Description
Cybersecurity problems and challenges. Supervised and unsupervised learning. Classification and regression algorithms. Performance assessment in cybersecurity domains. Techniques for imbalanced domains. Anomaly detection. Active learning strategies. Cybersecurity of data streams. ML vulnerability. NLP for cybersecurity. Malware, intrusion, credit card fraud detection, phishing, authentication abuse prevention.
Prerequisites
- Prerequisite(s): There are no official prerequisites for this course. However, students should have reasonable exposure to Artificial Intelligence, Machine Learning, and some programming experience in a high-level language.
Conditions and arrangements
- Prerequisite(s): There are no official prerequisites for this course. However, students should have reasonable exposure to Artificial Intelligence, Machine Learning, and some programming experience in a high-level language.
Reference text in its original language
Cybersecurity problems and challenges. Supervised and unsupervised learning. Classification and regression algorithms. Performance assessment in cybersecurity domains. Techniques for imbalanced domains. Anomaly detection. Active learning strategies. Cybersecurity of data streams. ML vulnerability. NLP for cybersecurity. Malware, intrusion, credit card fraud detection, phishing, authentication abuse prevention.
- Prerequisite(s): There are no official prerequisites for this course. However, students should have reasonable exposure to Artificial Intelligence, Machine Learning, and some programming experience in a high-level language.
Sources and references
Dates and sources are retained to help you verify the information. Translations are provided to facilitate reading; the official source governs conditions and requirements.
Source reference : https://calendar.carleton.ca/grad/courses/COMP/