Cyber AI Hunter

Analyzing Cyber Threats and Attacks

Information Security Basics 4.7(57)4h 57m21 lectures2 sections

What you'll learn

Understand the fundamentals of artificial intelligence in cybersecurity
Get to know cybersecurity data and feature engineering
Use machine learning to detect threats
Analyze behavior and detect anomalies
Understand the Threat Hunting methodology
Apply incident response steps
Complete practical exercises in every topic

About this course

This course shows you how to use artificial intelligence in cybersecurity to detect threats, hunt them, and defend against them. It begins with an introductory lecture, then the fundamentals of artificial intelligence in cybersecurity, Cyber AI Foundations, with a practical exercise. After that you learn cybersecurity data and feature engineering (Security Data and Features), then machine learning for detecting threats (ML Threat Detection), and Behavioral and Anomaly Analysis. You move on to the Threat Hunting methodology, and finally Incident Response, with each part followed by a practical exercise. The course ends with a final exam and a workbook. It is useful for people interested in cybersecurity, SOC teams, and defenders, and it is best if you have some basic knowledge of security and networks. By the end, you will be able to understand how artificial intelligence supports detecting abnormal behavior, analyzing security data, and following incident response steps, with protection as the goal.

Expected outcomes

Ability to detect cyber threats using artificial intelligence techniques.
Practical understanding of how to analyze security data within SOC environments.
Preparedness to work in Threat Hunting and Cyber Defense Teams.
Implement intelligent incident responses to reduce breach time and impact.

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Course content

Cyber AI Hunter

17 lectures

Course IntroIntroduces the Cyber AI course combining AI with cybersecurity for attack, defense and automation, covering lab requirements like VirtualBox and Kali Linux, real data sources, and a thinking-first approach over memorizing tools.10:36
Cyber AI Foundations (Part 1)Lays the foundations of AI in cybersecurity, showing the limits of traditional rule-based security, the evolution of threats, the data and logs explosion problem, and how learning detects abnormal behavior.20:08
Cyber AI Foundations (Part 2)Continues Cyber AI foundations stressing speed and proactivity, what AI does not do, the difference between Automation and AI, the Human in the Loop model, and its role in reducing alert noise in a SOC.22:09
Cyber AI LabRuns the first hands-on lab on Kali Linux by creating lab folders, downloading a data file and verifying it with commands like wc and sha, then doing an initial inspection of its contents and finding the most frequent messages.30:37
Security Data & Features (Part 1)Explains Feature Engineering, which turns raw data and logs into signals a model can learn from, security data sources like network, identity and application logs, quality problems, and examples such as login frequency.17:33
Security Data & Features (Part 2)Continues Feature Engineering with examples like time-based patterns and connection diversity, combining behavior with signatures, the class imbalance problem due to rare attacks, and common mistakes like copying features without checking context.20:15
Security Data LabRuns the second lab on security logs, turning them into useful features with commands like grep, understanding value distributions, indicators such as byte rate, and telling good features from bad ones before building a learning model.17:10
ML Threat Detection (Part 1)Explains using machine learning for threat detection, compared with signature-based detection, the balance between Precision and Recall, supervised and semi-supervised learning, and comparing models like Random Forest and neural networks.15:37
ML Threat Detection (Part 2)Continues machine learning threat detection covering feature importance and explainability, ROC AUC and PR AUC metrics, the cost of false positives, analyst feedback loops, and operational risks and model maintenance.17:23
Behavioral & Anomaly AnalysisExplains behavioral analytics and anomaly detection by building a baseline of normal behavior for users, endpoints and the network, spotting deviations, types of anomalies such as valid logins at odd times or places, and that a change can be legitimate.26:29
Behavioral Analysis LabA hands-on Kali Linux lab on building a behavioral baseline from real data, detecting anomalies without relying on signatures, and telling real anomalies from legitimate behavior to cut false positives using context.11:50
Threat Hunting (Part 1)Explains how correlation links scattered events into one picture, the limits of manual correlation and the role of AI, the levels of threat intelligence, and why behavior is harder for attackers to change than IPs or hashes.13:21
Threat Hunting (Part 2)Continues the AI-driven correlation approach for detecting multi-phase campaigns instead of isolated alerts, warns about biased or incomplete training data, and reviews the module quiz questions.16:47
Threat Hunting LabA hands-on lab on enriching security events with threat intelligence, correlating alerts from different sources into one incident, and mapping observed behavior to the MITRE ATT&CK framework, with the human analyst keeping the final decision.11:40
Incident ResponsePresents incident response as a race against time and how AI helps rank incidents by risk, add context, build timelines and suggest actions, while noting the risks of over-trust and keeping the human as decision maker.23:48
Incident Response LabA hands-on lab on using AI output for incident triage, prioritization and response planning on Kali Linux, covering confidence levels, the danger of over-trust, and the course takeaway that humans approve before any automation.12:50
Final ExamWalks through the final exam questions, including the definition of a behavioral baseline, why not every anomaly is a security incident, the value of MITRE ATT&CK, why behavior and context beat signatures, and the course closing messages.9:40