Artificial Intelligence-Based Anomaly Detection for Large-Scale Web Data Security Monitoring
DOI:
https://doi.org/10.13052/jwe1540-9589.2557Keywords:
Artificial intelligence, anomaly detection, web data, network security monitoring, deep learningAbstract
With the rapid development of the World Wide Web and the popularity of Internet applications, the generation and exchange of data have exploded. Large-scale data generation and transmission also bring severe security challenges. In response to the problems that existing anomaly detection methods are difficult to jointly model the semantic context and temporal dependencies in non-encrypted scenarios, and that single-modal feature information is insufficient in encrypted scenarios, resulting in limited detection accuracy, this study proposes two artificial intelligence anomaly detection methods that are adapted to different scenarios. For non-encrypted/low-encrypted scenarios, a BERT-LSTM-TextCNN parallel fusion architecture is proposed. This architecture extracts high-order semantic features, long-term dependency features, and multi-scale local features through parallel branches, and achieves complementary enhancement of multi-perspective information through feature concatenation, effectively solving the problem of difficult collaborative modeling of multiple types of features in non-encrypted scenarios. For multi-encrypted scenarios, a detection method based on improved ResNet and cross-modal feature fusion is proposed. Different from traditional methods that only rely on deep learning features, the study adaptively weights and fuses the deep semantic features extracted by ResNet with flow statistics features and temporal features and optimizes the fusion weights through a learnable random forest, breaking through the bottleneck of insufficient single-modal feature information in encrypted traffic. The precision reached 97.18%, the recall rate reached 95.26%, and the F1-score reached 96.21%. The AUC values were all greater than 0.97, the false positive rate was 8.12% lower than the traditional method, and the single-batch data detection time was only 37.25 s. In the multi-encryption scenario, the precision, recall rate and F1-score of the cross-modal feature fusion method were 98.48%, 87.30% and 92.57%, respectively. This effectively solves the detection limitations caused by feature ambiguity in encrypted environments. In summary, the artificial intelligence anomaly detection method effectively improves detection accuracy and efficiency and provides a feasible technical path for building a comprehensive World Wide Web data security monitoring system.
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