multi-layer perceptron

The concept of the Internet of Things (IoT) and its countless applications are considered as an inseparable part of the modern technology era. The placement of IoT- based devices and their limitations make the environment more vulnerable due to its openness. Security plays a critical role in IoT applications due to the pervasiveness of the IoT in all of the aspects in daily life. On the other hand, final devices such as their limited computing power, large number of devices connected to each other, and communication between devices and users do not allow for using traditional methods to solve security issues. Intrusion detection systems (IDSs) which can separate malicious traffic from normal mode are among the effective solutions in this field. the installed IDS should be highly accurate and lightweight to affect accuracy. In order to bring services closer to electronic devices, a concept called “fog” has emerged. A large number of studies have been conducted to make the light IDS for IoT networks utilizing various methods. The present study aims to proposea two-layer hierarchical IDS based on machine learning, which detects attacks by considering the limitations of IoT resources. In order to create an efficient and accurate IDS, the combination of two improved K-nearest neighbor (KNN) algorithms and a multi-layer perceptron (MLP) neural network was applied in the fog and cloud to separate the attacks from normal traffic, respectively. we evaluated our proposed method using IOT23 dataset. The results prove the improvement in accuracy, compared to the previous methods.

Distributed Denial of Service (DDoS) attacks are among the primary concerns in internet security today. Machine learning can be exploited to detect such attacks. In this paper, a multi-layer perceptron model is proposed and implemented using deep machine learning to distinguish between malicious and normal traffic based on their behavioral patterns. The proposed model is trained and tested using the CICDDoS2019 dataset. To remove irrelevant and redundant data from the dataset and increase learning accuracy, feature selection is used to select and extract the most effective features that allow us to detect these attacks. Moreover, we use the grid search algorithm to acquire optimum values of the model’s hyperparameters among the parameters’ space. In addition, the sensitivity of accuracy of the model to variations of an input parameter is analyzed. Finally, the effectiveness of the presented model is validated in comparison with some state-of-the-art works.

The purpose of this study is to predict catching diabetes in patients having the β-thalassemia. Here, an intelligent system predicts risk of catching diabetes in patients having major thalassemia and intermedia, using multilayer perceptron according to the thalassemia dataset, ZAFAR. In this work, the clinical characteristics of 255 patients, having β-thalassemia, have been studied in two groups of diabetic and non- diabetic patients. Research data includes gender, age, parental family relationship, type of thalassemia, the spleen, gall bladder and liver condition, the age of blood transfusion, blood transfusion intervals, the number of received blood units in each period, the condition of iron overload, the age of start iron chelation therapy, the average of heart T2 rates, the liver T2 and LIC, the number of years having MRI, serum ferritin level, glucose and the number of years the patient was observe. The accuracy of artificial neural network in diagnosing the patients having thalassemia and exposing diabetes would be 80.78% according to the collected dataset. The accuracy rate of the system is 89.48%, using this intelligent system and doing pre-processing. This system has a desirable performance in predicting catching diabetes in patients having β-thalassemia. The best mean square error in this model is 0.07 which results in reducing learning time and increasing the system accuracy. Based on the obtained results from this system, there are two important factors in catching diabetes by the patients having β-thalassemia; first the number of years passed by patient with the high serum ferritin level and second is the glucose level rate in previous years long. The purpose of this study is to predict catching diabetes in patients having the β-thalassemia. Here, an intelligent system predicts risk of catching diabetes in patients having major thalassemia and intermedia, using multilayer perceptron according to the thalassemia dataset, ZAFAR. In this work, the clinical characteristics of 255 patients, having β-thalassemia, have been studied in two groups of diabetic and non- diabetic patients. Research data includes gender, age, parental family relationship, type of thalassemia, the spleen, gall bladder and liver condition, the age of blood transfusion, blood transfusion intervals, the number of received blood units in each period, the condition of iron overload, the age of start iron chelation therapy, the average of heart T2 rates, the liver T2 and LIC, the number of years having MRI, serum ferritin level, glucose and the number of years the patient was observe. The accuracy of artificial neural network in diagnosing the patients having thalassemia and exposing diabetes would be 80.78% according to the collected dataset. The accuracy rate of the system is 89.48%, using this intelligent system and doing pre-processing. This system has a desirable performance in predicting catching diabetes in patients having β-thalassemia. The best mean square error in this model is 0.07 which results in reducing learning time and increasing the system accuracy. Based on the obtained results from this system, there are two important factors in catching diabetes by the patients having β-thalassemia; first the number of years passed by patient with the high serum ferritin level and second is the glucose level rate in previous years long. The purpose of this study is to predict catching diabetes in patients having the β-thalassemia. Here, an intelligent system predicts risk of catching diabetes in patients having major thalassemia and intermedia, using multilayer perceptron according to the thalassemia dataset, ZAFAR. In this work, the clinical characteristics of 255 patients, having β-thalassemia, have been studied in two groups of diabetic and non- diabetic patients. Research data includes gender, age, parental family relationship, type of thalassemia, the spleen, gall bladder and liver condition, the age of blood transfusion, blood transfusion intervals, the number of received blood units in each period, the condition of iron overload, the age of start iron chelation therapy, the average of heart T2 rates, the liver T2 and LIC, the number of years having MRI, serum ferritin level, glucose and the number of years the patient was observe. The accuracy of artificial neural network in diagnosing the patients having thalassemia and exposing diabetes would be 80.78% according to the collected dataset. The accuracy rate of the system is 89.48%, using this intelligent system and doing pre-processing. This system has a desirable performance in predicting catching diabetes in patients having β-thalassemia. The best mean square error in this model is 0.07 which results in reducing learning time and increasing the system accuracy. Based on the obtained results from this system, there are two important factors in catching diabetes by the patients having β-thalassemia; first the number of years passed by patient with the high serum ferritin level and second is the glucose level rate in previous years long.