Artificial Intelligence for Cybersecurity
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Artificial Intelligence for Cybersecurity
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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
- Artificial Intelligence and Machine Learning (AI/ML)
- Artificial Intelligence
- Cyber Security Strategy
- Large Language Modeling
- LLM Application
- Threat Detection
- Applied Machine Learning
- Machine Learning Methods
- Federated Learning
- IT Automation
- Cyber Risk
- Cyber Engineering
- Anomaly Detection
- AI Security
- Cybersecurity
- AI Integrations
- Malware Protection
- Cyber Attacks
- Intrusion Detection and Prevention
Tools you'll learn
Details to know
March 2026
19 assignments
See how employees at top companies are mastering in-demand skills
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
1 video•Total 1 minute
- Automation in Cybersecurity - Overview Video•1 minute
4 readings•Total 60 minutes
- Introduction•10 minutes
- Alerting and Reporting•20 minutes
- Potential Drawbacks and Challenges of Automation•10 minutes
- The Future of Automation in Cybersecurity•20 minutes
1 assignment•Total 10 minutes
- Automation in Cybersecurity Fundamentals•10 minutes
In this section, we explore AI's role in cybersecurity, including applications and regulatory compliance.
What's included
1 video3 readings1 assignment
1 video•Total 1 minute
- Cybersecurity Data Analytics - Overview Video•1 minute
3 readings•Total 50 minutes
- Introduction•20 minutes
- Applications of AI•20 minutes
- The Regulatory Landscape•10 minutes
1 assignment•Total 10 minutes
- AI in Cybersecurity Data Analytics•10 minutes
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
1 video•Total 1 minute
- AI Problems and Methods - Overview Video•1 minute
4 readings•Total 90 minutes
- Introduction•20 minutes
- Deep Learning•30 minutes
- Unsupervised Learning Methods•20 minutes
- Semi-supervised Learning Methods•20 minutes
1 assignment•Total 10 minutes
- Machine Learning Fundamentals and Techniques•10 minutes
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
1 video•Total 1 minute
- Malware and Network Intrusion Detection and Analysis - Overview Video•1 minute
2 readings•Total 40 minutes
- Introduction•20 minutes
- Network Intrusion Detection•20 minutes
1 assignment•Total 10 minutes
- Network Security and Machine Learning Fundamentals•10 minutes
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
1 video•Total 1 minute
- User and Entity Behavior Analysis - Overview Video•1 minute
3 readings•Total 50 minutes
- Introduction•10 minutes
- Feature Extraction•10 minutes
- Exercise UEBA Anomaly Detection•30 minutes
1 assignment•Total 10 minutes
- Behavioral Analysis in Cybersecurity•10 minutes
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
1 video•Total 1 minute
- Fraud, Spam, and Phishing Detection - Overview Video•1 minute
3 readings•Total 50 minutes
- Introduction•10 minutes
- Understanding Phishing Detection with a Practical Example•20 minutes
- Designing and Implementing Collaborative Anomaly Detection Systems•20 minutes
1 assignment•Total 10 minutes
- Detecting Fraud, Spam, and Phishing•10 minutes
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
1 video•Total 1 minute
- Threat Intelligence - Overview Video•1 minute
3 readings•Total 60 minutes
- Introduction•20 minutes
- Topic Modeling•20 minutes
- Implementation•20 minutes
1 assignment•Total 10 minutes
- Threat Intelligence Fundamentals and Analysis•10 minutes
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
1 video•Total 1 minute
- Large Language Models and Cybersecurity - Overview Video•1 minute
3 readings•Total 30 minutes
- Introduction•10 minutes
- Using LLMs for Security•10 minutes
- LLMs for Offensive Security•10 minutes
1 assignment•Total 10 minutes
- Security and Capabilities of Large Language Models•10 minutes
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
1 video•Total 1 minute
- Data Quality and Its Usage in the AI and LLM Era - Overview Video•1 minute
2 readings•Total 30 minutes
- Introduction•10 minutes
- Penn Treebank•20 minutes
1 assignment•Total 10 minutes
- Data Quality and Its Role in AI and LLM Development•10 minutes
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
1 video•Total 1 minute
- Technical Requirements - Overview Video•1 minute
1 reading•Total 40 minutes
- Technical Requirements - The Reading•40 minutes
1 assignment•Total 10 minutes
- Correlation, Causation, Bias, and Variance in AI and Cybersecurity•10 minutes
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
1 video•Total 1 minute
- Evaluation, Monitoring, and Feedback Loop - Overview Video•1 minute
4 readings•Total 60 minutes
- Introduction•20 minutes
- Cross-validation•10 minutes
- Monitoring During Testing or Production•20 minutes
- Human in the Loop•10 minutes
1 assignment•Total 10 minutes
- Model Evaluation and Performance Analysis•10 minutes
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.
What's included
1 video3 readings1 assignment
1 video•Total 1 minute
- Current Challenges in AI Security - Overview Video•1 minute
3 readings•Total 60 minutes
- Introduction•20 minutes
- Impact on Individual Privacy•20 minutes
- Tools and Technologies•20 minutes
1 assignment•Total 10 minutes
- Responsible AI and Ethical Considerations•10 minutes
In this section, we summarize AI and ML concepts, connect the previous sections, and highlight real-world successes.
What's included
1 video1 reading1 assignment
1 video•Total 1 minute
- Summary - Overview Video•1 minute
1 reading•Total 30 minutes
- Summary - The Reading•30 minutes
1 assignment•Total 10 minutes
- AI and Cybersecurity Fundamentals•10 minutes
Instructor
Explore more from Security
- Status: Preview2
28DIGITAL
Course
- Status: Free TrialJ
Johns Hopkins University
Course
- Status: Free Trial
Course
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