Use of GNN to automate SSDLC Alert Dismissal Review Process: Study on the Usage & Efficacy of Novel Deep Learning Techniques in Appsec - Couverture souple

Name: Use of GNN to automate SSDLC Alert Dismissal Review Process: Study on the Usage & Efficacy of Novel Deep Learning Techniques in Appsec
Author: Raha, Devjyoti
ISBN: 9783658514952

Raha, Devjyoti

9783658514952: Use of GNN to automate SSDLC Alert Dismissal Review Process: Study on the Usage & Efficacy of Novel Deep Learning Techniques in Appsec

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ISBN 10 : 3658514957 ISBN 13 : 9783658514952

Editeur : Springer Vieweg, 2026

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The work grew out of a very practical problem: the AppSec team was drowning in security scanning alerts but still occasionally missed real issues that had been dismissed as false positives. Wanted to present a way not just to tune individual tools, but to look across CodeQL, OWASP ZAP, GHAS secret scanning, and other scanners and understand where the triage process itself was failing. That led to the idea of treating the entire alert history as a graph, where alerts, code files, services, dependencies, users, and incidents are all connected nodes linked by data flows, temporal relationships, and shared context. From there, the team designed a JSON schema to normalize alerts from different tools, built a heterogeneous graph on top of that data, and implemented a graph neural network to learn patterns that distinguish correctly closed alerts from those that later turned out to be genuine issues.

It all started with synthetic and pilot datasets to prove feasibility, wiring up a small GCN/GAT-based model that could ingest these graphs and output a “retriage probability” for each closed alert, then iterated on node features and relationships until the model consistently identified historically missed alerts with high precision. Alongside the model, continued developing scripts and pipelines to generate training data, evaluate confusion matrices, and visualize results so that AppSec engineers could see not just scores but concrete examples of alerts being flagged for a second look. As results stabilized—showing strong precision and recall on retrospective tests—the focus shifted to integration: embedding this GNN step into CI/CD and SIEM workflows so that closed alerts could be continuously re-scored, and high‑risk ones automatically routed back to the security team for triage, with analyst feedback feeding into the next training cycle.

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� propos de l'auteur

Devjyoti Raha is working as an application architect with a strong enthusiasm in promoting automation leveraging AI.

Les informations fournies dans la section � A propos du livre � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteur: Springer Vieweg
Date d'�dition: 2026
Langue: anglais
ISBN 10: 3658514957
ISBN 13: 9783658514952
Reliure: Broch�
Nombre de pages: 144
Coordonn�es du fabricant: Springer Nature Customer Service Center GmbH
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Use of GNN to automate SSDLC Alert Dismissal Review Process

Devjyoti Raha

Edit� par Springer, Berlin, Springer Vieweg Jan 2027, 2027

ISBN 10 : 3658514957 ISBN 13 : 9783658514952

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The work grew out of a very practical problem: the AppSec team was drowning in security scanning alerts but still occasionally missed real issues that had been dismissed as false positives. Wanted to present a way not just to tune individual tools, but to look across CodeQL, OWASP ZAP, GHAS secret scanning, and other scanners and understand where the triage process itself was failing. That led to the idea of treating the entire alert history as a graph, where alerts, code files, services, dependencies, users, and incidents are all connected nodes linked by data flows, temporal relationships, and shared context. From there, the team designed a JSON schema to normalize alerts from different tools, built a heterogeneous graph on top of that data, and implemented a graph neural network to learn patterns that distinguish correctly closed alerts from those that later turned out to be genuine issues.It all started with synthetic and pilot datasets to prove feasibility, wiring up a small GCN/GAT-based model that could ingest these graphs and output a retriage probability for each closed alert, then iterated on node features and relationships until the model consistently identified historically missed alerts with high precision. Alongside the model, continued developing scripts and pipelines to generate training data, evaluate confusion matrices, and visualize results so that AppSec engineers could see not just scores but concrete examples of alerts being flagged for a second look. As results stabilized showing strong precision and recall on retrospective tests the focus shifted to integration: embedding this GNN step into CI/CD and SIEM workflows so that closed alerts could be continuously re-scored, and high risk ones automatically routed back to the security team for triage, with analyst feedback feeding into the next training cycle. 123 pp. Englisch. N� de r�f. du vendeur 9783658514952

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Paperback. Etat : new. Paperback. The work grew out of a very practical problem: the AppSec team was drowning in security scanning alerts but still occasionally missed real issues that had been dismissed as false positives. Wanted to present a way not just to tune individual tools, but to look across CodeQL, OWASP ZAP, GHAS secret scanning, and other scanners and understand where the triage process itself was failing. That led to the idea of treating the entire alert history as a graph, where alerts, code files, services, dependencies, users, and incidents are all connected nodes linked by data flows, temporal relationships, and shared context. From there, the team designed a JSON schema to normalize alerts from different tools, built a heterogeneous graph on top of that data, and implemented a graph neural network to learn patterns that distinguish correctly closed alerts from those that later turned out to be genuine issues.It all started with synthetic and pilot datasets to prove feasibility, wiring up a small GCN/GAT-based model that could ingest these graphs and output a retriage probability for each closed alert, then iterated on node features and relationships until the model consistently identified historically missed alerts with high precision. Alongside the model, continued developing scripts and pipelines to generate training data, evaluate confusion matrices, and visualize results so that AppSec engineers could see not just scores but concrete examples of alerts being flagged for a second look. As results stabilizedshowing strong precision and recall on retrospective teststhe focus shifted to integration: embedding this GNN step into CI/CD and SIEM workflows so that closed alerts could be continuously re-scored, and highrisk ones automatically routed back to the security team for triage, with analyst feedback feeding into the next training cycle. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N� de r�f. du vendeur 9783658514952

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Use of GNN to automate SSDLC Alert Dismissal Review Process

Raha, Devjyoti

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Use of GNN to automate SSDLC Alert Dismissal Review Process : Study on the Usage & Efficacy of Novel Deep Learning Techniques in Appsec

Devjyoti Raha

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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - The work grew out of a very practical problem: the AppSec team was drowning in security scanning alerts but still occasionally missed real issues that had been dismissed as false positives. Wanted to present a way not just to tune individual tools, but to look across CodeQL, OWASP ZAP, GHAS secret scanning, and other scanners and understand where the triage process itself was failing. That led to the idea of treating the entire alert history as a graph, where alerts, code files, services, dependencies, users, and incidents are all connected nodes linked by data flows, temporal relationships, and shared context. From there, the team designed a JSON schema to normalize alerts from different tools, built a heterogeneous graph on top of that data, and implemented a graph neural network to learn patterns that distinguish correctly closed alerts from those that later turned out to be genuine issues.It all started with synthetic and pilot datasets to prove feasibility, wiring up a small GCN/GAT-based model that could ingest these graphs and output a retriage probability for each closed alert, then iterated on node features and relationships until the model consistently identified historically missed alerts with high precision. Alongside the model, continued developing scripts and pipelines to generate training data, evaluate confusion matrices, and visualize results so that AppSec engineers could see not just scores but concrete examples of alerts being flagged for a second look. As results stabilized showing strong precision and recall on retrospective tests the focus shifted to integration: embedding this GNN step into CI/CD and SIEM workflows so that closed alerts could be continuously re-scored, and high risk ones automatically routed back to the security team for triage, with analyst feedback feeding into the next training cycle. N� de r�f. du vendeur 9783658514952

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Use of GNN to automate SSDLC Alert Dismissal Review Process: Study on the Usage & Efficacy of Novel Deep Learning Techniques in Appsec

Raha, Devjyoti

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Use of GNN to automate SSDLC Alert Dismissal Review Process

Devjyoti Raha

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -The work grew out of a very practical problem: the AppSec team was drowning in security scanning alerts but still occasionally missed real issues that had been dismissed as false positives. Wanted to present a way not just to tune individual tools, but to look across CodeQL, OWASP ZAP, GHAS secret scanning, and other scanners and understand where the triage process itself was failing. That led to the idea of treating the entire alert history as a graph, where alerts, code files, services, dependencies, users, and incidents are all connected nodes linked by data flows, temporal relationships, and shared context. From there, the team designed a JSON schema to normalize alerts from different tools, built a heterogeneous graph on top of that data, and implemented a graph neural network to learn patterns that distinguish correctly closed alerts from those that later turned out to be genuine issues. It all started with synthetic and pilot datasets to prove feasibility, wiring up a small GCN/GAT-based model that could ingest these graphs and output a retriage probability for each closed alert, then iterated on node features and relationships until the model consistently identified historically missed alerts with high precision. Alongside the model, continued developing scripts and pipelines to generate training data, evaluate confusion matrices, and visualize results so that AppSec engineers could see not just scores but concrete examples of alerts being flagged for a second look. As results stabilizedshowing strong precision and recall on retrospective teststhe focus shifted to integration: embedding this GNN step into CI/CD and SIEM workflows so that closed alerts could be continuously re-scored, and highrisk ones automatically routed back to the security team for triage, with analyst feedback feeding into the next training cycle.Springer Vieweg in Springer Science + Business Media, Abraham-Lincoln-Stra�e 46, 65189 Wiesbaden 144 pp. Englisch. N� de r�f. du vendeur 9783658514952

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