Artificial Intelligence for Cybersecurity

Artificial Intelligence for Cybersecurity

位教师：Packt - Course Instructors

访问权限由 New York State Department of Labor 提供

19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

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

19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

2 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

Recognize AI as a powerful tool for intelligence analysis in cybersecurity
Explore the components and workflow of AI security solutions
Design AI-based solutions for cybersecurity challenges

您将获得的技能

您将学习的工具

Fraud detection

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

19 项作业

授课语言：英语（English）

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19个模块

Artificial intelligence (AI) is revolutionizing how organizations safeguard digital assets, detect threats, and respond to cyberattacks. This course provides a deep understanding of how AI can be leveraged to enhance cybersecurity, enabling professionals to build intelligent systems that predict and prevent potential breaches.

Learners will explore how AI-driven techniques streamline security operations, identify anomalies, and improve decision-making in real-time. By the end of the course, you’ll be able to design AI-based solutions that strengthen defense mechanisms and address modern cybersecurity challenges. What makes this course unique is its focus on practical, hands-on implementation of AI tools and algorithms in security workflows. It bridges the gap between theory and practice, providing both conceptual clarity and real-world case studies. This course is ideal for cybersecurity professionals, machine learning practitioners, and students interested in combining AI with security. A basic understanding of Python and machine learning concepts is recommended. Based on the book, Artificial Intelligence for Cybersecurity, by Bojan Kolosnjaji, Huang Xiao, Peng Xu, and Apostolis Zarras.

In this section, we examine big data's role in cybersecurity, focusing on threat detection, incident response, and ethical considerations using advanced analytical tools and technologies.

涵盖的内容

2个视频6篇阅读材料1个作业

2个视频总计2分钟

Courser Overview 1分钟
Big Data in Cybersecurity - Overview Video 1分钟

6篇阅读材料总计60分钟

Introduction 10分钟
The Velocity of Data in Cyberspace 10分钟
The Veracity of Data in Cyberspace 10分钟
Behavioral Analytics 10分钟
Addressing Resource Constraints 10分钟
Big Data Applications in Cybersecurity 10分钟

1个作业总计10分钟

Big Data and Cybersecurity Fundamentals 10分钟

In this section, we cover automation in cybersecurity, including tools, challenges, and ethical considerations.

涵盖的内容

1个视频4篇阅读材料1个作业

In this section, we explore AI's role in cybersecurity, including applications and regulatory compliance.

涵盖的内容

1个视频3篇阅读材料1个作业

In this section, we clarify the distinctions between AI, ML, and statistics, and explore ML taxonomies, limitations, and security risks for practical applications.

涵盖的内容

1个视频6篇阅读材料1个作业

1个视频总计1分钟

AI, Machine Learning, and Statistics A Taxonomy - Overview Video 1分钟

6篇阅读材料总计80分钟

Introduction 10分钟
The Relation to Statistical Learning Theory 20分钟
Reinforcement Learning 20分钟
Graph Data 10分钟
DL and Its Recent Advances 10分钟
The Limitation and Security Concern 10分钟

1个作业总计10分钟

Exploring AI and Machine Learning Fundamentals 10分钟

In this section, we cover AI methods like random forest, K-means, and GANs for cybersecurity applications.

涵盖的内容

1个视频4篇阅读材料1个作业

In this section, we cover AI project workflows, tools for visual network traffic analysis, and malware detection.

涵盖的内容

1个视频11篇阅读材料1个作业

1个视频总计1分钟

Workflow, Tools, and Libraries in AI Projects - Overview Video 1分钟

11篇阅读材料总计140分钟

Introduction 10分钟
Workflow for the Pre-Trained AI Model 10分钟
Advanced Topics Integrating an AI Model into a Product 10分钟
Tools and Libraries for Visual Network Traffic Analysis 10分钟
Model Training and Testing 10分钟
Libraries of Visual Network Traffic Analysis 10分钟
An Example of Visual Network Traffic Analysis 20分钟
Background of Malware Detection 10分钟
Tools for Malware Detection 20分钟
Libraries for Malware Detection 10分钟
An Example of Android Malware Detection 20分钟

1个作业总计10分钟

AI Project Tools and Processes 10分钟

In this section, we explore AI-driven malware detection and network intrusion analysis, focusing on dataset utilization, model implementation, and real-world threat classification.

涵盖的内容

1个视频2篇阅读材料1个作业

In this section, we explore UEBA techniques for detecting advanced threats using AI-driven anomaly detection and numerical feature extraction from network data.

涵盖的内容

1个视频3篇阅读材料1个作业

In this section, we explore fraud, phishing, and spam detection using machine learning, focusing on collaborative methods like federated learning and multi-party computation for privacy-preserving anomaly detection.

涵盖的内容

1个视频3篇阅读材料1个作业

In this section, we cover user authentication and access control methods to secure digital environments.

涵盖的内容

1个视频7篇阅读材料1个作业

1个视频总计1分钟

User Authentication and Access Control - Overview Video 1分钟

7篇阅读材料总计130分钟

Introduction 10分钟
Knowledge-Based Authentication 10分钟
MFA 20分钟
Models Frameworks 10分钟
Implement the OAuth 2.0 Authorization Flow in Your Mobile App 20分钟
Use Different SELinux Contexts 20分钟
Defining and Applying Appropriate SELinux Contexts 40分钟

1个作业总计10分钟

Authentication and Access Control Fundamentals 10分钟

In this section, we cover threat intelligence retrieval and AI applications for analyzing cyber threats.

涵盖的内容

1个视频3篇阅读材料1个作业

In this section, we explore anomaly detection techniques for industrial control systems, focusing on identifying cyber threats and enhancing security through practical methods and frameworks.

涵盖的内容

1个视频6篇阅读材料1个作业

1个视频总计1分钟

Anomaly Detection in Industrial Control Systems - Overview Video 1分钟

6篇阅读材料总计70分钟

Introduction 10分钟
Phishing Attacks 10分钟
Cyberattacks on the Components of ICSs 10分钟
Model-based Techniques 10分钟
Anomaly Detection for the ICS 20分钟
Future Works and Directions 10分钟

1个作业总计10分钟

Anomaly Detection in Industrial Control Systems 10分钟

In this section, we explore the use of large language models (LLMs) in cybersecurity, focusing on their applications in threat detection, vulnerability discovery, and secure workflow design, while addressing their inherent security risks.

涵盖的内容

1个视频3篇阅读材料1个作业

In this section, we explore data quality's role in AI and LLMs, focusing on validation, cleaning, and practical applications to ensure reliable outcomes.

涵盖的内容

1个视频2篇阅读材料1个作业

In this section, we explore correlation, causation, bias, and variance in AI for cybersecurity, emphasizing their impact on model accuracy and decision-making in real-world applications.

涵盖的内容

1个视频1篇阅读材料1个作业

In this section, we explore evaluating AI models using metrics, monitoring performance for latency and bias, and implementing human-in-the-loop strategies for continuous improvement in cybersecurity.

涵盖的内容

1个视频4篇阅读材料1个作业

In this section, we explore adversarial machine learning (AML) concepts, vulnerabilities in generative AI, and defensive techniques to enhance ML security and robustness.

涵盖的内容

1个视频9篇阅读材料1个作业

1个视频总计1分钟

Learning in a Changing and Adversarial Environment - Overview Video 1分钟

9篇阅读材料总计120分钟

Introduction 10分钟
Introduction to AML 10分钟
The Realistic Learning Environment 10分钟
Learning Process with Data Flow 20分钟
Security Violation 10分钟
Knowledge of the Learning Algorithm 20分钟
Attack Taxonomy Summary 10分钟
Defense as Prevention 10分钟
Defense as a Response 20分钟

1个作业总计10分钟

Adversarial Machine Learning and System Security 10分钟

In this section, we examine AI security challenges, focusing on privacy, accountability, and trust, while exploring strategies for responsible AI governance and risk management.