Artificial Intelligence for Cybersecurity

Artificial Intelligence for Cybersecurity

Instructor: Packt - Course Instructors

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19 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

19 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

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

Skills you'll gain

Tools you'll learn

Fraud detection

Details to know

Shareable certificate

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Assessments

19 assignments

Taught in English

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There are 19 modules in this course

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.

What's included

2 videos6 readings1 assignment

2 videos Total 2 minutes

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

6 readings Total 60 minutes

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

1 assignment Total 10 minutes

Big Data and Cybersecurity Fundamentals 10 minutes

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

What's included

1 video4 readings1 assignment

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

What's included

1 video3 readings1 assignment

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

What's included

1 video6 readings1 assignment

1 video Total 1 minute

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

6 readings Total 80 minutes

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

1 assignment Total 10 minutes

Exploring AI and Machine Learning Fundamentals 10 minutes

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

What's included

1 video4 readings1 assignment

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

What's included

1 video11 readings1 assignment

1 video Total 1 minute

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

11 readings Total 140 minutes

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

1 assignment Total 10 minutes

AI Project Tools and Processes 10 minutes

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

What's included

1 video2 readings1 assignment

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

What's included

1 video3 readings1 assignment

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.

What's included

1 video3 readings1 assignment

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

What's included

1 video7 readings1 assignment

1 video Total 1 minute

User Authentication and Access Control - Overview Video 1 minute

7 readings Total 130 minutes

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

1 assignment Total 10 minutes

Authentication and Access Control Fundamentals 10 minutes

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

What's included

1 video3 readings1 assignment

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.

What's included

1 video6 readings1 assignment

1 video Total 1 minute

Anomaly Detection in Industrial Control Systems - Overview Video 1 minute

6 readings Total 70 minutes

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

1 assignment Total 10 minutes

Anomaly Detection in Industrial Control Systems 10 minutes

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.

What's included

1 video3 readings1 assignment

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

What's included

1 video2 readings1 assignment

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.

What's included

1 video1 reading1 assignment

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.

What's included

1 video4 readings1 assignment

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

What's included

1 video9 readings1 assignment

1 video Total 1 minute

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

9 readings Total 120 minutes

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

1 assignment Total 10 minutes

Adversarial Machine Learning and System Security 10 minutes

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