Chebbi Mastering Machine Learning for Penetration Testing

Develop an extensive skill set to break self-learning systems using Python
1. Auflage 2018
ISBN: 978-1-78899-311-1
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 276 Seiten

No detailed description available for "Mastering Machine Learning for Penetration Testing".

Autoren/Hrsg.

Chebbi, Chiheb

Fachgebiete

• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Kybernetik, Systemtheorie, Komplexe Systeme
• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik

Inhaltsverzeichnis

Table of Contents - Introduction to Machine Learning in Pentesting
- Phishing Domain Detection
- Malware Detection with API Calls and PE Headers
- Malware Detection with Deep Learning
- Botnet Detection with Machine Learning
- Machine Learning in Anomaly Detection Systems
- Detecting Advanced Persistent Threats
- Evading Intrusion Detection Systems with Adversarial Machine Learning
- Bypass machine learning malware Detectors
- Best Practices for Machine Learning and Feature Engineering
- Assessments

Über Autor(innen)

Chebbi Chiheb:

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.