基于AMCNN-LSTM 的电力无线接入专网异常流量检测.

夏炳森, 唐元春, 汪智平 - … of Chongqing University of Posts & …, 2021 - search.ebscohost.com
为了减轻电力无线专网系统因网络业务增多而带来的网络攻击以及异常流量入侵的安全事故
隐患ꎬ 提出了一种基于注意力机制的卷积-长短期记忆网络(convolution-long short-term …

Anomaly detection of terminal access based on LSTM and ResNet

N Li, Y Li, Z Lu, D Li, J Ding - Second International Symposium …, 2023 - spiedigitallibrary.org
Massive wireless debugging terminals and complex and diverse access requirements pose
significant challenges to the secure access of substation terminal equipment. It is crucial to …

Anomaly Traffic Detection Model Based on Hybrid Network

Y Yu, YY Ma - 2023 8th International Conference on Signal and …, 2023 - ieeexplore.ieee.org
With the rapid development of global informatization, the network has given us convenience,
especially promoting the rapid upgrading of the power grid, but at the same time, the power …

[PDF][PDF] 基于网络流量时空特征和自适应加权系数的异常流量检测方法

顾伟, 行鸿彦, 侯天浩 - 电子与信息学报, 2024 - jeit.ac.cn
针对传统异常流量检测模型对流量数据时空特性利用率较低从而导致检测模型性能较差的问题,
该文提出一种基于融合卷积神经网络(CNN), 多头挤压激励机制(MSE) 和双向长短期记忆 …

A Lightweight Bit-Operation Abnormal Traffic Detection Method Based On XNOR-CNN

Y Ge, X Li, B Cai - 2023 IEEE Wireless Communications and …, 2023 - ieeexplore.ieee.org
The rapid development of the Internet and the increasingly complex structure of network
space make the network security situation more and more serious. Abnormal traffic detection …

基于混合卷积神经网络和循环神经网络的入侵检测模型

方圆, 李明, 王萍, 江兴何, 张信明 - 计算机应用, 2018 - joca.cn
针对电力信息网络中的高级持续性威胁问题, 提出一种基于混合卷积神经网络(CNN)
和循环神经网络(RNN) 的入侵检测模型. 该模型根据网络数据流量的统计特征对当前网络状态 …

An Abnormal Network Traffic Detection Method for Steam Turbine Control System based on Long Short-Term Memory

Y Zhang, C Zhao, Y Xie, Z Sang - 2021 China Automation …, 2021 - ieeexplore.ieee.org
The steam turbine control system, as apart of the power control system, is a cyber physical
system which relies on communication technology and control technology to control the …

[引用][C] BLAC: 注意力机制时序网络流量异常检测模型

李婧, 周师严 - 现代电子技术, 2023

[引用][C] 基于深度学习的电力信息网络流量异常检测

杜浩良, 孔飘红, 金学奇, 黄银强 - 浙江电力, 2021

[引用][C] 智能变电站网络异常检测方法的研究与实现

田伟宏 - 2020 - 中国科学院大学(中国科学院沈阳计算 …