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

100%

Kaur G. , Gupta P.

2023

tom Vol. 30, no. 2

167--186

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.

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

80%

Nguyen H. , Bui X. N. , Moayedi H.

tom Vol. 67, no. 4

1025--1037

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.