Introduction to AI for Cybersecurity

Introduction to AI for Cybersecurity

本课程是 AI for Cybersecurity 专项课程的一部分

位教师：Lanier Watkins

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4个模块

深入了解一个主题并学习基础知识。

4.1

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9 小时完成

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4个模块

深入了解一个主题并学习基础知识。

4.1

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中级等级

推荐体验

9 小时完成

灵活的计划

自行安排学习进度

您将学到什么

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.

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作业

9 项作业

授课语言：英语（English）

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积累特定领域的专业知识

本课程是 AI for Cybersecurity 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

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该课程共有4个模块

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.

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.

涵盖的内容

2篇阅读材料

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.

涵盖的内容

2个视频3篇阅读材料3个作业

2个视频总计18分钟

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

3篇阅读材料总计70分钟

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

3个作业总计90分钟

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

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.

涵盖的内容

5个视频3篇阅读材料3个作业

5个视频总计15分钟

Detecting Email Cyber Threats with AI4分钟
Perceptron NN Spam Filter Example2分钟
Support Vector Machine (SVM) Spam Filter Example3分钟
Phishing Detection with Regression and Trees3分钟
Naive Bayes Spam Filter Example1分钟

3篇阅读材料总计70分钟

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

3个作业总计90分钟

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

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.

涵盖的内容

2个视频3篇阅读材料3个作业1个非评分实验室

2个视频总计11分钟

Securing User Authentication5分钟
Keystroke and Facial Recognition Biometric Authentication6分钟

3篇阅读材料总计70分钟

Reading References15分钟
Reading References15分钟
Self-Reflective Reading: Authentication is Paramount40分钟

3个作业总计90分钟

Understanding and Securing User Authentication Threats15分钟
Implementing Biometric Solutions: Keystroke and Facial Recognition15分钟
Securing User Authentication 60分钟

1个非评分实验室总计60分钟

Practice Lab: Detecting IoT Malware Behavior in Network Traffic60分钟

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位教师

授课教师评分

4.0 (20个评价)

Lanier Watkins

Johns Hopkins University

3 门课程6,256 名学生

提供方

Johns Hopkins University

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学生评论

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已于 Mar 5, 2025审阅

The necessary material, good questions. Thank you.

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