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Introduction to AI for Cybersecurity

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Introduction to AI for Cybersecurity

This course is part of AI for Cybersecurity Specialization

Instructor: Lanier Watkins

10,394 already enrolled

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

98 reviews

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

98 reviews

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

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

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

9 assignments

Taught in English

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This course is part of the AI for Cybersecurity Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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

In "Introduction to AI for Cybersecurity," you'll gain foundational knowledge of how artificial intelligence (AI) is transforming the field of cybersecurity. This course covers key AI techniques and how they can be applied to enhance security measures, detect threats, and secure digital systems. Learners will explore hands-on implementations of AI models using tools like Jupyter Notebooks, allowing them to detect spam, phishing emails, and secure user authentication using biometric solutions.

What makes this course unique is its focus on real-world applications, blending AI theory with practical skills relevant to today's cybersecurity challenges. By the end of the course, you'll have developed the ability to use AI to address cyber threats such as email fraud and fake logins, and will be equipped with practical skills to protect digital assets in a rapidly evolving technological landscape. Whether you're a cybersecurity professional or someone seeking to expand your skills in AI, this course provides a critical understanding of how AI can be leveraged to mitigate security risks and keep systems secure.

Module details

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.

What's included

2 readings

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.

What's included

2 videos3 readings3 assignments

2 videosTotal 17 minutes

Artificial Intelligence Background and Overview10 minutes
A Hands-On Look at Several Machine Learning Algorithms7 minutes

3 readingsTotal 70 minutes

Reading References15 minutes
Reading References15 minutes
Self-Reflective Reading: Hands-On ML Examples and Development Process40 minutes

3 assignmentsTotal 90 minutes

Introduction to Artificial Intelligence for Cybersecurity15 minutes
Practical Machine Learning Algorithms for Cybersecurity15 minutes
AI for Cybersecurity Professionals60 minutes

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.

What's included

5 videos3 readings3 assignments

5 videosTotal 15 minutes

Detecting Email Cyber Threats with AI4 minutes
Perceptron Spam Filter Example2 minutes
Support Vector Machine (SVM) Spam Filter Example3 minutes
Phishing Detection with Regression and Trees4 minutes
Naive Bayes Spam Filter Example1 minute

3 readingsTotal 70 minutes

Reading References15 minutes
Reading References15 minutes
Self-Reflective Reading: Bridging Theory and Practice in Cybersecurity40 minutes

3 assignmentsTotal 90 minutes

AI Techniques for Detecting Email Threats15 minutes
Advanced Spam Filtering Methods and Phishing Detection15 minutes
Ham or Spam? Detecting Email Cybersecurity Threats with AI60 minutes

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.