I tiakina i:
| Ngā kaituhi matua: | , , |
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| Hōputu: | Recurso digital |
| Reo: | |
| I whakaputaina: |
Zenodo
2026
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| Ngā marau: | |
| Urunga tuihono: | https://doi.org/10.5281/zenodo.19303021 |
| Ngā Tūtohu: |
Tāpirihia he Tūtohu
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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| _version_ | 1866901084613640192 |
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| author | DHADI, SAI PRANEETH REDDY M., Jithender Reddy PRASAD, Dr. G N R |
| author_facet | DHADI, SAI PRANEETH REDDY M., Jithender Reddy PRASAD, Dr. G N R |
| contents | <p>The dominant paradigm in cybersecurity continues to privilege infrastructure, protocol integrity, and endpoint resilience, while an increasing fraction of high-impact attacks bypasses these controls by directly manipulating human cognition. This work formalizes such attacks as structured distortions within human digital trust interactions, introducing Cognitive Topological Cybersecurity (CTC) as a rigorous analytical framework. Within this paradigm, interactions are modeled as a high-dimensional manifold whose geometry encodes trust relationships across users, communication channels, and psychological signals. The proposed Cognitive Attack Topology (CAT) framework operationalizes this view through the construction of a Trust Topology Tensor (TTT), enabling the quantification of adversarial influence via Cognitive Distortion Energy (CDE) and its normalized form, the Trust Distortion Index (TDI). A complementary Cognitive Manipulation Score (CMS) captures the composite effect of urgency, fear, authority, and persuasion signals. The framework is instantiated in a multi-layer architecture that integrates transformer- based signal decomposition with dynamic graph modeling and topological anomaly detection. Empirical evaluation is conducted on the GCT-100K dataset, a large-scale benchmark comprising real, public, and synthetically generated cognitive attack interactions. The CAT model achieves a ROC-AUC of 0.9555, exceeding conventional text-based baselines, while maintaining robustness under adversarial cloaking conditions. Notably, performance remains invariant across linguistic boundaries, with a multilingual AUC of 0.984, indicating that topological features of trust manipulation exhibit language-agnostic structure. These results establish that human-targeted cyber-attacks can be detected not as isolated semantic artifacts, but as measurable geometric perturbations in trust space, motivating a shift toward cognition-aware, topology-driven defensive systems.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19303021 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Cognitive Attack Topology (CAT): A Topological Framework for Modeling and Detecting Human-Centric Cyber Attacks DHADI, SAI PRANEETH REDDY M., Jithender Reddy PRASAD, Dr. G N R Cognitive Cybersecurity Cybersecurity Social Engineering Detection Trust Topology Human-Centric Security Graph-based Security AI-Fraud Detection <p>The dominant paradigm in cybersecurity continues to privilege infrastructure, protocol integrity, and endpoint resilience, while an increasing fraction of high-impact attacks bypasses these controls by directly manipulating human cognition. This work formalizes such attacks as structured distortions within human digital trust interactions, introducing Cognitive Topological Cybersecurity (CTC) as a rigorous analytical framework. Within this paradigm, interactions are modeled as a high-dimensional manifold whose geometry encodes trust relationships across users, communication channels, and psychological signals. The proposed Cognitive Attack Topology (CAT) framework operationalizes this view through the construction of a Trust Topology Tensor (TTT), enabling the quantification of adversarial influence via Cognitive Distortion Energy (CDE) and its normalized form, the Trust Distortion Index (TDI). A complementary Cognitive Manipulation Score (CMS) captures the composite effect of urgency, fear, authority, and persuasion signals. The framework is instantiated in a multi-layer architecture that integrates transformer- based signal decomposition with dynamic graph modeling and topological anomaly detection. Empirical evaluation is conducted on the GCT-100K dataset, a large-scale benchmark comprising real, public, and synthetically generated cognitive attack interactions. The CAT model achieves a ROC-AUC of 0.9555, exceeding conventional text-based baselines, while maintaining robustness under adversarial cloaking conditions. Notably, performance remains invariant across linguistic boundaries, with a multilingual AUC of 0.984, indicating that topological features of trust manipulation exhibit language-agnostic structure. These results establish that human-targeted cyber-attacks can be detected not as isolated semantic artifacts, but as measurable geometric perturbations in trust space, motivating a shift toward cognition-aware, topology-driven defensive systems.</p> |
| title | Cognitive Attack Topology (CAT): A Topological Framework for Modeling and Detecting Human-Centric Cyber Attacks |
| topic | Cognitive Cybersecurity Cybersecurity Social Engineering Detection Trust Topology Human-Centric Security Graph-based Security AI-Fraud Detection |
| url | https://doi.org/10.5281/zenodo.19303021 |