Biclustering is a well established way of two–dimensional data analysis. Its goals may be very easily extended into the analysis of three–dimensional input. Through the decades a lot of algorithms for searching for three-dimensional patterns have been developed. Recently, the Boolean reasoning–based efforts to search for biclusters have also been published. This paper presents a “proof–of– concept” for searching for one of possible triclusters with the application of Boolean reasoning.

The paper discusses a data science competition centered around the development of an anomaly detection system for IoT devices.The competition utilized a unique environment that allowed for the operation and monitoring of real IoT devices, including scheduling of attacks on these devices.The environment was used to collect the data, which included both normal and attack-induced behavior of IoT devices.The paper presents the background of the competition, the top models submitted, and the competition results.The paper also includes a discussion about restrictions related to the use of synthetic attack data as input for constructing anomaly detection systems.

As the IoT industry strongly extends there is a need for better security and threat detection tools. Many approaches are possible but the tendency is to detect attacks externally by using network traffic analysis. Network based intrusion detection could lead to satisfactory results however it is uncertain if host based methods would not give better results as IoT devices usually have repeatable and predictable behavior. Unfortunately host based detection methods can neither be directly compared against each other nor be compared to network based systems as there are no publicly available data sets with IoT device operating system traces. In this paper we propose and describe a framework which allows for emulation of IoT devices, simulation of random attacks and gathering of the operating system traces for Linux based IoT devices. We also publish the first gathered data set and we plan to release new extended data sets in near future.

Abstract Biclustering is a two-dimensional data analysis technique that, applied to a matrix, searches for a subset of rows and columns that intersect to produce a submatrix with given, expected features. Such an approach requires different methods to those of typical classification or regression tasks. In recent years it has become possible to express biclustering goals in the form of Boolean reasoning. This paper presents a new, heuristic approach to bicluster induction in binary data.

Assuring the network traffic safety is a very important issue in a variety of today’s industries. Therefore, the development of anomalies and attacks detection methods has been the goal of analyses. In the paper the binary classification-based approach to network traffic safety monitoring is presented. The well known methods were applied to artificially modified network traffic data and their detection capabilities were tested. More detailed interpretation of the nature of detected anomalies is carried out with the help of the XAI approach. For the purpose of experiments a new benchmark network traffic data set was prepared, which is now commonly available.

Biclustering is considered as the method of finding two–dimensional subgroups in a matrix of scalars. The paper introduces a new approach to biclustering continuous matrices on the basis of boolean function analysis. We draw the strong relation between inclusion–maximal (maximal with respect to inclusion) biclusters of the assumed maximal difference between the data in a bicluster and prime implicants of a boolean function describing the data. These biclusters are called similarity biclusters. In the opposition to them, a new notion of dissimilarity biclusters was also introduced in the paper.

Biclustering is a branch of data analysis, whereby the goal is to find two–dimensional subgroups in a matrix of scalars. We introduce a new approach for biclustering discrete and binary matrices on the basis of boolean function analysis. We draw the correspondence between non–extendable (maximal with respect to inclusion) exact biclusters and prime implicants of a discernibility function describing the data. We present also the results of boolean-style clustering of the artificial discrete image data. Some possibilities of utilizing basic image processing techniques for this kind of input to the biclustering problem are discussed as well.

Underground coal mining is a branch of an industry which safety of operation is very dependent on the natural hazards.A proper seismic event prediction is a significant aspect of building classification models from the real data, which can affect the coal mining safety increase.In this paper four models, built in a well known data mining environments, are presented.The obtained models, depending on a given implementation of popular methods, occurred comparable to the best results from the competition.

The paper presents the results of research related to the efficiency of the so-called rule quality measures which are used to evaluate the quality of rules at each stage of the rule induction. The stages of rule growing and pruning were considered along with the issue of conflict resolution which may occur during the classification. The work is the continuation of research on the efficiency of quality measures employed in sequential covering rule induction algorithm. In this paper we analyse only these quality measures (8 measures) which had been recognized as effective based on previous conducted research. The study was conducted on approximately 70 benchmark datasets related to classification, regression and survival analysis problems. In the comparisons we analyzed prognostic abilities of the induced rules as well as the complexity of the resulting rule-based data models.

The paper presents the results of research related to the efficiency of the so called rule quality measures which are used to evaluate the quality of rules at each stage of the rule induction.The stages of rule growing and pruning were considered along with the issue of conflicts resolution which may occur during the classification.The work is the continuation of research on the efficiency of quality measures employed in sequential covering rule induction algorithm.In this paper we analyse only these quality measures (9 measures) which had been recognised as effective based on previously conducted research.

This short article describes two kernel algorithms of the regression function estimation. One of them is called HASKE and has its own heuristic of the h parameter evaluation. The second is a hybrid algorithm that connects the SVM and HASKE in such a way that the definition of the local neighborhood is based on the definition of the h -neighborhood from HASKE . Both of them are used as predictors for time series.