Anomaly Detection Model of Network Dataflow Based on an Improved Grey Wolf Algorithm and CNN

With the popularization of the network and the expansion of its application scope, the problem of abnormal network traffic caused by network attacks, malicious software, traffic peaks, or network device failures is becoming increasingly prominent. This problem not only leads to a decline in network performance and service quality but also may pose a serious threat to network security. This paper proposes a hybrid data processing model based on deep learning for network anomaly detection to improve anomaly detection performance. First, the Grey Wolf optimization algorithm is improved to select high-quality data features, which are then converted to RGB images and input into an anomaly detection model. An anomaly detection model of network dataflow based on a convolutional neural network is designed to recognize network anomalies, including DoS (Denial of Service), R2L (Remote to Local), U2R (User to Root), and Probe (Probing). To verify the effectiveness of the improved Grey Wolf algorithm and the anomaly detection model, we conducted experiments on the KDD99 and UNSW-NB15 datasets. The proposed method achieves an average detection rate of 0.986, which is much higher than all the counterparts. Experimental results show that the accuracy and the detection rates of our method were improved, while the false alarm rate has been reduced, proving the effectiveness of our approach in network anomaly classification tasks.