| Title | A Mixed Method For Internal Threat Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Deng, Xiaolei, Zhang, Chunrui, Duan, Yubing, Xie, Jiajun, Deng, Kai |
| Conference Name | 2021 IEEE 5th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) |
| Keywords | Automation, Conferences, Data models, Deep Learning, feature extraction, fully connected neural network, Heuristic algorithms, Internal Threat, Metrics, mixed detection, privacy, pubcrawl, support vector data description, Support vector machines, threat vectors, Variational Auto-Encoders |
| Abstract | In recent years, the development of deep learning has brought new ideas to internal threat detection. In this paper, three common deep learning algorithms for threat detection are optimized and innovated, and feature embedding, drift detection and sample weighting are introduced into FCNN. Adaptive multi-iteration method is introduced into Support Vector Data Description (SVDD). A dynamic threshold adjustment mechanism is introduced in VAE. In threat detection, three methods are used to detect the abnormal behavior of users, and the intersection of output results is taken as the final threat judgment basis. Experiments on cert r6.2 data set show that this method can significantly reduce the false positive rate. |
| DOI | 10.1109/ITNEC52019.2021.9587030 |
| Citation Key | deng_mixed_2021 |
A Mixed Method For Internal Threat Detection