Chatterjee Saswati (16 results)

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

Condition: New.

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

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

Condition: New. Print on Demand.

Condition: New. Print on Demand.

Condition: New. PRINT ON DEMAND.

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

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

Taschenbuch. Condition: 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.…

Paperback. Condition: 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. …

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 200 pp. Englisch.

Taschenbuch. Condition: 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.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Network security has become increasingly important with the rapid expansion of interconnected digital systems and the growing sophistication of cyber threats. Among the major network-based threats, Distributed Denial of Service (DDoS), Man-in-the-Middle (MITM), and Address Resolution Protocol (ARP) spoofing attacks pose significant risks to data confidentiality, system availability, and secure communication. The dynamic nature of these attacks presents considerable challenges to conventional security mechanisms, which often rely on predefined rules and signatures. To address these limitations, this research investigates the application of machine learning techniques for intelligent network threat detection. The study focuses on identifying anomalous network behavior, distinguishing legitimate traffic from malicious activities, and improving the reliability and accuracy of cyber threat detection. The proposed machine learning-driven approach aims to strengthen intrusion detection capabilities and support more adaptive and effective network security mechanisms against evolving cyber threats. …

Taschenbuch. Condition: Neu. NETWORK INVESTIGATION OF CYBER THREAT USING MACHINE LEARNING | INVESTIGATION OF CYBER THREAT USING MACHINE LEARNING | Saswati Chatterjee | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786630539394 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.…

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Network security has become increasingly important with the rapid expansion of interconnected digital systems and the growing sophistication of cyber threats. Among the major network-based threats, Distributed Denial of Service (DDoS), Man-in-the-Middle (MITM), and Address Resolution Protocol (ARP) spoofing attacks pose significant risks to data confidentiality, system availability, and secure communication. The dynamic nature of these attacks presents considerable challenges to conventional security mechanisms, which often rely on predefined rules and signatures. To address these limitations, this research investigates the application of machine learning techniques for intelligent network threat detection. The study focuses on identifying anomalous network behavior, distinguishing legitimate traffic from malicious activities, and improving the reliability and accuracy of cyber threat detection. The proposed machine learning-driven approach aims to strengthen intrusion detection capabilities and support more adaptive and effective network security mechanisms against evolving cyber threats. 200 pp. Englisch. …