Insider threat detection based on operational attention and data augmentation - Guanyun FENG; Cai FU; Jianqiang LYU; Lansheng HAN (网络与信息安全学报)
In recent years, there has been an increased focus on the issue of insider threats.Insider threats are a major cause security breaches in organizations and pose an ongoing challenge.By analyzing the existing insider threat data, it was identified that the biggest challenge in insider threat detection lies in data imbalance and the limited number of labeled threat samples.In the Cert R4.2 dataset, which is a classic dataset for insider threat detection, there are over 3.22 million log data, but only 7,423 are marked as malicious operation logs.Furthermore, most of the operation types in the logs are not related to malicious behavior, and only two types of operations are highly correlated with malicious behavior, such as leaking company data, creating interference in the detection process.To address this challenge, a data processing framework was designed based on operational attention and data augmentation.Anomaly evaluation was first performed on operations by the framework, and operations with low anomaly scores were then masked.This makes the model better focus on operations related to malicious behavior, which can be considered as a hard attention mechanism for operations.Next, the characteristics of the insider threat dataset were analyzed, and three rules were designed for data augmentation on malicious samples to increase the diversity of samples and alleviate the substantial imbalance between positive and negative samples.Supervised insider threat detection was regarded as a time-series classification problem.Residual connections were added to the LSTM-FCN model to achieve multi-granularity detection, and indicators such as precision rate and recall rate were used to evaluate the model.The results indicate superior performance over existing baseline models.Moreover, the data processing framework was implemented on various classic models, such as ITD-Bert and TextCNN, and the results show that the methods effectively improve the performance of insider threat detection models.