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Springer Machine Learning for Authorship Attribution and Cyber Forensics

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Description

This Springer publication examines the critical intersection of machine learning, digital forensics and cybersecurity in modern online environments. The book maps the cybersecurity landscape whilst analysing vulnerabilities in email, chat conversations and social media platforms that expose organisations to cyber threats. It explores the sources, causes and effects of digital crimes alongside emerging threats to society, then addresses how knowledge discovery, machine learning and data analytics provide solutions for collecting cyber intelligence and strengthening digital forensic capabilities. Essential for professionals and researchers seeking to understand contemporary cybercrime prevention methods and the technological approaches that protect against sophisticated online threats.

Key Features

Covers machine learning techniques specifically designed for authorship attribution and cyber forensics applications.

Examines vulnerabilities in email, chat conversations and social media as vectors for cybercrime and digital attacks.

Explores data analytics and knowledge discovery methods for collecting and analysing cyber intelligence.

Published by Springer as part of the International Series on Computer, Entertainment and Media Technology.

Provides comprehensive analysis of digital crime sources, causes, effects and emerging societal threats.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
05 December 2020
Listed Since
11 September 2020

Barcode

No barcode data available

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