NSTL国家科技图书文献中心

1. University Network Intrusion Detection Method Based on Deep Learning NSTL国家科技图书文献中心

Huang Zhong - 《International Conference on Mechatronics and Intelligent Control (ICMIC 2024),Part Two of Two Parts》 - International Conference on Mechatronics and Intelligent Control - 2025, - 1344730.1～1344730.5 - 共5页

摘要： learning-based method for network* intrusion detection in* | network intrusion detection technology is reviewed, and | effectiveness of deep learning technology in network intrusion | With the increasing number of network* attacks* | , the network* security of universities has been*

关键词： Deep learning | Network intrusion detection | University network | Data preprocessing

2. SC-WGAN: GAN-Based Oversampling Method for Network Intrusion Detection NSTL国家科技图书文献中心

Wuxia Bai | Kailong Wang ... - 《Engineering of Complex Computer Systems》 - International Conference on Engineering of Complex Computer Systems - 2025, - 23～42 - 共20页

摘要： necessitates robust defenses, with Network Intrusion | Detection Systems (NIDS) at the forefront. Leveraging Deep | detection accuracy in NIDS. Nonetheless, the inherent data | imbalance between malicious and normal network* traffic* | detection performance. Addressing this, we introduce the

关键词： Network intrusion detection | Imbalanced data | Oversampling | GAN

3. Enhancing Network Intrusion Detection with VAE-GNN NSTL国家科技图书文献中心

Junyu Li | Haoxi Wang - 《Advanced Data Mining and Applications,Part VI》 - International Conference on Advanced Data Mining and Applications - 2025, - 302～317 - 共16页

摘要： severe. Intrusion* detection, as a key proactive defense* | novel intrusion* detection method based on VAE-GNN, an* | limitations of traditional intrusion* detection methods, such* | benchmark intrusion* detection datasets demonstrate that* | neural network methods, showing better detection

关键词： Intrusion detection system | Variational autoencoder | Graph neural network | Network security | Anomaly detection

4. An Investigation Into the Performance of Non-contrastive Self-supervised Learning Methods for Network Intrusion Detection NSTL国家科技图书文献中心

Hamed Fard | Tobias Schalau ... - 《Information and Communications Security,Part I》 - International Conference on Information and Communications Security - 2025, - 208～227 - 共20页

摘要：Network intrusion detection, a well | this paradigm for network* intrusion detection. While | intrusion detection* datasets, UNSW-NB15 and 5G-NIDD. For | , remains unclear for effective attack detection. This | experiments are systematically conducted on two network

关键词： Network intrusion detection | Self-Supervised learning | Data augmentation

5. Investigating the Transferability of Evasion Attacks in Network Intrusion Detection Systems Considering Domain-Specific Constraints NSTL国家科技图书文献中心

Mariama Mbow | Rodrigo Roman ... - 《Information Security Applications》 - International Conference on Information Security Applications - 2025, - 44～55 - 共12页

摘要： learning-based network* intrusion detection systems (NIDS* | for image classification without considering network | and will fail in the real world as network* traffic* | behavior of the network* traffic features leads to invalid* | network traffic flow or produces adversarial samples

关键词： Cybersecurity | Network intrusion detection systems | Adversarial machine learning | Evasion attacks

6. Neuro-Symbolic Integration for Open Set Recognition in Network Intrusion Detection NSTL国家科技图书文献中心

Alice Bizzarri | Chung-En Yu ... - 《AIxIA 2024 - Advances in Artificial Intelligence》 - International Conference of the Italian Association for Artificial Intelligence - 2025, - 50～63 - 共14页

摘要： vital in applications like Network Intrusion Detection | for network* intrusion detection by integrating deep* | Open Set Recognition (OSR) addresses the | challenge of classifying inputs into known and unknown | categories, a crucial task where labeling is often

关键词： Neuro-symbolic integration | Deep embedding for clustering | XGBoost | Open set recognition | Network intrusion detection

7. Research on feature classification of network intrusion detection based on deep learning NSTL国家科技图书文献中心

Feng Wei | Dongqing Liu ... - 《Fourth International Conference on Network Communication and Information Security (ICNCIS 2024)》 - International Conference on Network Communication and Information Security - 2025, - 135160S.1～135160S.7 - 共7页

摘要： network attack big data, the high dependency on prior | network based on CNN for classifying and detecting | network attacks in network security situation awareness | . A network* feature transfer learning method is* | introduced to solve the detection* and training efficiency*

关键词： CNN | Feature learning | Intrusion detection | Network attack

8. Enhanced Bayesian network classifier for intrusion detection: integrating semi-lazy learning and multi-conditional entropy NSTL国家科技图书文献中心

Yang Liu | Qi Wu ... - 《International Conference on Mechatronics and Intelligent Control (ICMIC 2024),Part One of Two Parts》 - International Conference on Mechatronics and Intelligent Control - 2025, - 1344709.1～1344709.8 - 共8页

摘要： cybersecurity, the need for sophisticated intrusion detection | Bayesian network* classifier (EBNC), integrating a semi* | to enhance the accuracy and efficiency of intrusion | detection. The EBNC dynamically constructs class-specific | the EBNC's efficiency and accuracy in intrusion

关键词： Machine learning | Classification | Bayesian network | Intrusion detection

9. A Novel and Efficient Multi-scale Spatio-Temporal Residual Network for Multi-class Intrusion Detection NSTL国家科技图书文献中心

Nan Li | Zhaojian Gao ... - 《Machine Learning for Cyber Security》 - International Conference on Machine Learning for Cyber Security - 2025, - 271～283 - 共13页

摘要： network intrusion detection models tend to disregard the | -based network* intrusion detection method, which* | intrusion detection models. To address the shortcomings of | the fact that intrusion* detection data has temporal* | With the development of network* devices, the*

关键词： Network traffic | Transformer | Temporal features | Intrusion detection

10. Railway Clearance Intrusion Detection Using Feature Fusion Enhancement Neural Network NSTL国家科技图书文献中心

Yuqiang He | Yu Yang ... - 《Eighth International Conference on Video and Image Processing (ICVIP 2024)》 - International Conference on Video and Image Processing - 2025, - 1355808.1～1355808.13 - 共13页

摘要： timely intrusion* detection is of great importance. To* | object-detection* method based on convolutional neural* | network (CNN), which is called Feature Fusion | Enhancement Neural Network* (FE-Net). It mainly includes* | Detection Module (RDM). FFE aims to enhance the detection

摘要： learning-based method for network* intrusion detection in* | network intrusion detection technology is reviewed, and | effectiveness of deep learning technology in network intrusion | With the increasing number of network* attacks* | , the network* security of universities has been*

关键词： Deep learning | Network intrusion detection | University network | Data preprocessing

关键词： Network intrusion detection | Imbalanced data | Oversampling | GAN

关键词： Intrusion detection system | Variational autoencoder | Graph neural network | Network security | Anomaly detection

摘要：Network intrusion detection, a well | this paradigm for network* intrusion detection. While | intrusion detection* datasets, UNSW-NB15 and 5G-NIDD. For | , remains unclear for effective attack detection. This | experiments are systematically conducted on two network

关键词： Network intrusion detection | Self-Supervised learning | Data augmentation

摘要： learning-based network* intrusion detection systems (NIDS* | for image classification without considering network | and will fail in the real world as network* traffic* | behavior of the network* traffic features leads to invalid* | network traffic flow or produces adversarial samples

关键词： Cybersecurity | Network intrusion detection systems | Adversarial machine learning | Evasion attacks

摘要： vital in applications like Network Intrusion Detection | for network* intrusion detection by integrating deep* | Open Set Recognition (OSR) addresses the | challenge of classifying inputs into known and unknown | categories, a crucial task where labeling is often

关键词： Neuro-symbolic integration | Deep embedding for clustering | XGBoost | Open set recognition | Network intrusion detection

摘要： network attack big data, the high dependency on prior | network based on CNN for classifying and detecting | network attacks in network security situation awareness | . A network* feature transfer learning method is* | introduced to solve the detection* and training efficiency*

关键词： CNN | Feature learning | Intrusion detection | Network attack

摘要： cybersecurity, the need for sophisticated intrusion detection | Bayesian network* classifier (EBNC), integrating a semi* | to enhance the accuracy and efficiency of intrusion | detection. The EBNC dynamically constructs class-specific | the EBNC's efficiency and accuracy in intrusion

关键词： Machine learning | Classification | Bayesian network | Intrusion detection

摘要： network intrusion detection models tend to disregard the | -based network* intrusion detection method, which* | intrusion detection models. To address the shortcomings of | the fact that intrusion* detection data has temporal* | With the development of network* devices, the*

关键词： Network traffic | Transformer | Temporal features | Intrusion detection

摘要： timely intrusion* detection is of great importance. To* | object-detection* method based on convolutional neural* | network (CNN), which is called Feature Fusion | Enhancement Neural Network* (FE-Net). It mainly includes* | Detection Module (RDM). FFE aims to enhance the detection

关键词： Railway | Intrusion detection | Convolutional neural network | Anchor matching strategy

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： learning-based method for network intrusion detection in | network intrusion detection technology is reviewed, and | effectiveness of deep learning technology in network intrusion | With the increasing number of network attacks | , the network security of universities has been

关键词： Deep learning | Network intrusion detection | University network | Data preprocessing

关键词： Network intrusion detection | Imbalanced data | Oversampling | GAN

摘要： severe. Intrusion detection, as a key proactive defense | novel intrusion detection method based on VAE-GNN, an | limitations of traditional intrusion detection methods, such | benchmark intrusion detection datasets demonstrate that | neural network methods, showing better detection

关键词： Intrusion detection system | Variational autoencoder | Graph neural network | Network security | Anomaly detection

摘要：Network intrusion detection, a well | this paradigm for network intrusion detection. While | intrusion detection datasets, UNSW-NB15 and 5G-NIDD. For | , remains unclear for effective attack detection. This | experiments are systematically conducted on two network

关键词： Network intrusion detection | Self-Supervised learning | Data augmentation

摘要： learning-based network intrusion detection systems (NIDS | for image classification without considering network | and will fail in the real world as network traffic | behavior of the network traffic features leads to invalid | network traffic flow or produces adversarial samples

关键词： Cybersecurity | Network intrusion detection systems | Adversarial machine learning | Evasion attacks

摘要： vital in applications like Network Intrusion Detection | for network intrusion detection by integrating deep | Open Set Recognition (OSR) addresses the | challenge of classifying inputs into known and unknown | categories, a crucial task where labeling is often

关键词： Neuro-symbolic integration | Deep embedding for clustering | XGBoost | Open set recognition | Network intrusion detection

摘要： network attack big data, the high dependency on prior | network based on CNN for classifying and detecting | network attacks in network security situation awareness | . A network feature transfer learning method is | introduced to solve the detection and training efficiency

关键词： CNN | Feature learning | Intrusion detection | Network attack

摘要： cybersecurity, the need for sophisticated intrusion detection | Bayesian network classifier (EBNC), integrating a semi | to enhance the accuracy and efficiency of intrusion | detection. The EBNC dynamically constructs class-specific | the EBNC's efficiency and accuracy in intrusion

关键词： Machine learning | Classification | Bayesian network | Intrusion detection

摘要： network intrusion detection models tend to disregard the | -based network intrusion detection method, which | intrusion detection models. To address the shortcomings of | the fact that intrusion detection data has temporal | With the development of network devices, the

关键词： Network traffic | Transformer | Temporal features | Intrusion detection

摘要： timely intrusion detection is of great importance. To | object-detection method based on convolutional neural | network (CNN), which is called Feature Fusion | Enhancement Neural Network (FE-Net). It mainly includes | Detection Module (RDM). FFE aims to enhance the detection

关键词： Railway | Intrusion detection | Convolutional neural network | Anchor matching strategy

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： learning-based method for network* intrusion detection in* | network intrusion detection technology is reviewed, and | effectiveness of deep learning technology in network intrusion | With the increasing number of network* attacks* | , the network* security of universities has been*

摘要：Network intrusion detection, a well | this paradigm for network* intrusion detection. While | intrusion detection* datasets, UNSW-NB15 and 5G-NIDD. For | , remains unclear for effective attack detection. This | experiments are systematically conducted on two network

摘要： learning-based network* intrusion detection systems (NIDS* | for image classification without considering network | and will fail in the real world as network* traffic* | behavior of the network* traffic features leads to invalid* | network traffic flow or produces adversarial samples

摘要： vital in applications like Network Intrusion Detection | for network* intrusion detection by integrating deep* | Open Set Recognition (OSR) addresses the | challenge of classifying inputs into known and unknown | categories, a crucial task where labeling is often

摘要： network attack big data, the high dependency on prior | network based on CNN for classifying and detecting | network attacks in network security situation awareness | . A network* feature transfer learning method is* | introduced to solve the detection* and training efficiency*

摘要： cybersecurity, the need for sophisticated intrusion detection | Bayesian network* classifier (EBNC), integrating a semi* | to enhance the accuracy and efficiency of intrusion | detection. The EBNC dynamically constructs class-specific | the EBNC's efficiency and accuracy in intrusion

摘要： network intrusion detection models tend to disregard the | -based network* intrusion detection method, which* | intrusion detection models. To address the shortcomings of | the fact that intrusion* detection data has temporal* | With the development of network* devices, the*

摘要： timely intrusion* detection is of great importance. To* | object-detection* method based on convolutional neural* | network (CNN), which is called Feature Fusion | Enhancement Neural Network* (FE-Net). It mainly includes* | Detection Module (RDM). FFE aims to enhance the detection