UNSW_NB15_RNN_入侵检测_UNSW-NB15_基于SGM-CNN入侵检测_

# Time-related-Intrusion-Detection-Model-based-on-Recurrent-Neural-Network Here, we use RNN to deal with the network intrusion problem. The UNSW-NB15 dataset is used. Totally, we divide the process into two parts.  The first part is regarded as pre-training. The stacked sparse autoencoder is used for feature extraction and dimension reduction. The structure of SSAE is as follow:  After that, the data can be in low-dimension. Then, we organize the 2D traffic into 3D data, that is, put a few samples together as a time-seires sample. Finally, different variant RNNs are adopted to classify the current data into normal or anomaly. The structure of LSTM is:  The structure of GRU is:  The final result of classification can be shown in this way:  If the method does help for you in your paper, please cite: Lin Y, Wang J, Tu Y, et al. Time-Related Network Intrusion Detection Model: A Deep Learning Method[C]//2019 IEEE Global Communications Conference (GLOBECOM). IEEE, 2019: 1-6. Likewise, if there is any question, contact me heuwangjie@hrbeu.edu.cn. Thanks. 本实验主要完成以下内容： 1.构建一个稀疏自编码器，完成降维任务 2.构建LSTM（GRU、双向LSTM、双向GRU），完成数据特征挖掘 3.最后以一个sigmoid神经元完成二分类任务，以binary_crossentropy作为衡量指标 预期： 1.不同DAE结构，对于分类的影响。 这个过程实际为pre-training过程，以DAE+单步LSTM（GRU及双向）结构完成任务 最终确定所需参数，包括DAE神经元个数以及稀疏系数rho的确定，需要给出一个表table 2.不同LSTM、GRU及双向，共4个的对比 这个过程实际为fine-tuning过程，以不同步数+结构完成任务 最终确定最优的结构，包括选用的cell是什么以及步数的确定，需要给出一个图figure（4条曲线，在不同步数的准确率和虚警率对比） 3.选出最优结构，然后进行test一维图的显示，figure