Research Article

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

Table 5

The results of the competing experiment and ablation experiment for the proposed method on CERT6.2.

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

Vanilla1 + LSTM-Diag[2]84.7% (89.6%)86.1% (88.0%)88.3% (63.8%)90.4
Vanilla1 + DNN-Diag[2]84.5% (89.4%)86.4% (88.2%)88.5% (63.9%)90.8
Vanilla2 + LSTM-CNN[3]88.2% (91.5%)89.9% (89.9%)90.7% (64.5%)92.7
TF-IDF + OB + SS (ours)92.4% (93.7%)93.7% (91.8%)94.8% (65.5%)95.3

Vanilla1 + LR79.4% (86.5%)80.1% (84.8%)83.2% (62.5%)84.2
TF-IDF (+LSTM)84.2% (89.3%)85.1% (87.5%)88.5% (63.9%)88.7
TF-IDF + SS86.5% (90.6%)88.7% (89.3%)90.4% (64.4%)91.5
TF-IDF + Bagging86.7% (90.7%)88.5% (89.2%)89.9% (64.3%)91.1
TF-IDF + Bagging + SS91.8% (93.4%)93.1% (91.5%)94.2% (65.3%)94.4
TF-IDF + OB + SS (ours)92.4% (93.7%)93.7% (91.8%)94.8% (65.5%)95.3

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