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学者姓名：王一鹏
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Abstract ：
由于网络安全领域课程—网络安全与防护课程本身理论性较强，同时教学过程中缺乏对学生学习结果的有效评价方式，实现网络安全与防护课程教学目标存在一定的难度。针对网络安全与防护课程教学建设问题，本文结合线上线下多种形式，提出了新的教学设计思路，并在其中融入了教学思政元素，阐述了课程混合式教学建设过程中的课程目标、教学内容、评价方法等方面的设计。通过对课程实践结果和教学数据的分析，网络安全与防护课程混合式教学设计取得了较好的教学效果。
Keyword ：
混合式教学 混合式教学 教学实践 教学实践 网络安全与防护 网络安全与防护 教学设计 教学设计
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| GB/T 7714 | 于海阳 , 杨震 , 赖英旭 et al. 网络安全与防护课程教学设计探索 [J]. | 中国多媒体与网络教学学报(上旬刊) , 2023 , (08) : 77-80 . |
| MLA | 于海阳 et al. "网络安全与防护课程教学设计探索" . | 中国多媒体与网络教学学报(上旬刊) 08 (2023) : 77-80 . |
| APA | 于海阳 , 杨震 , 赖英旭 , 刘静 , 王一鹏 . 网络安全与防护课程教学设计探索 . | 中国多媒体与网络教学学报(上旬刊) , 2023 , (08) , 77-80 . |
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Abstract ：
Network traffic classification plays a vital role in many fields such as intrusion detection, network management, and network security. As the proportion of IoT device traffic increases, many approaches to identifying IoT device types through traffic have emerged. Specifically, Deep Learning (DL) has been proven to be a more efficient approach for encrypted traffic identification than other traditional methods. However, most existing classification models are created in static datasets from the closed world, so they can only classify within a limited domain. In this case, interfering traffic in the open world is easily misidentified by classifiers as IoT device traffic. An autonomous framework is proposed to tackle this issue, effectively identifying the device type according to the grayscale graph generated by packet payload and automatically updating to adapt to the unknown environment in the open world. The core of the proposed framework consists of a packet graph-vector transformer, a CNN-based classifier, and an autonomous optimizer. The optimizer can filter interfering data and optimize the model by updating the training dataset. We comprehensively evaluated the proposed framework on two datasets, one taken from the UNSW IoT traces and the other collected by our experiments, containing traffic generated from two devices and three open-world scenarios. The results demonstrate that the proposed framework can update the training dataset by unsupervised filtering interference packets, enabling the model to automatically suit complex environments for accurate and robust IoT device type identification in the open world.
Keyword ：
Smart Home Smart Home IoT IoT Traffic Classifier Traffic Classifier Autonomous Update Autonomous Update Deep Learning Deep Learning CNN CNN
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| GB/T 7714 | Liu, Shuhe , Xu, Xiaolin , Zhang, Yongzheng et al. Autonomous Anti-interference Identification of IoT Device Traffic based on Convolutional Neural Network [J]. | 2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) , 2022 . |
| MLA | Liu, Shuhe et al. "Autonomous Anti-interference Identification of IoT Device Traffic based on Convolutional Neural Network" . | 2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) (2022) . |
| APA | Liu, Shuhe , Xu, Xiaolin , Zhang, Yongzheng , Wang, Yipeng . Autonomous Anti-interference Identification of IoT Device Traffic based on Convolutional Neural Network . | 2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) , 2022 . |
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Abstract ：
With the rapid development and wide application of Internet of Things (IoT) technology, Internet Service Providers need to accurately classify IoT traffic to provide hierarchical network management and network protection for highly heterogeneous IoT devices. Currently, popular traditional machine learning and deep learning-based approaches to IoT traffic classification require large amounts of labeled traffic to build classification models. However, in practice simple IoT traffic with simple operating modes can be identified with only a small amount of labeled traffic and some classes of IoT devices only generate a limited amount of traffic, therefore, the aforementioned methods is not applicable in such scenarios. In this paper, we propose FITIC, a novel IoT traffic classification method based on few-shot learning. FITIC proposes a feature construction method for IoT traffic characteristics and can classify IoT traffic with only a limited number of labeled traffic samples. We evaluate FITIC on two publicly available datasets, and the experimental results show that FITIC has excellent classification accuracy and outperforms the state-of-the-art traffic classification methods.
Keyword ：
Network Traffic Classification Network Traffic Classification Internet of Things Internet of Things Few-shot Learning Few-shot Learning
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| GB/T 7714 | Jia, Wenxu , Wang, Yipeng , Lai, Yingxu et al. FITIC: A Few-shot Learning Based IoT Traffic Classification Method [J]. | 2022 31ST INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATIONS AND NETWORKS (ICCCN 2022) , 2022 . |
| MLA | Jia, Wenxu et al. "FITIC: A Few-shot Learning Based IoT Traffic Classification Method" . | 2022 31ST INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATIONS AND NETWORKS (ICCCN 2022) (2022) . |
| APA | Jia, Wenxu , Wang, Yipeng , Lai, Yingxu , He, Huijie , Yin, Ruiping . FITIC: A Few-shot Learning Based IoT Traffic Classification Method . | 2022 31ST INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATIONS AND NETWORKS (ICCCN 2022) , 2022 . |
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Abstract ：
本发明公开了一种基于时序负载流量指纹的物联网设备流量识别方法及系统，本方法的具体工作流程可以分为训练阶段和分类阶段。在训练阶段，根据已标记类别的物联网设备流量的报文长度序列信息和报文字节序列信息，训练神经网络中的可学习参数，从而实现自动化的物联网设备流量指纹提取和物联网设备识别。在分类阶段，基于已训练完成的神经网络模型，对待识别物联网设备流量进行物联网设备流量指纹构建，并完成不同物联网设备的流量识别。本发明从不同的特征维度对于任何物联网设备产生的网络流量进行准确刻画，从而形成更具表达能力的物联网设备流量指纹，在物联网设备流量识别过程中具有高准确率、高度泛化能力和鲁棒性。
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| GB/T 7714 | 王一鹏 , 贾文旭 , 赖英旭 et al. 一种基于时序负载流量指纹的物联网设备识别方法及系统 : CN202110557882.1[P]. | 2021-05-21 . |
| MLA | 王一鹏 et al. "一种基于时序负载流量指纹的物联网设备识别方法及系统" : CN202110557882.1. | 2021-05-21 . |
| APA | 王一鹏 , 贾文旭 , 赖英旭 , 杨震 . 一种基于时序负载流量指纹的物联网设备识别方法及系统 : CN202110557882.1. | 2021-05-21 . |
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