A Novel Hybrid Architecture for Superior IoT Threat Detection through Real IoT Environments

As the Internet of Things(IoT)continues to expand,incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats,necessitating robust defense mechanisms.This paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world settings.Our proposed model combines Convolutional Neural Networks(CNN),Bidirectional Long Short-Term Memory(BLSTM),Gated Recurrent Units(GRU),and Attention mechanisms into a cohesive framework.This integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT systems.We evaluated our model using the RT-IoT2022 dataset,which includes various devices,standard operations,and simulated attacks.Our research’s significance lies in the comprehensive evaluation metrics,including Cohen Kappa and Matthews Correlation Coefficient(MCC),which underscore the model’s reliability and predictive quality.Our model surpassed traditional machine learning algorithms and the state-of-the-art,achieving over 99.6%precision,recall,F1-score,False Positive Rate(FPR),Detection Time,and accuracy,effectively identifying specific threats such as Message Queuing Telemetry Transport(MQTT)Publish,Denial of Service Synchronize network packet crafting tool(DOS SYN Hping),and Network Mapper Operating System Detection(NMAP OS DETECTION).The experimental analysis reveals a significant improvement over existing detection systems,significantly enhancing IoT security paradigms.Through our experimental analysis,we have demonstrated a remarkable enhancement in comparison to existing detection systems,which significantly strength-ens the security standards of IoT.Our model effectively addresses the need for advanced,dependable,and adaptable security solutions,serving as a symbol of the power of deep learning in strengthening IoT ecosystems amidst the constantly evolving cyber threat landscape.This achievement marks a significant stride towards protecting the integrity of IoT infrastructure,ensuring operational resilience,and building privacy in this groundbreaking technology.

Using Mixed Reality as a Simulation Tool in Urban Planning Project for Sustainable Development

Nowadays, urban design faces complex demands. It has become a necessity to negotiate between stakeholder objectives, the expectations of citizens and the demands of planning. It is desirable to involve the stakeholders and citizens from an early stage in the planning process to enable their different viewpoints to be successfully expressed and comprehended. Therefore, the basic aim of the study was how the MR （mixed reality） application is designed to encourage and improve communication on urban design among stakeholders and citizens？ In this paper, we discuss new approaches to visualize urban building and environment alternatives to different stakeholders and provide them with tools to explore different approaches to urban planning in order to support citizen＇s participation in urban planning with augmented and mixed reality. The major finding of the study is that learning ＂how these participatory technologies may help build a community of practice around an urban project＂. And throughout the different experiences, we can learn to assist towards development of a methodology to use the mixed reality as a simulation tool in the enhancement of collaborative interaction in real-Egyptian project. So, we can determine a number of recommendations to deal with new participatory design tools for urban planning projects.

Research and Design of the Adaptive Evaluation System Based on the IRT Theory

This paper, on the basis of the author realizing the skill evaluation system based on real environment, discusses several commonly used parameter estimation methods based on item response theory （ IRT ） and analyzes the advantages and disadvantages of each estimation method and their respective application fields. Also, it expounds the research theory and design process of skill adaptive evaluation system based on real environment and the innovation of the system.

Evaluation and optimization of a hybrid wave energy converter using excited motion response in two degrees of freedom

As waves in China seas are not high,a wave energy converter consisting of a coaxial annular buoy and a cylindrical buoy that extracts wave energy using two generators through the relative heave motion between the buoys and the pitch motion of the cylinder could be a more efficient choice.A dynamic model considering constraints and assuming linear power take-off is established to evaluate the power performance of the device.The influences of two key factors,the diameter of the annular buoy and the power take-off stiffness of the pitching generator,and their couplings on the power performance are analyzed.The power of the pitching generator accounts for a major proportion of the total power.An increase in the annular buoy diameter increases the power of the heaving generator while greatly decreases the power of the pitching generator.An increase in the power take-off stiffness of the pitching generator greatly decreases its power while has little influence on the power of the heaving generator.These two factors also influence the peak period of the total power.Based on the findings and practical limitations,an optimization strategy is proposed.Further,the device is optimized based on a real wave environment in Shandong Province,China.