Name
Title | ||
|---|---|---|
Deep Learning Based Attribute Classification Insider Threat Detection for Data Security |
Abstract | ||
|---|---|---|
With the evolution of network threat, identifying threat from internal is getting more and more difficult. To detect malicious insiders, we move forward a step and propose a novel attribute classification insider threat detection method based on long short term memory recurrent neural networks (LSTM-RNNs). To achieve high detection rate, event aggregator, feature extractor, several attribute classifiers and anomaly calculator are seamlessly integrated into an end-to-end detection framework. Using the CERT insider threat dataset v6.2 and threat detection recall as our performance metric, experimental results validate that the proposed threat detection method greatly outperforms k-Nearest Neighbor, Isolation Forest, Support Vector Machine and Principal Component Analysis based threat detection methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/DSC.2018.00092 | 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC) |
Keywords | Field | DocType |
insider threat,anomaly detection,data security,deep learning,recurrent neural networks | Data mining,Anomaly detection,Data security,Computer science,Support vector machine,Performance metric,Recurrent neural network,Insider threat,Feature extraction,Artificial intelligence,Deep learning | Conference |
ISBN | Citations | PageRank |
978-1-5386-4211-5 | 2 | 0.39 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fanzhi Meng | 1 | 15 | 2.02 |
| Fang Lou | 2 | 17 | 3.07 |
| Yunsheng Fu | 3 | 27 | 2.99 |
| Zhi-Hong Tian | 4 | 312 | 52.75 |