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AI for Cybersecurity

AI for Cybersecurity

位教师：Darius Bufnea

包含在中

了解更多

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Understand the fundamentals of AI and ML and how they are applied in cybersecurity, including key concepts, security domains and common cyber threats
Learn the core ML techniques used in cybersecurity — supervised, unsupervised, and reinforcement learning — and when to apply each method
Gain practical experience using Python to build, train, and evaluate ML models for malware detection, intrusion detection, and spam filtering
Develop the skills to apply AI-driven approaches to protect systems and networks against evolving cyber threats and real-world security challenges

您将获得的技能

您将学习的工具

Classification Algorithms

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

21 项作业

授课语言：英语（English）

91%

of learners achieved a positive career outcome

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

该课程共有4个模块

The AI for Cybersecurity course offers a comprehensive introduction to the usage of AI methods, most specifically machine learning in the field of cybersecurity. It begins with an introduction to AI, covering its definitions, historical development, and general applications. The course then discusses the importance of AI in cybersecurity, introducing key concepts, and the distinction between the host security and the network security.

Students will gain a solid understanding of cybersecurity fundamentals, including common attack vectors and vulnerabilities, as well as an introduction to the defense mechanisms used to protect systems. In the first parts, the course presents the core AI techniques applicable to cybersecurity from a theoretical point of view, with a focus on machine learning (ML) methods such as supervised, unsupervised and reinforcement learning. In subsequent parts, students will learn how to apply ML techniques practically to specific cybersecurity challenges like malware detection and classification, intrusion detection, and email spam filtering. They will explore the process of implementing ML models for these tasks, training and evaluating them on data using the Python programming language. Overall, this course equips students with both the theoretical understanding and practical skills needed to apply AI methods in order to protect systems against evolving cyber threats.

单元详情

Welcome to the introductory part of the AI and Cybersecurity course! During the 5 video lectures and 2 readings of this module you will find various definitions of the Artificial Intelligence, the evolution of this domain and the classification of the AI algorithms in search-based algorithms and intelligent systems. The domains where AI is successfully used are presented, with focus on the use of AI in cybersecurity related tasks (e.g.: network analysis, intrusion detection, malicious web link detection, anomaly detection or malware classification ). Afterwards, the basic concepts of cybersecurity will be introduced, and the classification of security threats at endpoint level or internet level. You will discover types of cybersecurity threats and how they can be defended.

涵盖的内容

12个视频7篇阅读材料7个作业1个讨论话题

12个视频总计52分钟

Introduction1分钟
Definition of AI2分钟
History of AI5分钟
AI algorithms classification2分钟
General usage of AI2分钟
Importance of AI in cybersecurity5分钟
Endpoint security1分钟
Static and dynamic features3分钟
Introduction1分钟
Overview of cybersecurity threats and challenges6分钟
Common attack vectors and vulnerabilities11分钟
Introduction to security controls and defense mechanisms11分钟

7篇阅读材料总计40分钟

Conclusions2分钟
References and other resources10分钟
Key concepts and terminologies2分钟
Main types of malware7分钟
Internet security2分钟
Humans & data2分钟
References and other resources15分钟

7个作业总计145分钟

Definition of AI15分钟
History of AI15分钟
AI algorithms and classification10分钟
General usage of AI15分钟
Introduction to AI test30分钟
Importance of AI in Cybersecurity & Key Concepts and Terminologies test30分钟
Fundamentals of Cybersecurity test30分钟

1个讨论话题总计10分钟

Discussion Prompt - introduce yourself10分钟

Welcome to the second module of the AI for Cybersecurity course. This module consists of five lessons that explore different AI techniques and their applications in cybersecurity. It begins with an introduction to Machine Learning (ML) and its three basic types. The second lesson discusses three key cybersecurity tasks and explains how ML can be applied to address them. In the third lesson, you will follow a practical example of implementing and evaluating a malware detection system using two ML models: Decision Trees and Random Forests. The fourth lesson introduces fundamental concepts of deep learning (DL) and its applications in cybersecurity. Finally, the module concludes with an overview of Natural Language Processing (NLP) and how it can be used for cybersecurity-related tasks.

涵盖的内容

31个视频2篇阅读材料10个作业

31个视频总计86分钟

Introduction1分钟
What is data2分钟
Supervised learning2分钟
Unsupervised learning2分钟
Reinforcement learning2分钟
Machine learning models1分钟
Machine learning algorithms1分钟
Machine learning process3分钟
Information security2分钟
Cyber threats2分钟
Malware detection2分钟
Malware detection with ML3分钟
Intrusion detection2分钟
Intrusion detection with ML2分钟
Email spam detection with ML1分钟
Introduction1分钟
Load and review the data3分钟
Preparing the data4分钟
Feature selection1分钟
Data splitting & transformation1分钟
Building the models1分钟
Testing the Decision Tree classifier2分钟
Testing the Random Forest classifier1分钟
Conclusion1分钟
Introduction to neural networks and deep learning architectures5分钟
Some famous neural network architectures5分钟
Applications of deep learning in cybersecurity: malware detection, clustering and classification8分钟
Training and evaluation of deep learning models for cybersecurity9分钟
Introduction1分钟
Overview of NLP techniques9分钟
NLP applications in cybersecurity4分钟

2篇阅读材料总计15分钟

Conclusions and references10分钟
Conclusions and references5分钟

10个作业总计240分钟

Supervised, Unsupervised and Reinforcement Learning test10分钟
Machine Learning for Cybersecurity30分钟
Training and Evaluation of ML Models for Cybersecurity20分钟
Deep Learning for Cybersecurity20分钟
Natural Language Processing for Cybersecurity10分钟
Supervised, Unsupervised and Reinforcement Learning test30分钟
Machine Learning (ML) for Cybersecurity test30分钟
Training and Evaluation of ML Models for Cybersecurity test30分钟
Deep Learning for Cybersecurity test30分钟
Natural Language Processing (NLP) for Cybersecurity test30分钟

This module explores how AI techniques are applied to detect and mitigate online threats. It begins with an overview of malicious web links, explaining how they redirect users, run harmful code, and spread misinformation like fake news and phishing content. Detection methods are categorized into dynamic (e.g., sandboxing, honeypots) and static (e.g., URL analysis, blacklists, machine learning models). The module also details how URLs can be analyzed through lexical, host-based, and social media features. A special focus is given to Domain Generation Algorithms (DGAs), which malware uses to create deceptive domain names. Detecting DGAs is challenging and involves either manual feature extraction or automated learning methods. Another topic of this module is detecting fake news using deep learning modules. Finally, the presentation briefly talks about clickbait detection. Real-world case studies and research-backed solutions are presented throughout. By the end, learners are equipped to recognize key cyber threats and understand the AI models used to counter them.

涵盖的内容

5个视频1篇阅读材料2个作业

This final module explores the ethical challenges and legal frameworks surrounding the use of AI in cybersecurity. Key concepts such as safety vs. security, risk management, and the balance between privacy and protection will be discussed. We will introduce the AI4People framework - autonomy, non-maleficence, beneficence, justice, and explainability - and examine its application to real-world cyber threats. The module also covers key regulations such as the EU AI Act, NIS2, the Cyber Resilience Act, and DORA, along with ethical guidelines from ACM, IEEE, and ISSA. Finally, we'll look at future trends, including open-source collaboration, ethical hacking, and global cooperation in securing AI systems. By the end, learners will understand the ethical and regulatory landscape and be prepared for the evolving challenges of AI in cybersecurity.