saswati chatterjee (11 resultados)

Paperback. Condición: Brand New. 74 pages. 6.00x0.17x9.00 inches. In Stock.

Paperback. Condición: new. Paperback. Machine Learning Approaches for DDoS Detection and Network Forensics An Investigative Framework Using KNN, SVM, and Bayesian Models on Benchmark Datasets In an era where cyber threats grow more sophisticated by the day, Distributed Denial-of-Service (DDoS) attacks have emerged as one of the most severe and disruptive forms of intrusion. This book presents a practical and research-driven guide to detecting and analyzing DDoS attacks using advanced machine learning techniques. Drawing on benchmark datasets like KDD Cup 99 and NSL-KDD, the authors introduce a robust framework for network forensic investigation, combining K-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Naive Bayesian classifiers. Each algorithm is evaluated using precision, recall, and ROC curves to assess their real-world applicability. This book explores: Core concepts of DDoS detection and digital evidence gathering Feature selection and dimensionality reduction for traffic analysis Implementation of classification models using real traffic data Performance evaluation and comparative analysis of learning algorithms Practical use of network forensic tools such as Xplico and NetDetector. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condición: New. Print on Demand.

Condición: New. Print on Demand.

Condición: New. PRINT ON DEMAND.

Paperback. Condición: new. Paperback. Machine Learning Approaches for DDoS Detection and Network Forensics An Investigative Framework Using KNN, SVM, and Bayesian Models on Benchmark Datasets In an era where cyber threats grow more sophisticated by the day, Distributed Denial-of-Service (DDoS) attacks have emerged as one of the most severe and disruptive forms of intrusion. This book presents a practical and research-driven guide to detecting and analyzing DDoS attacks using advanced machine learning techniques. Drawing on benchmark datasets like KDD Cup 99 and NSL-KDD, the authors introduce a robust framework for network forensic investigation, combining K-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Naive Bayesian classifiers. Each algorithm is evaluated using precision, recall, and ROC curves to assess their real-world applicability. This book explores: Core concepts of DDoS detection and digital evidence gathering Feature selection and dimensionality reduction for traffic analysis Implementation of classification models using real traffic data Performance evaluation and comparative analysis of learning algorithms Practical use of network forensic tools such as Xplico and NetDetector. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condición: new. Paperback. Machine Learning Approaches for DDoS Detection and Network Forensics An Investigative Framework Using KNN, SVM, and Bayesian Models on Benchmark Datasets In an era where cyber threats grow more sophisticated by the day, Distributed Denial-of-Service (DDoS) attacks have emerged as one of the most severe and disruptive forms of intrusion. This book presents a practical and research-driven guide to detecting and analyzing DDoS attacks using advanced machine learning techniques. Drawing on benchmark datasets like KDD Cup 99 and NSL-KDD, the authors introduce a robust framework for network forensic investigation, combining K-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Naive Bayesian classifiers. Each algorithm is evaluated using precision, recall, and ROC curves to assess their real-world applicability. This book explores: Core concepts of DDoS detection and digital evidence gathering Feature selection and dimensionality reduction for traffic analysis Implementation of classification models using real traffic data Performance evaluation and comparative analysis of learning algorithms Practical use of network forensic tools such as Xplico and NetDetector. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Machine Learning Approaches for DDoS Detection and Network ForensicsAn Investigative Framework Using KNN, SVM, and Bayesian Models on Benchmark DatasetsIn an era where cyber threats grow more sophisticated by the day, Distributed Denial-of-Service (DDoS) attacks have emerged as one of the most severe and disruptive forms of intrusion. This book presents a practical and research-driven guide to detecting and analyzing DDoS attacks using advanced machine learning techniques.Drawing on benchmark datasets like KDD Cup 99 and NSL-KDD, the authors introduce a robust framework for network forensic investigation, combining K-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Naïve Bayesian classifiers. Each algorithm is evaluated using precision, recall, and ROC curves to assess their real-world applicability.This book explores:Core concepts of DDoS detection and digital evidence gatheringFeature selection and dimensionality reduction for traffic analysisImplementation of classification models using real traffic dataPerformance evaluation and comparative analysis of learning algorithmsPractical use of network forensic tools such as Xplico and NetDetector.

Taschenbuch. Condición: Neu. Machine Learning Approaches for DDoS Detection and Network Forensics | Saswati Chatterjee | Taschenbuch | Englisch | 2025 | Eliva Press | EAN 9789999328524 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.