Devjyoti raha (9 resultados)
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Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
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EUR 120,54
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: 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.
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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EUR 135,26
Envío por EUR 3,47Se envía dentro de Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New.
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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EUR 137,49
Envío por EUR 11,67Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: Brand New. 142 pages. 5.83x0.33x8.27 inches. In Stock.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 96,46
Envío por EUR 60,99Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: 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.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 90,94
Envío por EUR 23,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Taschenbuch. Condición: 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 i…ndividual 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.
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Librería: moluna, Greven, Alemaniamoluna
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EUR 79,10
Envío por EUR 48,99Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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EUR 139,80
Envío por EUR 7,58Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New. Print on Demand.
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 90,94
Envío por EUR 60,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: 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 indiv…idual 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-Straße 46, 65189 Wiesbaden 144 pp. Englisch.
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 142,74
Envío por EUR 9,95Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New. PRINT ON DEMAND.
