In this paper use CIC-IDS2017 dataset to illustrate a comparative analysis of traditional and proposed models for intrusion detection in network security systems. The comparison includes DT, RF, ET and XGBoost classif...
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New security concerns about the transmission of sensitive data over enormous networks of linked devices have arisen with the advent of the 6G era and the broad adoption of massive machine-type communication (MTC). The...
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The present network infrastructure is safeguarded against cyber threats using network Intrusion Detection Systems (NIDS). Many existing methods, including basic deep learning approaches on graph data, struggle to capt...
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ISBN:
(纸本)9783031804076;9783031804083
The present network infrastructure is safeguarded against cyber threats using network Intrusion Detection Systems (NIDS). Many existing methods, including basic deep learning approaches on graph data, struggle to capture the spatiotemporal relationships between network nodes. They often don't consider the data in a continuous time format. To address these issues, we propose NID-TGN, an encoder-decoder model for intrusion detection in IoT dynamic networks. The encoder enhances the Temporal Graph network (TGN) framework by incorporating a learnable aggregation mechanism that better processes continuous time dynamic graph data. The decoder combines feature selection techniques with a random forest classifier, using only the node embeddings generated by the encoder to predict cyber attacks with an accuracy of 97%.
This paper explores trusted measurement technologies for Power Internet of Things (IoT) terminals, advocating for a dual-system architecture with a Trusted Platform Control Module (TPCM) at its core. The TPCM systemat...
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With the cross development of computational neuroscience and artificial intelligence, research on human brain neural network simulation and signal processing technology has become a hot topic of common concern in both...
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ISBN:
(数字)9781510686731
ISBN:
(纸本)9781510686724
With the cross development of computational neuroscience and artificial intelligence, research on human brain neural network simulation and signal processing technology has become a hot topic of common concern in both academia and industry. This study used a multi-level and multi-scale computational model and high-precision algorithm to simulate the dynamic behavior of human brain neural networks, exploring the complex interactions between neurons and their impact on information processing capabilities. Real time dynamic simulation of billions of neurons was achieved on a simulation platform using ultra large-scale integrated circuit chips, simulating signal processing patterns similar to those of the human brain. By using an improved backpropagation algorithm to decode and reconstruct neural signals, the efficiency of the algorithm and the accuracy of signal processing have been improved. On this basis, combined with experimental data obtained from functional magnetic resonance imaging, the similarities and differences between neural network simulation results and actual human brain activity were compared and analyzed, revealing the potential connection between cognitive function and brain network activity patterns. This study not only achieved new breakthroughs in simulation technology and signal processing algorithms, but also provided a new quantitative tool and theoretical support for related neuroscience research, which is of great significance for the development of brain computer interfaces and intelligent information processing systems. In addition, the study also delved into the balance between ensuring model complexity and processing efficiency, as well as the challenges and opportunities brought by interdisciplinary collaboration in the field of neuroscience. Through cross validation and error analysis of simulation experiments, the effectiveness of the model and the accuracy of prediction results were ensured. Based on this, feasible suggestions for opt
In Industrial Internet of Things (IIoT) settings, effective data processing and energy management are essential due to the resource-limited characteristics of the linked equipment. This research introduces a unique Sa...
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In high-density crowd, a unique visual motion effect called stop-and-go wave occurs, which could evolve to trampling and compression incidents. However, few computational models have been reported for stop-and-go wave...
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An artificial intelligence (AI) system works by combining a computer program and algorithms to make a device more efficient and intelligent for tasks that are typically performed by humans. Deep learning, machine lear...
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Drones are essential for civil engineering operations like logistics and data collecting. Current autonomous drone studies mainly concerns itself with safe path planning in static scenarios;however one of the major ch...
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The feasibility of using blockchain technology as a method to improve cybersecurity through data security transactions and users' anonymity is discussed in this paper. It scans network traffic, sings out abnormiti...
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