NLP and Neural Networks for Insider Threat Detection
Bibliographic record
Abstract
Insider threats in cybersecurity are notoriously difficult to detect due to their covert nature, often evading traditional security measures. In this paper, we propose an unsupervised method for insider threat detection, where we leverage advanced Natural Language Processing (NLP) techniques to enhance the detection of abnormal user activities indicative of insider threats. We represent user behaviors in a vector space using Word2Vec, which in turn are analyzed using state-of-the-art NLP models, including BERT, SciBERT, RoBERTa, GPT-2, and LLaMA. These models are integrated with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks to analyze the temporal behavior of user actions. We evaluate the proposed method using the CMU-CERT dataset version 4.2. Our implemented approaches based on NLP achieve better results than previous state-of-the-art approaches that use traditional unsupervised learning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".