APT-CGLP: Cross-Modal Security & Control Frameworks

• APT-CGLP is a dual-framework approach that aligns structured system data with unstructured narratives in cybersecurity and tunes reset controllers in mechatronics.
• In cybersecurity, it employs LLM-driven data synthesis, contrastive learning, and masked modeling for improved threat detection and scalable, automated analysis.
• In control, APT-CgLp uses higher-order sinusoidal input describing functions to balance tracking and noise rejection, optimizing controller performance.

APT-CGLP designates two distinct methodologies in advanced technical disciplines: (1) a framework for cross-modal advanced persistent threat (APT) hunting via contrastive graph-language pre-training in cyber security, and (2) a tuning protocol for Constant-in-gain Lead-in-phase (CgLp) reset controllers in high-precision mechatronics. Both share attributes of bridging structural gaps and optimizing performance through hybrid data-driven/model-based approaches, but are applied in fundamentally different contexts—information security (APT-CGLP, (Qiu et al., 25 Nov 2025)) and nonlinear control (APT-CgLp, (Hou et al., 2020)).

1. Advanced Persistent Threat Hunting via Contrastive Graph-Language Pre-Training

APT-CGLP in the security domain refers to an end-to-end, fully automated system for provenance-based APT detection that leverages contrastive graph-language pre-training to bridge the structural and semantic modality gaps between provenance graphs (derived from system audit logs) and unstructured cyber threat intelligence (CTI) text (Qiu et al., 25 Nov 2025). Traditional threat hunting pipelines rely on extracting attack graphs from CTI and matching them to observed system behaviors, but suffer from information loss and scalability bottlenecks due to brittle NLP extraction and manual curation.

The APT-CGLP system is organized around four architectural pillars:

• LLM-driven Data Synthesis (Graph2CTI): Benign audit-derived subgraphs are sampled and translated into CTI-style narratives via in-context LLM prompting, generating high-fidelity cross-modal supervision pairs.
• CTI Denoising: Real-world CTI reports, often containing noise and irrelevant metadata, are distilled using a chain-of-thought LLM process into concise, temporally- and causally-structured summaries suitable for downstream modeling.
• Cross-modal Pre-training: Graph and text encoders (2-layer GIN and BERT-Base, respectively) are jointly optimized with a multimodal transformer, employing a weighted sum of contrastive and masked modeling objectives to achieve both global semantic and fine-grained alignment between provenance graphs and CTI descriptions.
• Threat Hunting Module: Live provenance subgraphs are first coarsely retrieved against a CTI embedding database (via cosine similarity), then subjected to fine-grained semantic matching using the multimodal encoder to compute a match probability.

2. Addressing the Modality Gap: Structural and Semantic Bridging

The principal design challenge in provenance-based threat hunting is the large modality gap: provenance graphs contain fine-grained system-level interactions (e.g., process→file), while CTI is a high-level, often prose-based description of attacker TTPs. Off-the-shelf multimodal techniques (e.g., CLIP) fail because neither embedding structures nor semantic granularity align directly.

In APT-CGLP, this gap is mitigated by:

• Synthesizing realistic paired (G, T) examples to augment limited labeled datasets.
• Using contrastive learning to enforce proximity in latent space, such that semantically aligned (graph, CTI) pairs are mapped closely.
• Employing masked modeling at both graph (masked node prediction) and language (masked token prediction) levels, with cross-attention aligning fine-grained entities and actions across modalities.

Such an approach permits the end-to-end alignment of low-level system activity with high-level exploits, yielding operational threat hunting capabilities without manual intervention.

3. Multi-Objective Training and Alignment Mechanisms

APT-CGLP’s training algorithm integrates four core losses to achieve joint optimization:

Loss	Targeted Alignment	Mechanism
L_gtc (GTC)	Global (Graph↔Text)	Batchwise contrastive (NT-Xent) loss
L_gtm (GTM)	Joint Graph-Text Matching	BCE on multimodal embedding
L_mlm (MLM)	Local (Text, conditioned on Graph)	Masked LM, predict masked tokens
L_mgm (MGM)	Local (Graph, conditioned on Text)	Masked node, predict node embedding

The combined objective is:

$$L = \alpha \cdot L_{gtc} + (1-\alpha) \cdot (L_{gtm} + L_{mlm} + L_{mgm})$$

where $\alpha$ trades off between global contrastive and local masked objectives (empirically, $\alpha=0.7$). This synergistic formulation simultaneously pushes global (G,T) pairs to the same region of latent space and refines token/node-level cross-modal attention.

Two-stage retrieval further enhances scalability: initial embedding-based filtering followed by intensive fine matching preserves both efficiency and accuracy with manageable resource usage.

4. Experimental Results and Evaluation

The APT-CGLP methodology was evaluated on four real-world datasets with highly imbalanced threat prevalence (1.2% malicious subgraphs). Comparative analysis against both automated and human-in-the-loop baselines demonstrated:

• Superior fully automated accuracy: E3-Cadets F1=0.963, perfect recall, FPR=0.019. Across E3-Theia, Trace, and OpTC sets, F1 ranges 0.889–0.933.
• Incremental value of components: Absence of Graph2CTI pairs (>10% F1 drop), masked graph modeling (5–10% lower F1), or CTI denoising (>30% recall drop) each substantially degrade performance.
• Efficiency: GPU inference latency for top-10 retrieval is ~0.4 s/query; memory footprint is 1 GB, scalable to batch operation in real-time environments.

Ablative and comparative studies substantiate that both global contrastive and local masked objectives, as well as LLM-powered data synthesis, are critical for high-precision performance (Qiu et al., 25 Nov 2025).

5. Limitations and Prospective Extensions

Identified limitations include susceptibility to LLM hallucination in both synthetic data and CTI denoising, the necessity for trusted audit logs (excluding certain tampering/C2 scenarios), and possible misclassification due to ambiguous CTI phrasing. The approach currently presumes periodic offline pre-training to cope with concept drift in operational settings.

Potential future directions encompass:

• Cross-domain adaptation to ICS/SCADA, mobile, and cloud environments.
• Cross-lingual extension by adapting LLMs to non-English CTIs.
• Adversarial/robustness training to resist poisoning or evasion.
• Real-time incremental learning to continuously adapt to incoming CTI data.

A plausible implication is that similar cross-modal frameworks could generalize to other high-value domains involving structured graph phenomena and unstructured domain-specific narratives.

6. APT-CgLp: Analogous Methodology in Nonlinear Control

APT-CgLp (“Adaptive/Phase Tuning for CgLp”) is a nonlinear control design and tuning methodology centered on Constant-in-gain Lead-in-phase (CgLp) reset compensators, leveraging higher-order sinusoidal input describing functions (HOSIDF) for explicit trade-off management between tracking and noise injection (Hou et al., 2020). Here, the analogy to the security context lies in combing multiple levels of harmonic (modal) analysis for practical system optimization.

Key elements include:

• Parametric controller construction using hybrid reset/lead stages (GFORE/GSORE + linear lead/second-order lead).
• Use of the HOSIDF to quantify high-order harmonic generation due to reset nonlinearity, specifically focusing on the 3rd-harmonic peak $(M_p, \omega_p)$ as a proxy for tracking/noise performance trade-off.
• Systematic candidate selection: generate $(\gamma, b)$ pairs from the classical describing function to satisfy phase margin at $\omega_c$, then evaluate the trade-off via $M_p$ (amplitude at 3rd-harmonic peak) and $\omega_p$ (its frequency).
• Implementation guidance for digital control (Tustin discretization) and closed-loop pre-filtering for zero steady-state error.

Simulation and experimental validation underpin the methodology, with $S_\infty(\omega)$ and actual RMSE/peak errors demonstrating that maximizing $\omega_p$ aligns with optimal tracking and minimizing $M_p$ aligns with best noise rejection (Hou et al., 2020).

7. Comparative Perspective

Both instantiations of APT-CGLP exemplify systematic frameworks for bridging representational or behavioral incongruities: in security, cross-modal semantic alignment between graph-structured audit data and natural language CTI; in control, frequency-domain tuning balancing linear and nonlinear responses via harmonic proxy measures.

Their shared traits include:

• Data-driven (often LLM-empowered) synthesis or denoising to enhance supervision.
• Multilevel alignment: coarse-grained (global/task-level) and fine-grained (entity/harmonic-level).
• Multi-objective optimization to navigate operational trade-offs.

The main distinction is that, in APT-CGLP for security, contrastive/masked pre-training aligns multimodal data for semantic retrieval and classification; whereas, in APT-CgLp for control, modal analysis via HOSIDF guides controller synthesis and deployment in deterministic systems.

The emergence of such cross-modal and higher-order harmonic methodologies underscores a broader trend toward hybrid, multi-domain optimization tools grounded in rigorous, interpretable design and robust to real-world operational requirements (Hou et al., 2020, Qiu et al., 25 Nov 2025).