DL-Powered Anomaly Identification System for Enhanced IoT Data Security

In many commercial and public sectors,the Internet of Things(IoT)is deeply embedded.Cyber security threats aimed at compromising the security,reliability,or accessibility of data are a serious concern for the IoT.Due to the collection of data from several IoT devices,the IoT presents unique challenges for detecting anomalous behavior.It is the responsibility of an Intrusion Detection System(IDS)to ensure the security of a network by reporting any suspicious activity.By identifying failed and successful attacks,IDS provides a more comprehensive security capability.A reliable and efficient anomaly detection system is essential for IoT-driven decision-making.Using deep learning-based anomaly detection,this study proposes an IoT anomaly detection system capable of identifying relevant characteristics in a controlled environment.These factors are used by the classifier to improve its ability to identify fraudulent IoT data.For efficient outlier detection,the author proposed a Convolutional Neural Network(CNN)with Long Short Term Memory(LSTM)based Attention Mechanism(ACNN-LSTM).As part of the ACNN-LSTM model,CNN units are deployed with an attention mechanism to avoid memory loss and gradient dispersion.Using the N-BaIoT and IoT-23 datasets,the model is verified.According to the N-BaIoT dataset,the overall accuracy is 99%,and precision,recall,and F1-score are also 0.99.In addition,the IoT-23 dataset shows a commendable accuracy of 99%.In terms of accuracy and recall,it scored 0.99,while the F1-score was 0.98.The LSTM model with attention achieved an accuracy of 95%,while the CNN model achieved an accuracy of 88%.According to the loss graph,attention-based models had lower loss values,indicating that they were more effective at detecting anomalies.In both the N-BaIoT and IoT-23 datasets,the receiver operating characteristic and area under the curve(ROC-AUC)graphs demonstrated exceptional accuracy of 99%to 100%for the Attention-based CNN and LSTM models.This indicates that these models are capable of making precise predictions.

An Intelligent Cardiovascular Diseases Prediction System Focused on Privacy

Machine learning(ML)and cloud computing have now evolved to the point where they are able to be used effectively.Further improvement,however,is required when both of these technologies are combined to reap maximum bene-fits.A way of improving the system is by enabling healthcare workers to select appropriate machine learning algorithms for prediction and,secondly,by preser-ving the privacy of patient data so that it cannot be misused.The purpose of this paper is to combine these promising technologies to maintain the privacy of patient data during the disease prediction process.Treatment of heart failure may be improved and expedited with this framework.We used the following machine learning algorithms to make predictions:Logistic Regression(LR),Naive Bayes(NB),K-Nearest Neighbors(KNN),Decision Tree(DT)and Support Vector Machines(SVM).These techniques,combined with cloud computing ser-vices,improved the process of deciding whether to treat a patient with cardiac dis-ease.Using our classifiers,we classified cardiac patients according to their features,which are grouped into single features,combinations of selected fea-tures,and all features.In experiments using all clinical features,machine learning classifiers SVM,DT,and KNN outperformed the rest,whereas in experiments using minimal clinical features,SVM and KNN were the most accurate.Internet of Things(IoT)devices allow family physicians to share diagnostic reports on the cloud in a secure manner.Ring signatures are particularly useful for verifying the integrity of data exchange.Our system keeps the physician's identity confidential from all authorized users,who can still access medical reports publicly.Our pro-posed mechanism has been shown to be both effective and efficient when it comes to obtaining patient reports from cloud storage.

Survey and Analysis of VoIP Frame Aggregation Methods over A-MSDU IEEE 802.11n Wireless Networks

The IEEE 802.11n standard has provided prominent features that greatly contribute to ubiquitous wireless networks.Over the last ten years,voice over IP(VoIP)has become widespread around the globe owing to its low-cost or even free call rate.The combination of these technologies(VoIP and wireless)has become desirable and inevitable for organizations.However,VoIP faces a bandwidth utilization issue when working with 802.11 wireless networks.The bandwidth utilization is inefficient on the grounds that(i)80 bytes of 802.11/RTP/UDP/IP header is appended to 10–730 bytes of VoIP payload and(ii)765μs waiting intervals follow each 802.11 VoIP frame.Without considering the quality requirements of a VoIP call,be including frame aggregation in the IEEE 802.11n standard has been suggested as a solution for the bandwidth utilization issue.Consequently,several aggregation methods have been proposed to handle the quality requirements of VoIP calls when carried over an IEEE 802.11n wireless network.In this survey,we analyze the existing aggregation methods of VoIP over the A-MSDU IEEE 802.11n wireless standard.The survey provides researchers with a detailed analysis of the bandwidth utilization issue concerning the A-MSDU 802.11n standard,discussion of the main approaches of frame aggregation methods and existing aggregation methods,elaboration of the impact of frame aggregation methods on network performance and VoIP call quality,and suggestion of new areas to be investigated in conjunction with frame aggregation.The survey contributes by offering guidelines to design an appropriate,reliable,and robust aggregation method of VoIP over 802.11n standard.

Smart CardioWatch System for Patients with Cardiovascular Diseases Who Live Alone

The widespread use of smartwatches has increased their specific and complementary activities in the health sector for patient’s prognosis.In this study,we propose a framework referred to as smart forecasting CardioWatch(SCW)to measure the heart-rate variation(HRV)for patients with myocardial infarction(MI)who live alone or are outside their homes.In this study,HRV is used as a vital alarming sign for patients with MI.The performance of the proposed framework is measured using machine learning and deep learning techniques,namely,support vector machine,logistic regression,and decision-tree classification techniques.The results indicated that the analysis of heart rate can help health services that are located remotely from the patient to render timely emergency health care.Further,taking more cardiac parameters into account can lead to more accurate results.On the basis of our findings,we recommend the development of health-related software to aid researchers to develop frameworks,such as SCW,for effective provision of emergency health.

University Learning and Anti-Plagiarism Back-End Services

Plagiarism refers to the use of other people’s ideas and information without acknowledging the source.In this research,anti-plagiarism software was designed especially for the university and its campuses to identify plagiarized text in students’written assignments and laboratory reports.The proposed framework collected original documents to identify plagiarized text using natural language processing.Our research proposes a method to detect plagiarism by applying the core concept of text,which is semantic associations of words and their syntactic composition.Information on the browser was obtained through Request application programming interface by name Url.AbsoluteUri,and it is stored in a centralized Microsoft database Server.A total of 55,001 data samples were collected from 2015 to 2019.Furthermore,we assimilated data from a university website,specifically from the psau.edu.sa network,and arranged the data into students’categories.Furthermore,we extracted words from source documents and student documents using the WordNet library.On a benchmark dataset consisting of 785 plagiarized text and 4,716 original text data,a significant accuracy of 90.2%was achieved.Therefore,the proposed framework demonstrated better performance than the other available tools.Many students mentioned that working on assignments using the framework was suitable because they were able to work on the assignments in harmony,as per their timeframe and from different network locations.The framework also recommends procedures that can be used to avoid plagiarism.

Multi Criteria Decision Making System for Parking System

System supported smart parking can reduce traffic by making it stress free to locate empty parking spaces,hence lowering the risk of unfocussed driving.In this study,we propose a smart parking system using deep learning and an application-based approach.This system has two modules,one module detects and recognizes a license plate(LP),and the other selects a parking space;both modules use deep learning techniques.We used two modules that work independently to detect and recognize an LP by using an image of the vehicle.To detect parking space,only deep learning techniques were used.The two modules were compared with other state-of-the-art solutions.We utilized the You Only Look Once(YOLO)architecture to detect and recognize an LP because its performance in the context of Saudi Arabian LP numbers was superior to that of other solutions.Compared with existing state-of-the-art solutions,the performance of the proposed solution was more effective.The solution can be further improved for use in the city and large organizations that have priority parking spaces.A dataset of LP-annotated images of vehicles was used.The results of this study have considerable implications for smart parking,particularly in universities;in addition,they can be utilized for smart cities.