@Article{cmc.2026.079941,
AUTHOR = {Marwan Ali Albahar},
TITLE = {A Hybrid Self-Supervised Learning Framework for Advanced Persistent Threat Detection},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/26667},
ISSN = {1546-2226},
ABSTRACT = {Advanced Persistent Threats (APTs) are stealthy cyberattacks that can evade detection in system-level audit logs. Provenance graphs encode these logs as interacting entities and events, exposing a causal and dependency structure that is often obscured in linear representations. Prior provenance-based detectors typically apply anomaly detection over such graphs, yet they frequently incur high false-positive rates and produce coarse grained alerts; moreover, approaches that heavily depend on node-specific identifiers (e.g., file paths) can learn spurious correlations, reducing robustness and limiting reliability across heterogeneous workloads. In this paper, we present Self-Training Adaptive Graph Encoder <sc>(stage)</sc>, a lightweight, self-supervised anomaly detection framework for provenance graphs that (i) trains without attack labels and (ii) enforces leakage-free model selection and thresholding with explicit control over false-alarm rates. STAGE uses learnable degree and node-type embeddings, processed by a compact two-layer Graph Convolutional Networks (GCN) with residual connections and dual pooling. A memory augmented attention module captures global benign prototypes, improving resilience to rare-but-legitimate behaviors, and suppressing false alarms. Training combines contrastive learning over augmented graph views with a one-class Support Vector Data Description (SVDD) objective that learns a compact benign hypersphere in the embedding space. Inference, STAGE fuses neural embeddings with fixed dimensional structural graph statistics and scores them using an ensemble of classical one-class detectors. As a result, STAGE attains strong ranking quality and practical operating points on two benchmarks: the StreamSpot and Wget datasets. In the StreamSpot dataset, STAGE achieves an AUC of 0.998, operating at 95% recall with a 0% false positive rate. On the Wget dataset, it attains an AUC of 0.998 and an average precision of 0.998, achieving 100% recall and 96% precision at a 4% false positive rate. Overall, STAGE demonstrates strong empirical separability for benign-only provenance-based detection and provides an explicit mechanism to trade off recall and false positive rate through predefined thresholding policies.},
DOI = {10.32604/cmc.2026.079941}
}