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With the popularity of wireless networks, wireless sensor networks (WSNs) have advanced rapidly, and their flexibility and ease of deployment have resulted in more security concerns, making it critical to research network intrusion prevention for WSNs. Denial of service (DoS) is a common network attack, achieving its goal by bringing down the target network. A DoS attack on WSNs devices with limited resources would be fatal. This paper proposes a method based on principal component analysis (PCA) and a deep convolution neural network (DCNN) for DoS traffic anomaly detection in WSNs, based on the vulnerability of WSNs to attacks and the limited storage space of their devices. Compared with the conventional deep learning structure, the proposed model has a lightweight structure and more effective feature extraction capability, which can effectively detect network abnormal traffic in WSNs devices with limited storage capacity. To assure the effectiveness of the proposed model, receiver operating characteristic (ROC) curves, various classification metrics, and confusion matrices are used to verify the classification results of the model. Through experimental comparison, the proposed model, with small model size, outperforms other mainstream abnormal traffic detection models in terms of classification effect.

Recently, the massive increase in network users has dramatically increased network traffic, making it more difficult to maintain network security. The task of network security situation element extraction is to detect and classify network traffic. The detection rate of minority class samples is low in existing network traffic feature extraction classification methods, and most of the network threat data have seen extreme sample imbalance, which further affects the detection accuracy of minority class samples. To solve these problems, this paper proposes a network security situation element extraction method using conditional generative adversarial network (CGAN) and Transformer. Here, CGAN is applied to solve the sample imbalance problem in the data and improve the detection accuracy of minority samples. Transformer, as an effective feature learning method in natural language direction, has excellent long-distance feature extraction ability. By combining CGAN with Transformer, the detection accuracy of network traffic can be effectively improved. Also, validation was performed using the UNSW-NB15 and KDDcup99 datasets. Experimental results demonstrate that the method using a combination of CGAN and Transformer improved the detection rate for minority samples compared with other advanced-feature extraction classification methods, thereby improving the overall accuracy, F1-score, and specificity. The results are 89.

Traditional networks rely heavily on the distribution of expert experience when assessing complex network security situations, resulting in low assessment accuracy, which has been unable to adapt to the current network security needs of the big data era, and has unavoidable problems such as low efficiency and poor flexibility. In response to these problems, this paper proposes a network security situation assessment method based on D-S evidence theory to optimize neural networks. First establish the CS-BP neural network model, enhance the local search ability of the cuckoo algorithm through conjugate gradient calculation, and then introduce it into the BP neural network to improve the training convergence speed and overcome the local minimum problem; finally, in order to reduce the basic probability distribution (BPA) subjective impact, using DS evidence theory to optimize the CS-BP neural network, determine the degree of impact of each attack, and evaluate the value of the network security situation. The experimental results show that the network situation assessment model of CS-BP neural network optimized based on D-S evidence theory can effectively assess the network security situation in the environment of trusted equipment.

No abstract