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You're reading from Hands-On Machine Learning for Cybersecurity Safeguard your system by making your machines intelligent using the Python ecosystem

Product type Paperback

Published in Dec 2018

Publisher Packt

ISBN-13 9781788992282

Length 318 pages

Edition 1st Edition

Languages

Python

Tools

CUDA

Concepts

Cybersecurity

Authors (2):

Soma Halder

Sinan Ozdemir

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Table of Contents (13) Chapters

Preface

1. Basics of Machine Learning in Cybersecurity

2. Time Series Analysis and Ensemble Modeling FREE CHAPTER

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

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Use case

We will now discuss some of the earlier intrusions and injections that we have already discussed at the beginning of the chapter. For the purpose of our experiment, we will use the KDD Cup 1999 computer network intrusion detection dataset. The goal of this experiment is to distinguish between the good and bad network connections.

The dataset

The data sources are primarily sourced from the 1998 DARPA Intrusion Detection Evaluation Program by MIT Lincoln Labs. This dataset contains a variety of network events that have been simulated in the military network environment. The data is a TCP dump that has been accumulated from the local area network of an Air Force environment. The data is peppered with multiple attacks...

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Authors (2)

Halder

Soma Halder is the data science lead of the big data analytics group at Reliance Jio Infocomm Ltd, one of India's largest telecom companies. She specializes in analytics, big data, cybersecurity, and machine learning. She has approximately 10 years of machine learning experience, especially in the field of cybersecurity. She studied at the University of Alabama, Birmingham where she did her master's with an emphasis on Knowledge discovery and Data Mining and computer forensics. She has worked for Visa, Salesforce, and AT&T. She has also worked for start-ups, both in India and the US (E8 Security, Headway ai, and Norah ai). She has several conference publications to her name in the field of cybersecurity, machine learning, and deep learning.

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Sinan Ozdemir

Sinan is an active lecturer focusing on large language models and a former lecturer of data science at the Johns Hopkins University. He is the author of multiple textbooks on data science and machine learning including "Quick Start Guide to LLMs". Sinan is currently the founder of LoopGenius which uses AI to help people and businesses boost their sales and was previously the founder of the acquired Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. He holds a Master's Degree in Pure Mathematics from Johns Hopkins University and is based in San Francisco.

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