Machine Learning Approaches for DDoS Detection and Network Forensics

Saswati Chatterjee

ISBN 10: 9999328520 / ISBN 13: 9789999328524

Published by Eliva Press, 2025

About this Item

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.

Seller Inventory # 9789999328524

Bibliographic Details

• Title: Machine Learning Approaches for DDoS Detection and Network Forensics
• Publisher: Eliva Press
• Publication Date: 2025
• Language: English
• ISBN 10: 9999328520
• ISBN 13: 9789999328524
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 229W x 152H x 4D millimeters 229 millimeters width by 152 millimeters height by 4 millimeters depth
• Weight: 125 grams

About this title

Synopsis

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 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 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.