As the transportation and information industries continue to advance, the increasing variety of application scenarios, devices with computing capabilities, and a growing number of open ports have heightened security r...
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ISBN:
(纸本)9798350375084;9798350375077
As the transportation and information industries continue to advance, the increasing variety of application scenarios, devices with computing capabilities, and a growing number of open ports have heightened security risks for vehicle networks. To improve the accuracy of detecting abnormal traffic in vehicle networks, we propose a model based on ensemble learning with a Stacking model integration approach. This method includes a meta-classifier composed of decision trees, extremely randomized trees, and extreme gradient boosting. The final classification prediction results are obtained by linearly stacking input features and weights into a SoftMax meta-learner. Additionally, the research enhances the classification accuracy of network flow data through parameter optimization. Testing results on the real automotive hacker attack dataset, Car-Hacking, show that this method achieves an accuracy rate of up to 99.2% in detecting denial of service, gear spoofing, and RPM spoofing attack types, and up to 97.5% accuracy in Fuzzy attack types. The study indicates that this model has a low false positive rate, high detection accuracy, and high detection rate, significantly outperforming traditional detection methods based on other machinelearning technologies.
In the current digital age, the proliferation of music libraries and streaming services has led to an overwhelming abundance of musical content. Automated music genre detection is crucial for enhancing user experience...
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Fraud detection in blockchain transactions is critical as the technology becomes more integrated into financial systems. Traditional rule-based systems have become ineffective against new fraudulent tactics. To solve ...
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Manipulating the words in some useful ways makes the mind refreshed and worth seeking. Instead of the website, the music software is not able to grab the attention of the user in most of the moody cases. Also, the com...
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With advances in technology, the complexity of functionality implemented in integrated circuits are on the rise. One of the most challenging aspects associated with complex functionality is the verification of it. Des...
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ISBN:
(纸本)9798331522452;9798331522445
With advances in technology, the complexity of functionality implemented in integrated circuits are on the rise. One of the most challenging aspects associated with complex functionality is the verification of it. Design verification should cross-check the functionality in different operating modes, input values, and module state. The verification problem gets exacerbated when we consider other challenges like (i) scalability to address the different module requirements for different market segments (ii) reducing time to market (iii) verification of corner case requirements. Given the complexity of the designs, it is not practical to do the complete verification. Hence, designs traditionally resorted to constrained random verification whereby random input stimulus is driven to the design, and the design output is verified using an independently coded model. The goodness of the verification is measured using code coverage and functional coverage. Many times during the random verification, the stimulus generated will not be adding to any incremental coverage thus resulting in more time to complete the verification. In this paper, we attempt to bridge this gap by using machinelearning. The ML model is leveraged here to predict if the given randomly generated stimulus will provide incremental coverage and then the stimulus is simulated based on the prediction.
Technology now a days has become the most important requirement in *** the use of technology increases the threats also enhanced as most of the applications always require internet to access data through network. Due ...
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作者：
Qin, JieliXiamen Inst Technol
Higher Educ Key Lab Flexible Mfg Equipment Integr Xiamen 361021 Peoples R China Xiamen Inst Technol
Sch Mech Elect & Informat Engn Xiamen 361021 Fujian Peoples R China
With the rapid development of the semiconductor industry, the process technology has become more complex, and it is increasingly important to maximize the control of defects in production and improve wafer yield. Defe...
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ISBN:
(纸本)9798350375084;9798350375077
With the rapid development of the semiconductor industry, the process technology has become more complex, and it is increasingly important to maximize the control of defects in production and improve wafer yield. Defects shown in Wafer Bin Maps (WBMS) are often strongly correlated with local system failures in production. In view of this, this paper proposes the convolutional neural networks (CNNs) architecture based on machinelearning, and realizes the classification of hybrid defects by designing a separate classification model of minimum defects and using the probability analysis of recognition results. The purpose is to classify and statistics the faults of the wafer diagram fault diagram, perform yield analysis and calculation, identify defect types according to the wafer (fault pattern recognition), and take corresponding decisions to adjust the process in view of the systematic fault chip. The effectiveness of the method is proved by experiments, and the identification accuracy of single defects is higher, and the detection performance of mixed defects is also improved, and the overall performance is better.
Federated learning (FL) is a rapidly growing field in machinelearning that allows data to be trained across multiple decentralized devices. The selection of clients to participate in the training process is a critica...
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To meet the demand of modern application, modern machinelearning and adaptive signal processing techniques are needed. With the help of revolutionary advancements in mobile communication such as 5G and 6G, integratio...
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Reinforcement learning (RL) has emerged as a vital component in the development of autonomous systems. However, several challenges, such as high computational demands, limited generalization in dynamic environments, a...
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