Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

Heart monitoring improves life quality.Electrocardiograms(ECGs or EKGs)detect heart irregularities.Machine learning algorithms can create a few ECG diagnosis processing methods.The first method uses raw ECG and time-series data.The second method classifies the ECG by patient experience.The third technique translates ECG impulses into Q waves,R waves and S waves(QRS)features using richer information.Because ECG signals vary naturally between humans and activities,we will combine the three feature selection methods to improve classification accuracy and diagnosis.Classifications using all three approaches have not been examined till now.Several researchers found that Machine Learning(ML)techniques can improve ECG classification.This study will compare popular machine learning techniques to evaluate ECG features.Four algorithms—Support Vector Machine(SVM),Decision Tree,Naive Bayes,and Neural Network—compare categorization results.SVM plus prior knowledge has the highest accuracy(99%)of the four ML methods.QRS characteristics failed to identify signals without chaos theory.With 99.8%classification accuracy,the Decision Tree technique outperformed all previous experiments.

An Efficient Encryption and Compression of Sensed IoT Medical Images Using Auto-Encoder

Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common practice.Encryption ofmedical images is very important to secure patient information.Encrypting these images consumes a lot of time onedge computing;therefore,theuse of anauto-encoder for compressionbefore encodingwill solve such a problem.In this paper,we use an auto-encoder to compress amedical image before encryption,and an encryption output(vector)is sent out over the network.On the other hand,a decoder was used to reproduce the original image back after the vector was received and decrypted.Two convolutional neural networks were conducted to evaluate our proposed approach:The first one is the auto-encoder,which is utilized to compress and encrypt the images,and the other assesses the classification accuracy of the image after decryption and decoding.Different hyperparameters of the encoder were tested,followed by the classification of the image to verify that no critical information was lost,to test the encryption and encoding resolution.In this approach,sixteen hyperparameter permutations are utilized,but this research discusses three main cases in detail.The first case shows that the combination of Mean Square Logarithmic Error(MSLE),ADAgrad,two layers for the auto-encoder,and ReLU had the best auto-encoder results with a Mean Absolute Error(MAE)=0.221 after 50 epochs and 75%classification with the best result for the classification algorithm.The second case shows the reflection of auto-encoder results on the classification results which is a combination ofMean Square Error(MSE),RMSprop,three layers for the auto-encoder,and ReLU,which had the best classification accuracy of 65%,the auto-encoder gives MAE=0.31 after 50 epochs.The third case is the worst,which is the combination of the hinge,RMSprop,three layers for the auto-encoder,and ReLU,providing accuracy of 20%and MAE=0.485.

An Efficient Intrusion Detection Framework for Industrial Internet of Things Security

Recently,the Internet of Things(IoT)has been used in various applications such as manufacturing,transportation,agriculture,and healthcare that can enhance efficiency and productivity via an intelligent management console remotely.With the increased use of Industrial IoT(IIoT)applications,the risk of brutal cyber-attacks also increased.This leads researchers worldwide to work on developing effective Intrusion Detection Systems(IDS)for IoT infrastructure against any malicious activities.Therefore,this paper provides effective IDS to detect and classify unpredicted and unpredictable severe attacks in contradiction to the IoT infrastructure.A comprehensive evaluation examined on a new available benchmark TON_IoT dataset is introduced.The data-driven IoT/IIoT dataset incorporates a label feature indicating classes of normal and attack-targeting IoT/IIoT applications.Correspondingly,this data involves IoT/IIoT services-based telemetry data that involves operating systems logs and IoT-based traffic networks collected from a realistic medium-scale IoT network.This is to classify and recognize the intrusion activity and provide the intrusion detection objectives in IoT environments in an efficient fashion.Therefore,several machine learning algorithms such as Logistic Regression(LR),Linear Discriminant Analysis(LDA),K-Nearest Neighbors(KNN),Gaussian Naive Bayes(NB),Classification and Regression Tree(CART),Random Forest(RF),and AdaBoost(AB)are used for the detection intent on thirteen different intrusion datasets.Several performance metrics like accuracy,precision,recall,and F1-score are used to estimate the proposed framework.The experimental results show that the CART surpasses the other algorithms with the highest accuracy values like 0.97,1.00,0.99,0.99,1.00,1.00,and 1.00 for effectively detecting the intrusion activities on the IoT/IIoT infrastructure on most of the employed datasets.In addition,the proposed work accomplishes high performance compared to other recent related works in terms of different security and detection evaluation parameters.

Bayesian belief-based model for reliability improvement of the digital reactor protection system

The digital reactor protection system(RPS)is one of the most important digital instrumentation and control(I&C)systems utilized in nuclear power plants(NPPs).It ensures a safe reactor trip when the safety-related parameters violate the operational limits and conditions of the reactor.Achieving high reliability and availability of digital RPS is essential to maintaining a high degree of reactor safety and cost savings.The main objective of this study is to develop a general methodology for improving the reliability of the RPS in NPP,based on a Bayesian Belief Network(BBN)model.The structure of BBN models is based on the incorporation of failure probability and downtime of the RPS I&C components.Various architectures with dual-state nodes for the I&C components were developed for reliability-sensitive analysis and availability optimization of the RPS and to demonstrate the effect of I&C components on the failure of the entire system.A reliability framework clarified as a reliability block diagram transformed into a BBN representation was constructed for each architecture to identify which one will fit the required reliability.The results showed that the highest availability obtained using the proposed method was 0.9999998.There are 120 experiments using two common component importance measures that are applied to define the impact of I&C modules,which revealed that some modules are more risky than others and have a larger effect on the failure of the digital RPS.

Advanced Transition/Cluster Key Management Scheme for End-System Multicast Protocol

The recent growth of the World Wide Web has sparked new research into using the Internet for novel types of group communication, like multiparty videoconferencing and real-time streaming. Multicast has the potential to be very useful, but it suffers from many problems like security. To achieve secure multicast communications with the dynamic aspect of group applications due to free membership joins and leaves in addition to member's mobility, key management is one of the most critical problems. So far, a lot of multicast key management schemes have been proposed and most of them are centralized, which have the problem of 'one point failure' and that the group controller is the bottleneck of the group. In order to solve these two problems, we propose a Key Management Scheme, using cluster-based End-System Multicast (ESM). The group management is between both 1) the main controller (MRP, Main Rendezvous Point) and the second controllers (CRP, Cluster RP), and 2) the second controllers (CRPs) and its members. So, ESM simplifies the implementation of group communication and is efficient ways to deliver a secure message to a group of recipients in a network as a practical alternative to overcome the difficulty of large scale deployment of traditional IP multicast. In this paper, we analyze different key management schemes and propose a new scheme, namely Advanced Transition/Cluster Key management Scheme (ATCKS) and find it has appropriate performance in security.

A Survey of MANET Survivability Routing Techniques

Mobile ad hoc networks have a wide range of application usage today, due to its great services, easy installation and configuration, and its other distinctive characteristics. In contrast, the attackers also have developed their own way to disrupt MANET normal operations. Many techniques, approaches and protocols have proposed to support Mobile Ad hoc Network (MANET) survivability in adversarial environment. Survivable of routing operations is the key aspects of the challenge in MANETs because most of destructive attacks classified as active attacks and all are intent to attack MANET routing operation to prevent it from providing it services in a right time. In this paper, we will discuss the most effective and practical initiatives have designed to keep MANET survive in an adversarial environment and how it supporting MANET availability.