Semantic and Graph-Based Unsupervised Learning for Insider Threat Detection Using User Activity Sequences
Bibliographic record
Abstract
Insider threats, where legitimate users misuse their access for malicious purposes, remain challenging to detect due to their contextual and behavioral subtleties. This paper presents a novel machine learning framework that captures user activity sequences through a user-centric representation named the User Daily Activity Sentence (UDAS). Unlike prior work that informally uses daily sequences, we formalize UDAS as a behavioral encoding technique using Word2Vec embeddings and extensively evaluate it across multiple unsupervised anomaly detection methods.To enrich this representation with relational context, we propose a graph-based extension that constructs a user interaction graph based on co-device usage and domain access. A Graph Convolutional Network (GCN) is applied to enhance semantic user embeddings, and anomaly detection is performed using Kmeans clustering.To the best of our knowledge, this is the first work to systematically combine semantic sequence embeddings with graph-based relational learning for insider threat detection. Experiments on the CERT Insider Threat v4.2 dataset show that our method outperforms prior unsupervised models in accuracy and robustness. The proposed framework requires no feature engineering or labeled data, making it applicable to real-world monitoring environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".