基于深度学习的工业物联网智能入侵检测

如何有效识别工业物联网入侵攻击行为是一个新挑战.针对工业物联网中入侵检测特征提取不高、检测效率低、适应能力差等问题，提出一种基于深度学习的工业物联网智能入侵检测方法.首先，在数据处理上改进采样算法用于调节少数类别样本数量，提高检测精度；其次，构建堆叠降噪卷积自编码网络提取关键特征，结合卷积神经网络和降噪自编码器，加强特征识别能力；为了避免信息丢失和信息模糊，改进池化操作以增加其自适应处理能力，并在模型训练过程中采用Adam算法获取最优参数；最后，采用NSL-KDD数据集测试提出方法的性能.实验结果表明，该方法相比现有的RNN、DBN和IDABCNN的准确率分别提高了3.66%、4.93%和4.6%；与未经采样算法的SDCAENN试验对比，U2R和R2L的检测精度分别提高17.57%和3.28%.

IIoT Intelligent Intrusion Detection Based on Deep Learning

How to effectively identify the intrusion attack behavior of the Industrial Internet of Things (IIOT) is a new challenge. Aiming at the problems of low intrusion detection feature extraction, low detection efficiency, and poor adaptability in IIOT, an intelligent intrusion detection method based on deep learning is proposed. First, improve the sampling algorithm in data processing for adjusting the number of samples in a few categories to improve the detection accuracy. Second, build a stacked denoising convolutional self-encoding network to extract key features. Combine the convolutional neural network and the denoising self-encoder to enhance feature recognition ability. In order to avoid information loss and information ambiguity, improve the pooling operation to increase its adaptive processing ability, and use Adam algorithm to obtain the optimal parameters during model training. Finally, use the NSL-KDD dataset to test the performance of the proposed method. Experimental results show that the accuracy of the method is 3.66%, 4.93%, and 0.04% higher than the existing RNN, DBN, and IDMBCNN, respectively. Compared with the SDCAENN test without sampling algorithm, the detection accuracy of U2R and R2L is improved by 17.57 % and 3.28%.