S-DAPT-2026: A Stage-Aware Synthetic Dataset for Advanced Persistent Threat Detection
This work addresses the problem of limited realistic datasets for APT detection in cybersecurity, providing a scalable tool for researchers and practitioners, though it is incremental as it builds on existing synthetic dataset methods.
The paper tackles the challenge of detecting advanced persistent threats (APTs) by introducing a synthetic dataset that emulates multistage APT campaigns across campus and organizational networks, capturing fourteen distinct alert types, and an efficient alert correlation framework using KNN clustering with cosine similarity.
The detection of advanced persistent threats (APTs) remains a crucial challenge due to their stealthy, multistage nature and the limited availability of realistic, labeled datasets for systematic evaluation. Synthetic dataset generation has emerged as a practical approach for modeling APT campaigns; however, existing methods often rely on computationally expensive alert correlation mechanisms that limit scalability. Motivated by these limitations, this paper presents a near realistic synthetic APT dataset and an efficient alert correlation framework. The proposed approach introduces a machine learning based correlation module that employs K Nearest Neighbors (KNN) clustering with a cosine similarity metric to group semantically related alerts within a temporal context. The dataset emulates multistage APT campaigns across campus and organizational network environments and captures a diverse set of fourteen distinct alert types, exceeding the coverage of commonly used synthetic APT datasets. In addition, explicit APT campaign states and alert to stage mappings are defined to enable flexible integration of new alert types and support stage aware analysis. A comprehensive statistical characterization of the dataset is provided to facilitate reproducibility and support APT stage predictions.