| Title | Anomaly Detection for Scenario-based Insider Activities using CGAN Augmented Data |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Gayathri, R G, Sajjanhar, Atul, Xiang, Yong, Ma, Xingjun |
| Conference Name | 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) |
| Date Published | oct |
| Keywords | Adversarial training, anomaly detection, Benchmark testing, composability, data augmentation, generative adversarial network, generative adversarial networks, Human Behavior, insider threat, Measurement, Metrics, Organizations, policy-based governance, privacy, pubcrawl, security, visualization |
| Abstract | Insider threats are the cyber attacks from the trusted entities within an organization. An insider attack is hard to detect as it may not leave a footprint and potentially cause huge damage to organizations. Anomaly detection is the most common approach for insider threat detection. Lack of real-world data and the skewed class distribution in the datasets makes insider threat analysis an understudied research area. In this paper, we propose a Conditional Generative Adversarial Network (CGAN) to enrich under-represented minority class samples to provide meaningful and diverse data for anomaly detection from the original malicious scenarios. Comprehensive experiments performed on benchmark dataset demonstrates the effectiveness of using CGAN augmented data, and the capability of multi-class anomaly detection for insider activity analysis. Moreover, the method is compared with other existing methods against different parameters and performance metrics. |
| DOI | 10.1109/TrustCom53373.2021.00105 |
| Citation Key | gayathri_anomaly_2021 |
Anomaly Detection for Scenario-based Insider Activities using CGAN Augmented Data