AI for Cybersecurity

AI for Cybersecurity

Instructors: Darius Bufnea

Access provided by Kalinga Institute of Industrial Technology

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

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

Skills you'll gain

Tools you'll learn

Details to know

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Assessments

21 assignments

Taught in English

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There are 4 modules in this course

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.

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.

What's included

12 videos7 readings7 assignments1 discussion prompt

12 videos Total 52 minutes

Introduction 1 minute
Definition of AI 2 minutes
History of AI 5 minutes
AI algorithms classification 2 minutes
General usage of AI 2 minutes
Importance of AI in cybersecurity 5 minutes
Endpoint security 1 minute
Static and dynamic features 3 minutes
Introduction 1 minute
Overview of cybersecurity threats and challenges 6 minutes
Common attack vectors and vulnerabilities 11 minutes
Introduction to security controls and defense mechanisms 11 minutes

7 readings Total 40 minutes

Conclusions 2 minutes
References and other resources 10 minutes
Key concepts and terminologies 2 minutes
Main types of malware 7 minutes
Internet security 2 minutes
Humans & data 2 minutes
References and other resources 15 minutes

7 assignments Total 145 minutes

Introduction to AI test 30 minutes
Importance of AI in Cybersecurity & Key Concepts and Terminologies test 30 minutes
Fundamentals of Cybersecurity test 30 minutes
Definition of AI 15 minutes
History of AI 15 minutes
AI algorithms and classification 10 minutes
General usage of AI 15 minutes

1 discussion prompt Total 10 minutes

Discussion Prompt - introduce yourself 10 minutes

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.

What's included

31 videos2 readings10 assignments

31 videos Total 86 minutes

Introduction 1 minute
What is data 2 minutes
Supervised learning 2 minutes
Unsupervised learning 2 minutes
Reinforcement learning 2 minutes
Machine learning models 1 minute
Machine learning algorithms 1 minute
Machine learning process 3 minutes
Information security 2 minutes
Cyber threats 2 minutes
Malware detection 2 minutes
Malware detection with ML 3 minutes
Intrusion detection 2 minutes
Intrusion detection with ML 2 minutes
Email spam detection with ML 1 minute
Introduction 1 minute
Load and review the data 3 minutes
Preparing the data 4 minutes
Feature selection 1 minute
Data splitting & transformation 1 minute
Building the models 1 minute
Testing the Decision Tree classifier 2 minutes
Testing the Random Forest classifier 1 minute
Conclusion 1 minute
Introduction to neural networks and deep learning architectures 5 minutes
Some famous neural network architectures 5 minutes
Applications of deep learning in cybersecurity: malware detection, clustering and classification 8 minutes
Training and evaluation of deep learning models for cybersecurity 9 minutes
Introduction 1 minute
Overview of NLP techniques 9 minutes
NLP applications in cybersecurity 4 minutes

2 readings Total 15 minutes

Conclusions and references 10 minutes
Conclusions and references 5 minutes

10 assignments Total 240 minutes

Supervised, Unsupervised and Reinforcement Learning test 30 minutes
Machine Learning (ML) for Cybersecurity test 30 minutes
Training and Evaluation of ML Models for Cybersecurity test 30 minutes
Deep Learning for Cybersecurity test 30 minutes
Natural Language Processing (NLP) for Cybersecurity test 30 minutes
Supervised, Unsupervised and Reinforcement Learning test 10 minutes
Machine Learning for Cybersecurity 30 minutes
Training and Evaluation of ML Models for Cybersecurity 20 minutes
Deep Learning for Cybersecurity 20 minutes
Natural Language Processing for Cybersecurity 10 minutes

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.

What's included

5 videos1 reading2 assignments

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.