Anomaly-Based Insider Threat Detection Using Deep Autoencoders
Abstract
In recent years, the malicious insider threat has become one of the most significant cyber security threats that an organisation can be subject to. Due to an insider's natural ability to evade deployed information security mechanisms such as firewalls and endpoint protections, the detection of an insider threat can be challenging. Moreover, compared to the volume of audit data that an organization collects for the purpose of intrusion/anomaly detection, the digital footprint left by a malicious insider's action can be minuscule. To detect insider threats from large and complex audit data, in this paper, we propose a detection system that implements anomaly detection using an ensemble of deep autoencoders. Each autoencoder in the ensemble is trained using a certain category of audit data, which represents a user's normal behaviour accurately. The reconstruction error obtained between the original and the decoded data is used to measure whether any behaviour is anomalous or not. After the data has been processed by the individually trained autoencoders and the respective reconstruction errors obtained, a joint decision-making mechanism is used to report a user's overall maliciousness score. Numerical experiments are conducted using a benchmark dataset for insider threat detection. Results indicate that the proposed detection system is able to detect all of the malicious insider actions with a reasonable false positive rate.
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Existing studies that include time elements focus only on one type of time information, either absolute or relative.
“These studies have focused only on one type of time information, either absolute time information or relative time information.”
From BRITD: behavior rhythm insider threat detection with time awareness and user adaptation (2024) · cites this paper as Liu et al. (2018)
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- Authors
- Liu, L., De Vel, O., Chen, C., Zhang, J., & Xiang, Y.
- Affiliations
- Swinburne University of Technology (AU)
- Venue
- 2018 IEEE International Conference on Data Mining Workshops (ICDMW)
- Publisher
- IEEE
- Published
- 2018-11
- Pages
- 39-48
- ISSN
- 2375-9259 (ISSN-L)
- OpenAlex
- W2914623161
- Cited by
- 79
- References
- 23
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 11 · 2023: 15 · 2022: 13 · 2021: 13 · 2020: 7 · 2019: 3
- FWCI
- 4.75
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE ... International Conference on Data Mining workshops (publishedVersion)
- ResearchOnline - JCU (James Cook University) (submittedVersion)
- Figshare (submittedVersion)
- Swinburne figshare (Swinburne University of Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
- Keywords
- insider threat detection, malicious insiders, anomaly detection, deep autoencoder, reconstruction error, audit data, user behavior modeling, false positive rate
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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