Introduction to AI for Cybersecurity

Intermediate Level
9 hours to complete Recommended experience
Flexible Schedule

Lanier Watkins

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

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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.