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Price distribution over 70 days • 2 price levels

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Most common price: £45 (47 days, 67.1%)

Price range: £41 - £45

Price levels: 2 different prices over 70 days

Description

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.

Product Specifications

Format: paperback
ASIN: 9999328520
Category: Books > Subjects > Computing & Internet > Computer Science > Architecture & Microprocessors
Domain: Amazon UK
Release Date: 06 July 2025
Listed Since: 09 July 2025

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About as cheap as it gets — within 5% of the all-time low it hit 1 month ago.

Machine Learning Approaches for DDoS Detection and Network Forensics: An Investigative Framework Using KNN, SVM, and Bayesian Models on Benchmark Datasets

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About as cheap as it gets — within 5% of the all-time low it hit 1 month ago.

Machine Learning Approaches for DDoS Detection and Network Forensics: An Investigative Framework Using KNN, SVM, and Bayesian Models on Benchmark Datasets

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