A Graph Learning-Based Approach for Lateral Movement Detection
Bibliographic record
Abstract
Lateral movement, a crucial phase in the Advanced Persistent Threat (APT) life cycle, refers to a strategy employed by adversaries to traverse horizontally within a network. The aim is to gain access to various systems or resources, thereby expanding their control and potential access to valuable targets. Detecting these attacks becomes challenging for conventional detection systems due to various factors, including the complexity of pathways, the mimicking of legitimate user behavior by attackers, and limited network visibility. To address these challenges, advanced detection techniques are required to effectively and dynamically analyze multiple features within the interconnected structure of the network. This paper introduces an innovative approach to detect malicious lateral movement paths by leveraging authentication events and graph learning techniques. The proposed method involves constructing a heterogeneous graph, and employing DeepWalk for node embedding. By combining node embedding features with the temporal information of authentication events, feature vectors are generated for each authentication request. These features are then used to train multiple machine learning-based classifiers to detect malicious lateral movement paths. Furthermore, to assess the model’s performance in a more realistic scenario, a series of additional experiments were conducted. These experiments provided further validation of the model’s robustness and its capability for forward prediction.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".