Research Article

Detecting Insider Threat from Behavioral Logs Based on Ensemble and Self-Supervised Learning

Table 4

The results of the competing experiment and ablation experiment for the proposed method on CERT4.2.

MethodDR (FPR = 0.05)DR (FPR = 0.1)DR (FPR = 0.5)AUC (%)

Vanilla1 + LSTM-Diag [2]91.7% (93.3%)92.5% (91.2%)94.5% (65.4%)93.3
Vanilla1 + DNN-Diag [2]92.1% (93.5%)92.7% (91.3%)94.4% (65.4%)93.6
Vanilla2 + LSTM-CNN [3]92.9% (93.9%)93.8% (91.9%)95.7% (65.7%)94.5
TF-IDF + OB + SS (ours)97.9% (96.4%)98.5% (94.1%)98.9% (66.4%)99.2

Vanilla1 + LR85.8% (90.2%)86.9% (88.4%)92.1% (64.8%)87.6
TF-IDF (+LSTM)88.1% (91.4%)89.5% (89.7%)93.5% (68.0%)89.9
TF-IDF + SS89.4% (92.1%)91.1% (90.5%)92.6% (64.9%)91.9
TF-IDF + Bagging93.8% (94.4%)94.5% (92.2%)95.9% (65.7%)95.4
TF-IDF + Bagging + SS95.1% (95.0%)95.8% (92.8%)96.2% (65.8%)97.1
TF-IDF + OB + SS (ours)97.9% (96.4%)98.5% (94.1%)98.9% (66.4%)99.2

SS: self-supervised; OB: Over-Bootstrap. The values within parentheses are F1 values corresponding to DR and specific FPR.