AI Academy

Introduction to Intrusion Detection Systems (IDS)

Instructor: Jason Crossland Duration: 51 hours to complete 3 weeks at 17 hours a week
Objective 1 Understand the roles of HIDS and NIDS, their key components, and applications across cybersecurity contexts.
Objective 2 Install and configure IDS solutions on VMs to detect real-time threats in host and network environments.
Objective 3 Compare signature-based and anomaly-based detection methods and configure IDS rules accordingly.
Objective 4 Use quantitative techniques to assess IDS effectiveness, analyzing data to select optimal solutions for security needs.
Threat Management
Intrusion Detection and Prevention
System Monitoring
Virtual Machines
Network Security
Endpoint Detection and Response
Anomaly Detection
Threat Detection
Continuous Monitoring
Network Analysis
Network Monitoring
Event Monitoring
Distributed Denial-Of-Service (DDoS) Attacks

Advanced Network Analysis and Incident Response

Instructor: Jason Crossland Duration: 46 hours to complete 3 weeks at 15 hours a week
Objective 1 Understand the differences between network situational awareness and traditional NIDS for effective incident detection.
Objective 2 Gain proficiency in using GOTS and COTS tools for network packet analysis and troubleshooting networking challenges.
Objective 3 Learn to conduct ROC analysis on IDS data and interpret event graphs and precision-recall metrics for better decision-making.
Objective 4 Explore the NIST Cybersecurity Framework and SANS Incident Response Cycle to effectively manage and respond to cyber incidents.
NIST 800-53
Network Analysis
Network Monitoring
Cyber Threat Intelligence
Computer Security Incident Management
Cloud Security
Network Security
Anomaly Detection
Intrusion Detection and Prevention
Incident Response
Cyber Security Strategy
Threat Detection

Machine Learning and Emerging Technologies in Cybersecurity

Instructor: Jason Crossland Duration: 4 weeks to complete at 10 hours a week
Objective 1 Explore advanced machine learning techniques, including neural networks and clustering, for improved threat detection in cybersecurity.
Objective 2 Understand the integration of machine learning algorithms into Intrusion Detection Systems (IDS) for enhanced security measures.
Objective 3 Gain knowledge of The Onion Router (ToR) architecture and its applications, focusing on privacy and anonymous communication.
Objective 4 Learn to utilize Security Onion tools for effective incident response within high-volume enterprise environments, enhancing cybersecurity strategy.
Network Security
Applied Machine Learning
Machine Learning Algorithms
Cybersecurity
Intrusion Detection and Prevention
Artificial Neural Networks
Computer Security Incident Management
Supervised Learning
Machine Learning
Threat Detection
Incident Response
Unsupervised Learning
Deep Learning