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Mastering Machine Learning for Penetration Testing

You're reading from Mastering Machine Learning for Penetration Testing Develop an extensive skill set to break self-learning systems using Python

Product type Paperback

Published in Jun 2018

Publisher Packt

ISBN-13 9781788997409

Length 276 pages

Edition 1st Edition

Languages

Python

Concepts

Machine Learning

Author (1):

Chiheb Chebbi

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Table of Contents (18) Chapters

Title Page

Dedication

Packt Upsell

Contributors

Preface

1. Introduction to Machine Learning in Pentesting

2. Phishing Domain Detection FREE CHAPTER

3. Malware Detection with API Calls and PE Headers

4. Malware Detection with Deep Learning

5. Botnet Detection with Machine Learning

6. Machine Learning in Anomaly Detection Systems

7. Detecting Advanced Persistent Threats

8. Evading Intrusion Detection Systems

9. Bypassing Machine Learning Malware Detectors

10. Best Practices for Machine Learning and Feature Engineering

1. Assessments

2. Other Books You May Enjoy

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Index

The detection of network anomalies

Network intrusion detection systems (IDSs) are not a new idea. They have been proposed since the earliest network attacks. IDS can be categorized into two major categories, based on their deployment: HIDS and NIDS. The following diagram illustrates a high-level overview of an IDS architecture:

HIDS

HIDS are able to collect and monitor computer systems (especially their internals) in order to give security analysts a deep visibility into what's happening on critical systems, such as workstations, servers, and mobile devices. The main goal of an HIDS is to detect intrusions.

NIDS

NIDS are responsible for detecting intrusions in network data. Basically, the detection is made based on specific patterns in sequential data. In other words, NIDSs read all of the incoming packets and try to find anomalies in them.

Anomaly-based IDS

When it comes to IDS, we are generally talking about two categories: host-based and network-based. But a new class of IDS has also arisen...

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Authors (1)

Chiheb Chebbi

Chiheb Chebbi

Chiheb Chebbi is an InfoSec enthusiast who has experience in various aspects of information security, focusing on the investigation of advanced cyber attacks and researching cyber espionage and APT attacks. Chiheb is currently pursuing an engineering degree in computer science at TEK-UP university in Tunisia. His core interests are infrastructure penetration testing, deep learning, and malware analysis. In 2016, he was included in the Alibaba Security Research Center Hall Of Fame. His talk proposals were accepted by DeepSec 2017, Blackhat Europe 2016, and many world-class information security conferences.

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