Insider threat represents a major cybersecurity challenge to companies, organizations, and government agencies. Insider threat detection involves many challenges, including unbalanced data, limited ground truth, and possible user behavior changes. This research presents an unsupervised learning based anomaly detection approach for insider threat detection. We employ four unsupervised learning methods with different working principles, and explore various representations of data with temporal information. Furthermore, different computational intelligence schemes are explored to combine these models to create anomaly detection ensembles for improving the detection performance. Evaluation results show that the approach allows learning from unlabelled data under challenging conditions for insider threat detection. Insider threats are detected with high detection and low false positive rates. For example, 60% of malicious insiders are detected under 0.1% investigation budget, and all malicious insiders are detected at less than 5% investigation budget. Furthermore, we explore the ability of the proposed approach to generalize for detecting new anomalous behaviors in different datasets, i.e., robustness. Finally, results demonstrate that a voting-based ensemble of anomaly detection can be used to improve detection performance as well as the robustness. Comparisons with the state-of-the-art confirm the effectiveness of the proposed approach.

中文翻译：

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使用无监督集成对内部威胁进行异常检测

内部威胁是公司、组织和政府机构面临的主要网络安全挑战。内部威胁检测涉及许多挑战，包括不平衡的数据、有限的真实情况以及可能的用户行为变化。本研究提出了一种基于无监督学习的异常检测方法，用于内部威胁检测。我们采用四种具有不同工作原理的无监督学习方法，并探索具有时间信息的数据的各种表示。此外，探索了不同的计算智能方案以组合这些模型以创建异常检测集合以提高检测性能。评估结果表明，该方法允许在内部威胁检测具有挑战性的条件下从未标记的数据中学习。以高检测率和低误报率检测到内部威胁。例如，60% 的恶意内部人员在 0.1% 的调查预算下被检测到，所有恶意内部人员都在低于 5% 的调查预算内被检测到。此外，我们探索了所提出的方法在不同数据集中检测新异常行为的泛化能力，即鲁棒性。最后，结果表明，基于投票的异常检测集成可用于提高检测性能和鲁棒性。与最先进技术的比较证实了所提出方法的有效性。我们探索了所提出的方法在不同数据集中检测新异常行为的泛化能力，即鲁棒性。最后，结果表明，基于投票的异常检测集成可用于提高检测性能和鲁棒性。与最先进技术的比较证实了所提出方法的有效性。我们探索了所提出的方法在不同数据集中检测新异常行为的泛化能力，即鲁棒性。最后，结果表明，基于投票的异常检测集成可用于提高检测性能和鲁棒性。与最先进技术的比较证实了所提出方法的有效性。