In today’s interconnected society, cyberattacks have become more frequent and sophisticated, and existing intrusion detection systems may not be adequate in the complex cyberthreat landscape. For instance, existing intrusion detection systems may have overfitting, low classification accuracy, and high false positive rate (FPR) when faced with significantly large volume and variety of network data. An intrusion detection approach based on improved deep belief network (DBN) is proposed in this paper to mitigate the above problems, where the dataset is processed by probabilistic mass function (PMF) encoding and Min-Max normalization method to simplify the data preprocessing. Furthermore, a combined sparsity penalty term based on Kullback-Leibler (KL) divergence and non-mean Gaussian distribution is introduced in the likelihood function of the unsupervised training phase of DBN, and sparse constraints retrieve the sparse distribution of the dataset, thus avoiding the problem of feature homogeneity and overfitting. Finally, simulation experiments are performed on the NSL-KDD and UNSW-NB15 public datasets. The proposed method achieves 96.17% and 86.49% accuracy, respectively. Experimental results show that compared with the state-of-the-art methods, the proposed method achieves significant improvement in classification accuracy and FPR.

在当今互联互通的社会中，网络攻击变得越来越频繁和复杂，并且在复杂的网络威胁环境中，现有的入侵检测系统可能并不足够。例如，当面临大量网络数据时，现有的入侵检测系统可能具有过拟合，低分类精度和较高的误报率（FPR）。为了缓解上述问题，本文提出了一种基于改进的深度信念网络（DBN）的入侵检测方法，该方法采用概率质量函数（PMF）编码和最小-最大归一化方法对数据集进行处理，以简化数据预处理。此外，在DBN无监督训练阶段的似然函数中引入了基于Kullback-Leibler（KL）散度和非均值高斯分布的组合稀疏惩罚项，稀疏约束检索了数据集的稀疏分布，从而避免了特征同质和过度拟合。最后，对NSL-KDD和UNSW-NB15公开数据集进行了模拟实验。所提方法的准确率分别为96.17％和86.49％。实验结果表明，与最新方法相比，该方法在分类准确度和FPR方面有显着提高。对NSL-KDD和UNSW-NB15公开数据集进行了模拟实验。所提方法的准确率分别为96.17％和86.49％。实验结果表明，与最新方法相比，该方法在分类准确度和FPR方面有显着提高。对NSL-KDD和UNSW-NB15公开数据集进行了模拟实验。所提方法的准确率分别为96.17％和86.49％。实验结果表明，与最新方法相比，该方法在分类准确度和FPR方面有显着提高。