Application of big data analysis in anomaly detection of communication networks

Aiming at the challenges brought by high-dimensional, time-series and heterogeneous data in anomaly detection of communication networks, this study proposes an anomaly detection model based on big data analysis, which integrates adaptive isolation forest (AIF) and attention-based long short-term memory network (Attention-based LSTM). By using weighted principal component analysis (Weighted PCA) to reduce feature dimensionality, combined with the fast screening of AIF and the time-series feature capture of Attention-based LSTM, the model can accurately identify complex anomaly patterns. Experimental results show that the model is significantly superior to traditional methods in terms of precision, recall and F1 score, especially in the high-dynamic scenario of 5G network, the detection rate of anomalies such as DDoS attacks and equipment failures exceeds 95%, showing excellent robustness and real-time performance. The visualization of ROC curve and anomaly score distribution further verifies the detection ability of the model in a low signal-to-noise ratio environment, providing an efficient solution for anomaly detection in communication networks and promoting the development of intelligent network security systems.