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1

Detection of distributed denial of service attacks for IoT-based healthcare systems

Kaur Gaganjot, Gupta Prinima

Computer Assisted Methods in Engineering and Science

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2023

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Vol. 30, no. 2

167--186

EN

One of the major common assaults in the current Internet of things (IoT) network-based healthcare infrastructures is distributed denial of service (DDoS). The most challenging task in the current environment is to manage the creation of vast multimedia data from the IoT devices, which is difficult to be handled solely through the cloud. As the software defined networking (SDN) is still in its early stages, sampling-oriented measurement techniques used today in the IoT network produce low accuracy, increased memory usage, low attack detection, higher processing and network overheads. The aim of this research is to improve attack detection accuracy by using the DPTCM-KNN approach. The DPTCMKNN technique outperforms support vector machine (SVM), yet it still has to be improved. For healthcare systems, this work develops a unique approach for detecting DDoS assaults on SDN using DPTCM-KNN.

2

A comparison of advanced computational models and experimental techniques in predicting blast induced ground vibration in open pit coal mine

Nguyen Hoang, Bui Xuan Nam, Moayedi Hossein

Acta Geophysica

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2019

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Vol. 67, no. 4

1025--1037

EN

Green mining is an essential requirement for the development of the mining industry. Of the operations in mining technology, blasting is one of the operations that signifcantly affect the environment, especially ground vibration. In this paper, four artificial intelligence (AI) models including artificial neural network (ANN), k-nearest neighbor (KNN), support vector machine (SVM), and classification and regression tree (CART) were developed as the advanced computational models for estimating blast-induced ground vibration in a case study of Vietnam. Some empirical techniques were applied and developed to predict ground vibration and compared with the four AI models as well. For this research, 68 events of blasting were collected; 80% of the whole datasets were used to build the mentioned models, and the rest 20% were used for testing/checking the models’ performances. Mean absolute error (MAE), determination coefficient (R2 ), and root-mean-square error (RMSE) were used as the standards to evaluate the quality of the models in this study. The results indicated that the advanced computational models were much better than empirical techniques in estimating blast-induced ground vibration in the present study. The ANN model (2-6-8-6-1) was introduced as the most superior model for predicting ground vibration with an RMSE of 0.508, R2 of 0.981 and MAE of 0.405 on the testing dataset. The SVM, CART, and KNN models provided poorer performance with an RMSE of 1.192, 2.820, 1.878; R2 of 0.886, 0.618, 0.737; and MAE of 0.659, 1.631, 0.762, respectively.

3

Evaluation of the impact of explanatory variables on the accuracy of prediction of daily inflow to the sewage treatment plant by selected models nonlinear

Szeląg B., Bartkiewicz L., Studziński J., Barbusiński K.

Archives of Environmental Protection

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2017

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Vol. 43, no. 3

74--81

EN

The aim of the study was to evaluate the possibility of applying different methods of data mining to model the inflow of sewage into the municipal sewage treatment plant. Prediction models were elaborated using methods of support vector machines (SVM), random forests (RF), k-nearest neighbour (k-NN) and of Kernel regression (K). Data consisted of the time series of daily rainfalls, water level measurements in the clarified sewage recipient and the wastewater inflow into the Rzeszow city plant. Results indicate that the best models with one input delayed by 1 day were obtained using the k-NN method while the worst with the K method. For the models with two input variables and one explanatory one the smallest errors were obtained if model inputs were sewage inflow and rainfall data delayed by 1 day and the best fit is provided using RF method while the worst with the K method. In the case of models with three inputs and two explanatory variables, the best results were reported for the SVM and the worst for the K method. In the most of the modelling runs the smallest prediction errors are obtained using the SVM method and the biggest ones with the K method. In the case of the simplest model with one input delayed by 1 day the best results are provided using k-NN method and by the models with two inputs in two modelling runs the RF method appeared as the best.

PL

Celem pracy jest ocena możliwości zastosowania różnych metod data mining do modelowania dopływu ścieków do komunalnej oczyszczalni ścieków. Do opracowania modeli statystycznych metodą wektorów nośnych, lasów losowych, k – najbliższego sąsiada i regresji Kernela wykorzystano szeregi pomiarowe dobowych wartości opadów deszczu, stanów wody w odbiorniku oraz dopływów do komunalnej oczyszczalni ścieków w Rzeszowie. Z obliczeń wykonanych metodami SVM, RF, k-NN i K wynika, że dla modeli z jedną zmienną objaśniającą opóźnioną o dobę w stosunku do wartości dopływu, najlepsze wyniki otrzymano modelem autoregresyjnym bazującym na metodzie k-NN a najgorsze regresją Kernela. W przypadku modeli z dwoma zmiennymi objaśniającymi najmniejsze wartości błędów uzyskano, dla modeli uwzględniających dopływ ścieków i całkowitą wysokość opadu deszczu z jednodobowym opóźnieniem; najlepsze wyniki uzyskano metodą RF a najgorsze regresji Kernela. Dla modeli z dwiema zmiennymi objaśniającymi, ale trzema sygnałami wejściowymi, najmniejsze błędy dopływu ścieków do OŚ uzyskano metodą SVM, a najgorsze regresji Kernela. Z wykonanych symulacji stwierdzono, że w większości przypadków najmniejsze wartości błędów dopływu ścieków do oczyszczalni otrzymano metodą SVM a największe metodą K. W przypadku najprostszego modelu z jednym sygnałem wejściowym opóźnionym o 1 dobę najlepsze wyniki obliczeń uzyskano metodą k-NN, a w dwóch przypadkach modeli, gdzie ujęto 2 sygnały wejściowe, najlepsza okazała się metoda RF.

4

Improving prediction models applied in systems monitoring natural hazards and machinery

Sikora M., Sikora B.

International Journal of Applied Mathematics and Computer Science

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2012

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Vol. 22, no. 2

477-491

EN

A method of combining three analytic techniques including regression rule induction, the k-nearest neighbors method and time series forecasting by means of the ARIMA methodology is presented. A decrease in the forecasting error while solving problems that concern natural hazards and machinery monitoring in coal mines was the main objective of the combined application of these techniques. The M5 algorithm was applied as a basic method of developing prediction models. In spite of an intensive development of regression rule induction algorithms and fuzzy-neural systems, the M5 algorithm is still characterized by the generalization ability and unbeatable time of data model creation competitive with other systems. In the paper, two solutions designed to decrease the mean square error of the obtained rules are presented. One consists in introducing into a set of conditional variables the so-called meta-variable (an analogy to constructive induction) whose values are determined by an autoregressive or the ARIMA model. The other shows that limitation of a data set on which the M5 algorithm operates by the k-nearest neighbor method can also lead to error decreasing. Moreover, three application examples of the presented solutions for data collected by systems of natural hazards and machinery monitoring in coal mines are described. In Appendix, results of several benchmark data sets analyses are given as a supplement of the presented results.