Development and evaluation of ensemble learning models for detection of distributed denial-of-service attacks in ınternet of things

Internet of Things that process tremendous confidential data have difficulty performing traditional security algorithms, thus their security is at risk. The security tasks to be added to these devices should be able to operate without disturbing the smooth operation of the system so that the availability of the system will not be impaired. While various attack detection systems can detect attacks with high accuracy rates, it is often impossible to integrate them into Internet of Things devices. Therefore, in this work, the new Distributed Denial-of-Service (DDoS) detection models using feature selection and learning algorithms jointly are proposed to detect DDoS attacks, which are the most common type encountered by Internet of Things networks. Additionally, this study evaluates the memory consumption of single-based, bagging, and boosting algorithms on the client-side which has scarce resources. Not only the evaluation of memory consumption but also development of ensemble learning models refer to the novel part of this study. The data set consisting of 79 features in total created for the detection of DDoS attacks was minimized by selecting the two most significant features. Evaluation results confirm that the DDoS attack can be detected with high accuracy and less memory usage by the base models compared to complex learning methods such as bagging and boosting models. As a result, the findings demonstrate the feasibility of the base models, for the Internet of Things DDoS detection task, due to their application performance

Hybrid machine learning model coupled with school closure for forecasting covıd-19 cases in the most affected countries

Coronavirus disease (Covid-19) caused millions of confirmed cases and deaths worldwide since first appeared in China. Forecasting methods are essential to take precautions early and control the spread of this rapidly expanding pandemic. Therefore, in this research, a new customized hybrid model consisting of Back Propagation-Based Artificial Neural Network (BP-ANN), Correlated Additive Model (CAM) and Auto-Regressive Integrated Moving Average (ARIMA) models were developed for the purpose of forecast Covid-19 prevalence in Brazil, US, Russia and India. The Covid-19 dataset is obtained from the World Health Organization website from 22 January, 2020 to 6 January, 2021. Various parameters were tested to select the best ARIMA models for these countries based on the lowest MAPE values (5.21, 11.42, 1.45, 2.72) for Brazil, the US, Russia and India, respectively. On the other hand, the proposed BP-ANN model itself provided less satisfactory MAPE values. Finally, the developed new customized hybrid model was achieved to obtain the best MAPE results (4.69, 6.4, 0.63, 2.25) for forecasting Covid-19 prevalence in Brazil, the US, Russia and India, respectively. Those results emphasize the validity of our hybrid model. Besides, the proposed prediction models can assist countries in terms of taking important precautions to control the spread of Covid-19 in the world

Correlation value determined to increase Salmonella prediction success of deep neural network for agricultural waters

The use of computer-based tools has been becoming popular in the field of produce safety. Various algorithms have been applied to predict the population and presence of indicator microorganisms and pathogens in agricultural water sources. The purpose of this study is to improve the Salmonella prediction success of deep feed-forward neural network (DFNN) in agricultural surface waters with a determined correlation value based on selected features. Datasets were collected from six agricultural ponds in Central Florida. The most successful physicochemical and environmental features were selected by the gain ratio for the prediction of generic Escherichia coli population with machine learning algorithms (decision tree, random forest, support vector machine). Salmonella prediction success of DFNN was evaluated with dataset including selected environmental and physicochemical features combined with predicted E. coli populations with and without correlation value. The performance of correlation value was evaluated with all possible mathematical dataset combinations (nCr) of six ponds. The higher accuracy performances (%) were achieved through DFNN analyses with correlation value between 88.89 and 98.41 compared to values with no correlation value from 83.68 to 96.99 for all dataset combinations. The findings emphasize the success of determined correlation value for the prediction of Salmonella presence in agricultural surface waters.Cankiri Karatekin UniversityThis study was supported by Cankiri Karatekin University