Blockchain merges technology with the Internet of Things(IoT)for addressing security and privacy-related issues.However,conventional blockchain suffers from scalability issues due to its linear structure,which increas...Blockchain merges technology with the Internet of Things(IoT)for addressing security and privacy-related issues.However,conventional blockchain suffers from scalability issues due to its linear structure,which increases the storage overhead,and Intrusion detection performed was limited with attack severity,leading to performance degradation.To overcome these issues,we proposed MZWB(Multi-Zone-Wise Blockchain)model.Initially,all the authenticated IoT nodes in the network ensure their legitimacy by using the Enhanced Blowfish Algorithm(EBA),considering several metrics.Then,the legitimately considered nodes for network construction for managing the network using Bayesian-Direct Acyclic Graph(B-DAG),which considers several metrics.The intrusion detection is performed based on two tiers.In the first tier,a Deep Convolution Neural Network(DCNN)analyzes the data packets by extracting packet flow features to classify the packets as normal,malicious,and suspicious.In the second tier,the suspicious packets are classified as normal or malicious using the Generative Adversarial Network(GAN).Finally,intrusion scenario performed reconstruction to reduce the severity of attacks in which Improved Monkey Optimization(IMO)is used for attack path discovery by considering several metrics,and the Graph cut utilized algorithm for attack scenario reconstruction(ASR).UNSW-NB15 and BoT-IoT utilized datasets for the MZWB method simulated using a Network simulator(NS-3.26).Compared with previous performance metrics such as energy consumption,storage overhead accuracy,response time,attack detection rate,precision,recall,and F-measure.The simulation result shows that the proposed MZWB method achieves high performance than existing works.展开更多
Introduction With the deepening integration of digital technologies in education,virtual reality(VR)immersive learning has emerged as a transformative medium that breaks through traditional classroom constraints throu...Introduction With the deepening integration of digital technologies in education,virtual reality(VR)immersive learning has emerged as a transformative medium that breaks through traditional classroom constraints through its three core attributes:immersion,interactivity,and conceptualization.As of 2024,the global VR education market has surpassed$8 billion in value.By creating simulated learning environments—including virtual laboratories and historical scenario reconstructions—VR transforms abstract knowledge into tangible experiential learning content,significantly enhancing learners'engagement and retention.However,existing research reveals notable individual variations in VR immersion learning outcomes:some learners struggle with environmental adaptation and lack sufficient motivation,with psychological traits emerging as the key variable explaining these differences.展开更多
文摘Blockchain merges technology with the Internet of Things(IoT)for addressing security and privacy-related issues.However,conventional blockchain suffers from scalability issues due to its linear structure,which increases the storage overhead,and Intrusion detection performed was limited with attack severity,leading to performance degradation.To overcome these issues,we proposed MZWB(Multi-Zone-Wise Blockchain)model.Initially,all the authenticated IoT nodes in the network ensure their legitimacy by using the Enhanced Blowfish Algorithm(EBA),considering several metrics.Then,the legitimately considered nodes for network construction for managing the network using Bayesian-Direct Acyclic Graph(B-DAG),which considers several metrics.The intrusion detection is performed based on two tiers.In the first tier,a Deep Convolution Neural Network(DCNN)analyzes the data packets by extracting packet flow features to classify the packets as normal,malicious,and suspicious.In the second tier,the suspicious packets are classified as normal or malicious using the Generative Adversarial Network(GAN).Finally,intrusion scenario performed reconstruction to reduce the severity of attacks in which Improved Monkey Optimization(IMO)is used for attack path discovery by considering several metrics,and the Graph cut utilized algorithm for attack scenario reconstruction(ASR).UNSW-NB15 and BoT-IoT utilized datasets for the MZWB method simulated using a Network simulator(NS-3.26).Compared with previous performance metrics such as energy consumption,storage overhead accuracy,response time,attack detection rate,precision,recall,and F-measure.The simulation result shows that the proposed MZWB method achieves high performance than existing works.
文摘Introduction With the deepening integration of digital technologies in education,virtual reality(VR)immersive learning has emerged as a transformative medium that breaks through traditional classroom constraints through its three core attributes:immersion,interactivity,and conceptualization.As of 2024,the global VR education market has surpassed$8 billion in value.By creating simulated learning environments—including virtual laboratories and historical scenario reconstructions—VR transforms abstract knowledge into tangible experiential learning content,significantly enhancing learners'engagement and retention.However,existing research reveals notable individual variations in VR immersion learning outcomes:some learners struggle with environmental adaptation and lack sufficient motivation,with psychological traits emerging as the key variable explaining these differences.
