Machine Learning for Network Security

SkillsFuture Credit Approved Course

Introduction

Cybersecurity experts may have a hard time monitoring and analysing network data due to the sheer volume. One possible solution that is being widely explored is to use Machine Learning and Data Mining methods for cybersecurity related problems. This can help to build better detectors from complex data that is generated over the Internet. Such methods can also help discover the information required to build more secure systems.

Data can be gathered from a combination of software installed on customer workstations and sensors placed in network segments. This data can be fed to systems that use machine learning which can classify the incoming samples and can distinguish between normal behaviour and any anomalies. Historical data and patterns can be used to compare samples and identify evolving threats.

Course Content

  1. Introduction to Network Security Concepts
    • Challenges to Securing Information
    • Core principles of Information Security
    • Types of Network Attacks
    • Malware
  2. Introduction to Firewalls
    • Types of Firewalls
    • Firewall Design and Architecture
    • Configuring Firewalls
  3. Intrusion Detection and Prevention Systems
    • Types of IDS
    • Host and Network based IDS
    • IPS
    • Honeypots
    • Introduction to Snort
  4. Introduction to Data Mining and Machine Learning Concepts
    • Supervised /Unsupervised Machine Learning Methods
    • Challenges in Data Mining and Machine Learning
  5. Data Collection and Analysis
    • Machine Learning for Anomaly Detection
    • Machine Learning in Intrusion Detection
    • Machine Learning and Network Traffic
    • Emerging Challenges in Cybersecurity
  6. Network Forensics
    • Forensic Principles
    • Capturing Network Traffic
    • Use of Cyber Forensic Tools
    • Legal issues