CN109831392A - 半监督网络流量分类方法 - Google Patents
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Cited By (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110691100A (zh) * | 2019-10-28 | 2020-01-14 | 中国科学技术大学 | 基于深度学习的分层网络攻击识别与未知攻击检测方法 |
CN111343147A (zh) * | 2020-02-05 | 2020-06-26 | 北京中科研究院 | 一种基于深度学习的网络攻击检测装置及方法 |
CN111401447A (zh) * | 2020-03-16 | 2020-07-10 | 腾讯云计算(北京)有限责任公司 | 一种基于人工智能的流量作弊识别方法、装置、电子设备 |
CN111585997A (zh) * | 2020-04-27 | 2020-08-25 | 国家计算机网络与信息安全管理中心 | 一种基于少量标注数据的网络流量异常检测方法 |
CN111711633A (zh) * | 2020-06-22 | 2020-09-25 | 中国科学技术大学 | 多阶段融合的加密流量分类方法 |
CN111797935A (zh) * | 2020-07-13 | 2020-10-20 | 扬州大学 | 基于群体智能的半监督深度网络图片分类方法 |
CN111988306A (zh) * | 2020-08-17 | 2020-11-24 | 北京邮电大学 | 基于变分贝叶斯的网内DDoS攻击流量检测方法和系统 |
CN111988237A (zh) * | 2020-07-31 | 2020-11-24 | 中移(杭州)信息技术有限公司 | 流量识别方法、装置、电子设备及存储介质 |
CN113032778A (zh) * | 2021-03-02 | 2021-06-25 | 四川大学 | 一种基于行为特征编码的半监督网络异常行为检测方法 |
WO2022252881A1 (zh) * | 2021-06-03 | 2022-12-08 | 北京有竹居网络技术有限公司 | 图像处理方法、装置、可读介质和电子设备 |
CN116383771A (zh) * | 2023-06-06 | 2023-07-04 | 云南电网有限责任公司信息中心 | 基于变分自编码模型的网络异常入侵检测方法和系统 |
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CN107958216A (zh) * | 2017-11-27 | 2018-04-24 | 沈阳航空航天大学 | 基于半监督的多模态深度学习分类方法 |
CN108881196A (zh) * | 2018-06-07 | 2018-11-23 | 中国民航大学 | 基于深度生成模型的半监督入侵检测方法 |
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CN102611706A (zh) * | 2012-03-21 | 2012-07-25 | 清华大学 | 一种基于半监督学习的网络协议识别方法及系统 |
CN102685016A (zh) * | 2012-06-06 | 2012-09-19 | 济南大学 | 互联网流量区分方法 |
CN104657743A (zh) * | 2015-01-23 | 2015-05-27 | 南京邮电大学 | 一种半监督的最小最大模块化模式分类方法 |
US20170193400A1 (en) * | 2015-12-31 | 2017-07-06 | Kla-Tencor Corporation | Accelerated training of a machine learning based model for semiconductor applications |
US20180007578A1 (en) * | 2016-06-30 | 2018-01-04 | Alcatel-Lucent Usa Inc. | Machine-to-Machine Anomaly Detection |
EP3306890A1 (en) * | 2016-10-06 | 2018-04-11 | Cisco Technology, Inc. | Analyzing encrypted traffic behavior using contextual traffic data |
CN107819698A (zh) * | 2017-11-10 | 2018-03-20 | 北京邮电大学 | 一种基于半监督学习的网络流量分类方法、计算机设备 |
CN107958216A (zh) * | 2017-11-27 | 2018-04-24 | 沈阳航空航天大学 | 基于半监督的多模态深度学习分类方法 |
CN108881196A (zh) * | 2018-06-07 | 2018-11-23 | 中国民航大学 | 基于深度生成模型的半监督入侵检测方法 |
Cited By (16)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110691100A (zh) * | 2019-10-28 | 2020-01-14 | 中国科学技术大学 | 基于深度学习的分层网络攻击识别与未知攻击检测方法 |
CN111343147A (zh) * | 2020-02-05 | 2020-06-26 | 北京中科研究院 | 一种基于深度学习的网络攻击检测装置及方法 |
CN111401447A (zh) * | 2020-03-16 | 2020-07-10 | 腾讯云计算(北京)有限责任公司 | 一种基于人工智能的流量作弊识别方法、装置、电子设备 |
CN111401447B (zh) * | 2020-03-16 | 2023-04-07 | 腾讯云计算(北京)有限责任公司 | 一种基于人工智能的流量作弊识别方法、装置、电子设备 |
CN111585997B (zh) * | 2020-04-27 | 2022-01-14 | 国家计算机网络与信息安全管理中心 | 一种基于少量标注数据的网络流量异常检测方法 |
CN111585997A (zh) * | 2020-04-27 | 2020-08-25 | 国家计算机网络与信息安全管理中心 | 一种基于少量标注数据的网络流量异常检测方法 |
CN111711633A (zh) * | 2020-06-22 | 2020-09-25 | 中国科学技术大学 | 多阶段融合的加密流量分类方法 |
CN111797935A (zh) * | 2020-07-13 | 2020-10-20 | 扬州大学 | 基于群体智能的半监督深度网络图片分类方法 |
CN111797935B (zh) * | 2020-07-13 | 2023-10-31 | 扬州大学 | 基于群体智能的半监督深度网络图片分类方法 |
CN111988237A (zh) * | 2020-07-31 | 2020-11-24 | 中移(杭州)信息技术有限公司 | 流量识别方法、装置、电子设备及存储介质 |
CN111988306B (zh) * | 2020-08-17 | 2021-08-24 | 北京邮电大学 | 基于变分贝叶斯的网内DDoS攻击流量检测方法和系统 |
CN111988306A (zh) * | 2020-08-17 | 2020-11-24 | 北京邮电大学 | 基于变分贝叶斯的网内DDoS攻击流量检测方法和系统 |
CN113032778A (zh) * | 2021-03-02 | 2021-06-25 | 四川大学 | 一种基于行为特征编码的半监督网络异常行为检测方法 |
WO2022252881A1 (zh) * | 2021-06-03 | 2022-12-08 | 北京有竹居网络技术有限公司 | 图像处理方法、装置、可读介质和电子设备 |
CN116383771A (zh) * | 2023-06-06 | 2023-07-04 | 云南电网有限责任公司信息中心 | 基于变分自编码模型的网络异常入侵检测方法和系统 |
CN116383771B (zh) * | 2023-06-06 | 2023-10-27 | 云南电网有限责任公司信息中心 | 基于变分自编码模型的网络异常入侵检测方法和系统 |
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