Name
Abstract | ||
|---|---|---|
In the insider threat detection domain, data imbalance is a well-known problem. Most existing solutions, including rebalancing datasets and anomaly detection, have problems such as model overfitting, high cost, and high False Positive Rate (FPR). Therefore, how to effectively detect insider threats on an imbalanced dataset is a challenge. This paper proposes a new Siamese-architecture Insider Threat Detection (SITD) method, which detects insider threat by judging whether the input sample pairs belong to the same category instead of directly classifying a sample while avoiding the abovementioned problems. In addition, we improve the contrastive loss function to make the model pay more attention to the samples pairs of different categories, which significantly enhances the detection performance. Experimental results show that SITD outperforms other insider detection methods on the imbalanced CERT dataset. Moreover, SITD can achieve a good result no matter how imbalanced the dataset is. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/CSCWD54268.2022.9776049 | 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD) |
Keywords | DocType | ISBN |
Insider threat detection,Siamese architecture,Imbalanced dataset,Contrastive loss,Cyber-security | Conference | 978-1-6654-0763-2 |
Citations | PageRank | References |
0 | 0.34 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shaolei Zhou | 1 | 0 | 0.34 |
| Liming Wang | 2 | 0 | 0.34 |
| Jing Yang | 3 | 94 | 27.54 |
| Pengwei Zhan | 4 | 0 | 2.03 |