Devjyoti raha (9 risultati)
- Brossura
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 120,02
Spedizione gratuitaSpedito in U.S.A.Quantità: 1 disponibili
Paperback. Condizione: 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.
- Brossura
Da: Books Puddle, New York, NY, U.S.A.Books Puddle
Contatta il venditoreVenditore con 4 stelleCondizione: Nuovo
EUR 134,67
EUR 3,45 spedizioneSpedito in U.S.A.Quantità: 4 disponibili
Condizione: New.
- Brossura
Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 137,23
EUR 11,65 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 1 disponibili
Paperback. Condizione: Brand New. 142 pages. 5.83x0.33x8.27 inches. In Stock.
- Brossura
Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 96,46
EUR 60,99 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. Condizione: 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.
- Brossura
- Print on Demand
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 90,94
EUR 23,00 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Taschenbuch. Condizione: 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.
- Brossura
- Print on Demand
Da: moluna, Greven, Germaniamoluna
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 79,10
EUR 48,99 spedizioneSpedito da Germania a U.S.A.Quantità: Più di 20 disponibili
Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.
- Brossura
- Print on Demand
Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
Contatta il venditoreVenditore con 4 stelleCondizione: Nuovo
EUR 139,19
EUR 7,58 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 4 disponibili
Condizione: New. Print on Demand.
- Brossura
- Print on Demand
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 90,94
EUR 60,00 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. Condizione: 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 indi…vidual 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.
- Brossura
- Print on Demand
Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
Contatta il venditoreVenditore con 4 stelleCondizione: Nuovo
EUR 142,74
EUR 9,95 spedizioneSpedito da Germania a U.S.A.Quantità: 4 disponibili
Condizione: New. PRINT ON DEMAND.
