Introduction to AI for Cybersecurity

Instructor: Lanier Watkins

Intermediate Level • 9 hours to complete Recommended experience • Flexible Schedule

What You'll Learn

  • Use AI techniques to detect and mitigate various cyber threats, protecting digital assets and data.
  • Develop and apply machine learning models to identify, classify, and filter spam and phishing emails.
  • Implement AI-driven biometric solutions like keystroke dynamics and facial recognition to enhance user authentication security.

Skills You'll Gain

Cybersecurity
Machine Learning Algorithms
Machine Learning
Authentications
Natural language processing
Computer Vision
Artificial Intelligence and Machine Learning (AI/ML)
Email Security
Cyber Threat Intelligence
Cyber Attacks
Multi-Factor Authentication
Artificial Intelligence
Intrusion Detection and Prevention
Threat Modeling
Threat Detection
Deep Learning
Jupyter

Shareable Certificate

Earn a shareable certificate to add to your LinkedIn profile

Outcomes

  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

This course will guide you through the ML development process and its vital applications in combating cyber threats. We’ll explore the challenges posed by technological advancements, examine AI’s role in spam filtering and email threat detection, and implement key algorithms like decision trees and Naïve Bayes. Additionally, you’ll learn how biometric solutions, such as keystroke dynamics and facial recognition, can enhance user authentication security.

In this module, we will discuss the background of artificial intelligence (AI) and provide a brief overview. Also, in this module and every module, we will take a hands-on approach to learning how to use AI for cybersecurity.

In this module, we shall discuss the detection of email threats using AI. Also, we will implement hands-on examples of the use of various ML techniques to detect email threats such as perceptron for spam filtering, support vector machine for spam filtering, regression and decision tree algorithms for spam filtering, and the use of Naïve Bayes ML algorithm and natural language processing for spam filtering.

In this module, we will discuss the background of threats against user authentication. Also, we will explore hands-on implementations of fake login detection analytics using biometrics.