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.
Introduction to AI for Cybersecurity
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
9 hours to complete
3 weeks at 3 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
Details to know
Shareable certificate
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Recently updated!
September 2024
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.
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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.
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
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
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.
What's included
2 videos3 readings3 assignments1 ungraded lab
Instructor
Offered by
Recommended if you're interested in Security
Johns Hopkins University
Johns Hopkins University
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