuzzy association rule algorithm is obtained to deal with quantitative data. Fuzzy c-means (FCM) is used to discretize continuous attributes, and the fuzzy concept pattern and its support and credibility are defined. Add a Mining Query flag to extend the SPARQL Protocol and RDF Query Language (SPARQL) syntax, define the user specified mining model, generate the corresponding project set and transaction, adopt the fuzzy association rule algorithm to generate fuzzy association rules, and realize the RDF data mining analysis of network information. The experimental results show that the network RDF data mining analysis effect of the proposed algorithm is good, which can effectively improve the network RDF data mining analysis accuracy and shorten the mining analysis time.

Fuzzy rules automatically derived from a set of training examples quite often produce better classification results than fuzzy rules translated from medical knowledge. This study aims to investigate the difference in domain representation between a knowledge-based and a data-driven fuzzy system applied to an electrocardiography classification problem. For a three-class electrocardiographic arrhythmia classification task a set of fifteen fuzzy rules is derived from medical expertise on the basis of twelve electrocardiographic measures. A second set of fuzzy rules is automatically constructed on thirty-nine MIT-BIH database's records. The performances of the two classifiers on thirteen different records are comparable and up to a certain extent complementary. The two fuzzy models are then analyzed, by using the concept of information gain to estimate the impact of each ECG measure on each fuzzy decision process. Both systems rely on the beat prematurity degree and the QRS complex width and neglect the P wave existence and the ST segment features. The PR interval is not well characterized across the fuzzy medical rules while it plays an important role in the data-driven fuzzy system. The T wave area shows a higher information gain in the knowledge based decision process, and is not very much exploited by the data-driven system. The main difference between a human designed and a data driven ECG arrhythmia classifier is found about the PR interval and the T wave.

As opposed to quantitative association rule mining, fuzzy association rule mining is said to prevent the overestimation of boundary cases, as can be shown by small examples. Rule mining, however, becomes interesting in large databases, where the problem of boundary cases is less apparent and can be further suppressed by using sensible partitioning methods. A data-driven approach is used to investigate if there is a significant difference between quantitative and fuzzy association rules in large databases. The influence of the choice of a particular triangular norm in this respect is also examined.

In business association rules being considered as important assets, play a vital role in its productivity and growth. Different business partnership share association rules in order to explore the capabilities to make effective decision for enhance-ment of business and core capabilities. The fuzzy association rule mining approach emerged out of the necessity to mine quantitative data regularly present in database. An association rule is sensitive when it violates few rules and regulation for sharing particular nature of information to third world. Like classical association rules, there is a need for some privacy measures to be taken for retaining the standards and importance of fuzzy association rules. Privacy preservation is used for valu-able information extraction and minimizing the risk of sensitive information disclosure. Our proposed model mainly focuses to secure the sensitive information revealing association rules. In our model, sensitive fuzzy association rules are secured by identifying sensitive fuzzy item to perturb fuzzified dataset. The resulting transformed FARs are analyzed to conclude/calculate the accuracy level of our model in context of newly generated fuzzy association rules, hidden rules and lost rules. Extensive experiments are carried out in order to demonstrate the results of our proposed model. Privacy preservation of maximum number of sensitive FARs by keeping minimum perturbation highlights the significance of our model.

Fuzzy ellipsoid theory was introduced to mining fuzzy association rule from quantitative data. The algorithm includes two steps. First, fuzzy clustering procedure is taken to get fuzzy ellipsoids of the feature space. Second, mining procedure is taken to get fuzzy association rule based on the projection regions of fuzzy ellipsoids. The algorithm was compared with a Apriori-based fuzzy association rule mining algorithm. Results show that the algorithm is considerably quicker, especially when mining from large databases. Experiments on real-life data also show that the algorithm is effective to find association rules.

We examine the effect of genetic rule selection as a postprocessing procedure in fuzzy data mining. Usually a large number of fuzzy rules are extracted in a heuristic manner from numerical data using a rule evaluation criterion in fuzzy data mining. It is, however, very difficult for human users to understand thousands of fuzzy rules. Thus it is necessary to decrease the number of extracted fuzzy rules when our task is to present understandable knowledge to human users. In this paper, we use genetic rule selection to decrease the number of extracted fuzzy rules. Through computational experiments, we examine the effect of genetic rule selection. First we extract fuzzy rules that satisfy minimum support and confidence levels. Thousands of fuzzy rules are extracted from numerical data in a heuristic manner. Then we apply genetic rule selection to extracted fuzzy rules. Experimental results show that genetic rule selection significantly decreases the number of extracted fuzzy rules without degrading their classification accuracy.

Fuzzy Association Rule Mining (FARM) extends traditional ARM by evaluating and pruning rules based on interestingness measures to identify relevant patterns for various applications. The focus of this paper is to explore the application of FARM techniques demonstrating its algorithmic implementation in a meteorological dataset. Three major algorithms known as fuzzy Apriori, FTDA (Fuzzy Transaction Data-Mining Algorithm) and CFARM Composite Fuzzy Association Rule Mining) are experimented and analyzed. The experiment uses a real meteorological dataset spanning twenty years consisting some important attributes of weather such as rainfall, temperature, relative humidity, wind speed and bright sunshine hours of the North Bank Plain Zone (NBPZ) of the Brahmaputra River in Assam, India. The collected dataset is pre-processed into a transaction dataset and converted into a fuzzy dataset using membership functions. The three FARM algorithms are subsequently employed to uncover associations among various attributes within the fuzzy meteorological dataset. This study analyzes experimental results from three algorithms, focusing on factors like rule generation, computation time, and memory consumption. While Fuzzy Apriori provides comprehensive rule generation, it comes at the cost of higher computation time and memory usage. FTDA and CFARM, on the other hand, offer more efficient and significant rule generation, making them more suitable for large-scale, complex data analysis. The findings of this paper can contribute to the development of resilient and efficient data mining frameworks, enhancing the decision-making process for stakeholders in the meteorological domain. Thus, the paper introduces a new method for analyzing meteorological data using Fuzzy Association Rule Mining (FARM) techniques.

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