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Hands-On Machine Learning for Cybersecurity

You're reading from  Hands-On Machine Learning for Cybersecurity

Product type Book
Published in Dec 2018
Publisher Packt
ISBN-13 9781788992282
Pages 318 pages
Edition 1st Edition
Languages
Authors (2):
Soma Halder Soma Halder
Profile icon Soma Halder
Sinan Ozdemir Sinan Ozdemir
Profile icon Sinan Ozdemir
View More author details
Toc

Table of Contents (13) Chapters close

Preface 1. Basics of Machine Learning in Cybersecurity 2. Time Series Analysis and Ensemble Modeling 3. Segregating Legitimate and Lousy URLs 4. Knocking Down CAPTCHAs 5. Using Data Science to Catch Email Fraud and Spam 6. Efficient Network Anomaly Detection Using k-means 7. Decision Tree and Context-Based Malicious Event Detection 8. Catching Impersonators and Hackers Red Handed 9. Changing the Game with TensorFlow 10. Financial Fraud and How Deep Learning Can Mitigate It 11. Case Studies 12. Other Books You May Enjoy

Worms

Worms are also a specialized form of malware that vigorously spread across multiple computer systems. Worms typically cause hindrances to the computer system and eat up bandwidth with payload that may even damage the system. Worms, unlike viruses, do not require any human intervention to multiply or replicate. They have the capability of self replication.

In the previous chapters, we have already established the fact that the most popular attacks are malware, phishing, or cross-site scripting. Other ways of malware spreading within the network systems are through malware injections.

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