Information security has emerged as a crucial consideration over the past decade due to escalating cyber security threats,with Internet of Things(IoT)security gaining particular attention due to its role in data commu...Information security has emerged as a crucial consideration over the past decade due to escalating cyber security threats,with Internet of Things(IoT)security gaining particular attention due to its role in data communication across various industries.However,IoT devices,typically low-powered,are susceptible to cyber threats.Conversely,blockchain has emerged as a robust solution to secure these devices due to its decentralised nature.Nevertheless,the fusion of blockchain and IoT technologies is challenging due to performance bottlenecks,network scalability limitations,and blockchain-specific security vulnerabilities.Blockchain,on the other hand,is a recently emerged information security solution that has great potential to secure low-powered IoT devices.This study aims to identify blockchain-specific vulnerabilities through changes in network behaviour,addressing a significant research gap and aiming to mitigate future cybersecurity threats.Integrating blockchain and IoT technologies presents challenges,including performance bottlenecks,network scalability issues,and unique security vulnerabilities.This paper analyses potential security weaknesses in blockchain and their impact on network operations.We developed a real IoT test system utilising three prevalent blockchain applications to conduct experiments.The results indicate that Distributed Denial of Service(DDoS)attacks on low-powered,blockchain-enabled IoT sensor networks cause measurable anomalies in network and device performance,specifically:(1)an average increase in CPU core usage to 34.32%,(2)a reduction in hash rates by up to 66%,(3)an increase in batch timeout by up to 14.28%,and(4)an increase in block latency by up to 11.1%.These findings suggest potential strategies to counter future DDoS attacks on IoT networks.展开更多
Recently,the spread of COVID-19 virus infection and the increase of people number with chronic diseases have attracted great attention all over the world.The detection and control of such diseases based on patient dem...Recently,the spread of COVID-19 virus infection and the increase of people number with chronic diseases have attracted great attention all over the world.The detection and control of such diseases based on patient demographic data are considered to be a major problem.The key issue in the solution to these problems is the development of methods and algorithms to forecast wellness and categorise patients according to their healthy and unhealthy states.In this paper,a comprehensive analysis of machine learning approaches in the field of diagnosing COVID-19 has been conducted,and for the detection of chronic diseases in patients,to identify symptoms of COVID-19 virus infection in advance,and control the situation a healthcare system has been proposed.The constructed system provides real-time monitoring of chronic diseases and COVID-19 virus infection in patients.The proposed system consists of five layers:IoT sensor layer,Data transmission layer,Fog layer,Cloud layer,the Application layer.The system architecture in the Fog layer uses machine learning and deep learning algorithms to diagnose patients'diseases,to generate and send diagnostic and emergency alerts to users.The classification module of the system's Fog layer categorises the patient's health status into healthy and unhealthy classes.In this module,to classify medical data the Decision Tree,Random Forest,SVM,Gradient Boosting,Logistic Regression algorithms are used.The COVID-19 dataset is used to test the effectiveness of the methods.The best results from the comparative analysis of the methods are obtained from the Decision Tree,Random Forest,and Gradient Boosting algorithms,which are recognised data points with high accuracy and on the accuracy metric reached 1.0,0.99,1.0 values,respectively.The classification of the other two SVM and Logistic Regression algorithms provided the worst results,and the accuracy score of both classifiers obtained a 0.89 value.展开更多
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(Grant number IMSIU-RP23017).
文摘Information security has emerged as a crucial consideration over the past decade due to escalating cyber security threats,with Internet of Things(IoT)security gaining particular attention due to its role in data communication across various industries.However,IoT devices,typically low-powered,are susceptible to cyber threats.Conversely,blockchain has emerged as a robust solution to secure these devices due to its decentralised nature.Nevertheless,the fusion of blockchain and IoT technologies is challenging due to performance bottlenecks,network scalability limitations,and blockchain-specific security vulnerabilities.Blockchain,on the other hand,is a recently emerged information security solution that has great potential to secure low-powered IoT devices.This study aims to identify blockchain-specific vulnerabilities through changes in network behaviour,addressing a significant research gap and aiming to mitigate future cybersecurity threats.Integrating blockchain and IoT technologies presents challenges,including performance bottlenecks,network scalability issues,and unique security vulnerabilities.This paper analyses potential security weaknesses in blockchain and their impact on network operations.We developed a real IoT test system utilising three prevalent blockchain applications to conduct experiments.The results indicate that Distributed Denial of Service(DDoS)attacks on low-powered,blockchain-enabled IoT sensor networks cause measurable anomalies in network and device performance,specifically:(1)an average increase in CPU core usage to 34.32%,(2)a reduction in hash rates by up to 66%,(3)an increase in batch timeout by up to 14.28%,and(4)an increase in block latency by up to 11.1%.These findings suggest potential strategies to counter future DDoS attacks on IoT networks.
基金This study was funded by the project“Management of big data resources in the electronic socio-technological environment,the formation of electronic demography and the development of its intellectual analysis technologies”.
文摘Recently,the spread of COVID-19 virus infection and the increase of people number with chronic diseases have attracted great attention all over the world.The detection and control of such diseases based on patient demographic data are considered to be a major problem.The key issue in the solution to these problems is the development of methods and algorithms to forecast wellness and categorise patients according to their healthy and unhealthy states.In this paper,a comprehensive analysis of machine learning approaches in the field of diagnosing COVID-19 has been conducted,and for the detection of chronic diseases in patients,to identify symptoms of COVID-19 virus infection in advance,and control the situation a healthcare system has been proposed.The constructed system provides real-time monitoring of chronic diseases and COVID-19 virus infection in patients.The proposed system consists of five layers:IoT sensor layer,Data transmission layer,Fog layer,Cloud layer,the Application layer.The system architecture in the Fog layer uses machine learning and deep learning algorithms to diagnose patients'diseases,to generate and send diagnostic and emergency alerts to users.The classification module of the system's Fog layer categorises the patient's health status into healthy and unhealthy classes.In this module,to classify medical data the Decision Tree,Random Forest,SVM,Gradient Boosting,Logistic Regression algorithms are used.The COVID-19 dataset is used to test the effectiveness of the methods.The best results from the comparative analysis of the methods are obtained from the Decision Tree,Random Forest,and Gradient Boosting algorithms,which are recognised data points with high accuracy and on the accuracy metric reached 1.0,0.99,1.0 values,respectively.The classification of the other two SVM and Logistic Regression algorithms provided the worst results,and the accuracy score of both classifiers obtained a 0.89 value.
