Machine Learning for Network Security


Uncover the power of Artificial Intelligence (AI) and elevate your expertise by enrolling in our cutting-edge course. Designed to help you learn, upskill, and acquire knowledge that is very relevant and useful in addressing real-world problems, this course focuses on the pivotal role of AI in revolutionizing cybersecurity. In an era where organizations grapple with vast volumes of data generated and transmitted over networks, traditional cybersecurity approaches often fall short. Machine Learning and Data Mining emerge as transformative solutions to address the challenges faced by cybersecurity experts. These advanced methods empower professionals to build more effective detectors from complex data sets, enhancing the security of systems. By leveraging machine learning, historical data, and pattern recognition, the course equips you to classify incoming samples and distinguish between normal behavior and anomalies. The comprehensive curriculum delves into network security intricacies and explores diverse machine learning and data mining solutions tailored to cybersecurity challenges. Stay ahead of the curve and navigate the dynamic landscape of cybersecurity with proficiency. Enroll now to gain essential skills that are not just relevant and useful but instrumental in solving real-world problems in the realm of network security and cybersecurity.

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