S-DAPT-2026: A Stage-Aware Synthetic Dataset for Advanced Persistent Threat Detection

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.

Visualising Evolution History in Multi- and Many-Objective Optimisation

Evolutionary algorithms are widely used to solve optimisation problems. However, challenges of transparency arise in both visualising the processes of an optimiser operating through a problem and understanding the problem features produced from many-objective problems, where comprehending four or more spatial dimensions is difficult. This work considers the visualisation of a population as an optimisation process executes. We have adapted an existing visualisation technique to multi- and many-objective problem data, enabling a user to visualise the EA processes and identify specific problem characteristics and thus providing a greater understanding of the problem landscape. This is particularly valuable if the problem landscape is unknown, contains unknown features or is a many-objective problem. We have shown how using this framework is effective on a suite of multi- and many-objective benchmark test problems, optimising them with NSGA-II and NSGA-III.