用双向LSTM识别网络逃逸流量，准确率达96.1%。
Network evasion detection with Bi-LSTM model
- 采用Bi-LSTM捕捉网络流的前后序列特征
- 在真实数据集上平均准确率达96.1%
- 适合网络安全检测与异常流量分析场景
网络逃逸检测旨在判断网络流量是否来自链路层的逃逸威胁，即通过混淆签名来伪装数据流量。由于以往研究存在诸多缺陷，本文提出一种基于深度学习的架构来解决该问题。从数据帧中提取关键信息作为特征，并专门采用双向长短期记忆网络（Bi-LSTM）以有效追踪网络流的时序特性，编码过去与未来状态。此外，在Bi-LSTM末端引入Softmax分类器，实现类别精准选择。实验结果表明，该方法在网络逃逸检测任务中表现优异，平均准确率达到96.1%。
原文摘要 · Abstract (English)
Network evasion detection aims to distinguish whether the network flow comes from link layer exists network evasion threat, which is a means to disguise the data traffic on detection system by confusing the signature. Since the previous research works has all sorts of frauds, we propose a architecture with deep learning network to handle this problem. In this paper, we extract the critical information as key features from data frame and also specifically propose to use bidirectional long short-term memory (Bi-LSTM) neural network which shows an outstanding performance to trace the serial information, to encode both the past and future trait on the network flows. Furthermore we introduce a classifier named Softmax at the bottom of Bi-LSTM, holding a character to select the correct class. All experiments results shows that we can achieve a significant performance with a deep Bi-LSTM in network evasion detection and it's average accuracy reaches 96.1%.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。