Machine Learning for Network Security and Anomaly Detection Training Course

Module 1: Foundations of Network Security and Machine Learning

• Evolution of network defense: From firewalls to behavioral AI
• Overview of the ML pipeline: Data collection, preprocessing, modeling, and evaluation
• Introduction to the cybersecurity data science toolkit: Python, Scikit-learn, and Pandas
• Defining performance metrics in security: Precision, Recall, and the ROC curve
• Understanding the "Black Box" challenge in automated security decisions
• Practical session: Setting up an AI-ready environment and performing exploratory data analysis on a network traffic dataset

Module 2: Data Engineering for Network Telemetry

• Working with raw network data: PCAP, NetFlow, and IPFIX
• Techniques for cleaning and normalizing high-volume streaming data
• Feature engineering: Extracting packet-level and flow-level statistical features
• Handling imbalanced datasets: Dealing with the rarity of malicious traffic
• Dimensionality reduction: Managing large-scale data with PCA and t-SNE
• Practical session: Building a preprocessing pipeline to convert raw PCAP files into a structured feature set for ML

Module 3: Supervised Learning for Traffic Classification

• Implementing Logistic Regression and Decision Trees for baseline traffic labeling
• Utilizing Random Forests and XGBoost for high-accuracy protocol identification
• Differentiating between benign applications and encrypted malicious channels
• Multi-class classification: Identifying specific attack types (Probing, R2L, U2R)
• Cross-validation techniques to ensure model stability across different network segments
• Practical session: Developing a classification model to distinguish between standard web traffic and C2 (Command & Control) callbacks

Module 4: Unsupervised Learning and Anomaly Detection

• Introduction to "Zero-Day" detection: Finding patterns without labels
• Implementing K-Means and DBSCAN for discovering unknown traffic clusters
• One-Class SVMs and Isolation Forests for outlier detection in network flows
• Statistical methods for identifying deviations from "Normal" network baselines
• Scoring anomalies: Distinguishing between network misconfigurations and malicious acts
• Practical session: Training an Isolation Forest model to detect unauthorized port scanning within a corporate network

Module 5: Deep Learning for Network Packet Inspection

• Fundamentals of Neural Networks for high-dimensional security data
• Using Convolutional Neural Networks (CNNs) on raw packet payloads
• Recurrent Neural Networks (RNNs) and LSTMs for analyzing sequences of network events
• Autoencoders for network reconstruction: Identifying subtle deviations in packet headers
• Introduction to Self-Supervised Learning for network representation
• Practical session: Implementing an Autoencoder to detect anomalies in encrypted traffic streams without decryption

Module 6: Detecting Denial of Service (DoS) and Botnets

• Characterizing DoS/DDoS patterns: Volumetric vs. application-layer attacks
• Time-series analysis for detecting traffic spikes and periodic botnet communication
• Using ensemble methods to identify coordinated botnet activity across multiple hosts
• Real-time monitoring: Applying ML to high-speed packet streams for instant mitigation
• Managing false positives during legitimate traffic surges (The "Flash Crowd" problem)
• Practical session: Building a real-time detector for SYN flood attacks using streaming statistical features

Module 7: Behavioral Analytics and Insider Threat Detection

• Modeling User and Entity Behavior Analytics (UEBA) through network logs
• Identifying credential theft: Detecting unusual login patterns and data exfiltration
• Profiling "Normal" behavior for service accounts and automated systems
• Detecting beaconing behavior: Identifying compromised internal hosts
• Correlating network events with host-based telemetry for holistic detection
• Practical session: Developing a behavioral model to identify a "low and slow" data exfiltration attempt

Module 8: Graph-Based Analysis for Lateral Movement

• Representing network connections as a graph: Nodes, edges, and topologies
• Using Graph Neural Networks (GNNs) to identify suspicious pathing in a network
• Detecting lateral movement: Finding unusual hops between internal workstations
• Identifying "Choke Points" and high-risk nodes in an enterprise architecture
• Community detection: Finding clusters of compromised machines within a network
• Practical session: Applying a graph-based algorithm to identify a lateral movement path within a simulated Active Directory environment

Module 9: Adversarial Machine Learning in Network Defense

• Understanding the vulnerability of security models: Evasion and poisoning attacks
• How attackers use AI to bypass ML-based firewalls and IDSs
• Defensive distillation and adversarial training: Hardening models against manipulation
• Evaluating model robustness against obfuscated and encrypted payloads
• Securing the ML pipeline: Protecting training data and model weights from tampering
• Practical session: Attempting to bypass a pre-trained traffic classifier using adversarial perturbation techniques

Module 10: Operationalizing and Deploying ML Models in Production

• Architectures for real-time inference: From the edge to the centralized SOC
• Monitoring model drift: Detecting when network patterns change over time
• Integrating ML alerts with existing SIEM/SOAR platforms
• The "Human-in-the-Loop" requirement: Designing interfaces for security analysts
• Building a long-term AI strategy and roadmap for network security
• Practical session: Deploying a trained detection model as a REST API and creating a real-time monitoring dashboard

Requirements:

• Participants should be reasonably proficient in English.
• Applicants must live up to Phoenix Training Center admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@phoenixtrainingcenter.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@phoenixtrainingcenter.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@phoenixtrainingcenter.com or on Tel: +254720272325 / +254725012095 / +254724452588