Introduction

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the cybersecurity landscape, offering cutting-edge solutions for malware detection and classification. With the explosive growth of sophisticated cyber threats, conventional antivirus techniques are no longer sufficient. Training Course on AI ML for Malware Detection and Classification is designed to equip cybersecurity professionals, data scientists, and IT leaders with the advanced skills necessary to implement AI/ML-based malware detection techniques. Through hands-on labs, real-world case studies, and theoretical foundations, participants will gain actionable insights into threat modeling, neural network development, and anomaly detection.

This program offers comprehensive coverage of deep learning algorithms, supervised and unsupervised learning methods, malware feature extraction, and real-time threat prediction. With a focus on modern attack vectors, polymorphic malware, and evasion techniques, this training provides essential knowledge and tools for defending enterprise networks. By the end of the course, attendees will be able to design and deploy intelligent threat detection systems capable of identifying and classifying known and unknown malware in real-time environments.

Programme Curriculum

Training Course on AI/ML for Malware Detection and Classification

Introduction

Course Objectives

Understand the fundamentals of AI and machine learning in cybersecurity.
Learn the taxonomy and classification of malware types and behaviors.
Develop ML-based models for signatureless malware detection.
Apply supervised, unsupervised, and reinforcement learning to malware analysis.
Extract and engineer features from malware datasets.
Analyze datasets using tools like VirusShare, VirusTotal, and EMBER.
Use neural networks, SVM, and deep learning models for malware classification.
Detect zero-day threats using anomaly-based ML algorithms.
Evaluate model performance using precision, recall, and confusion matrix.
Understand evasion tactics and adversarial ML in malware detection.
Leverage AutoML tools for rapid model prototyping.
Implement scalable threat detection pipelines in cloud-based environments.
Conduct malware forensics using AI-assisted classification models.

Target Audiences

Cybersecurity Analysts
SOC Engineers
Malware Researchers
AI/ML Engineers
Threat Intelligence Specialists
IT Security Managers
Penetration Testers
Incident Response Teams

Course Duration: 10 days

Course Modules

Module 1: Introduction to AI and Machine Learning in Cybersecurity

Overview of AI/ML fundamentals
Cybersecurity landscape and malware evolution
Importance of intelligent threat detection
AI/ML roles in SOC environments
Tools and platforms used
Case Study: Evolution of malware detection from 2000s to present

Module 2: Malware Fundamentals and Classification

Types of malware (worms, trojans, ransomware, etc.)
Static vs dynamic analysis
Malware behaviors and attack vectors
Obfuscation and polymorphism
Real-time detection challenges
Case Study: WannaCry ransomware behavior analysis

Module 3: Feature Extraction from Malware

Importance of feature engineering
PE headers, opcodes, API calls
Static feature extraction tools
Dynamic analysis and sandboxing
Labeling and preprocessing datasets
Case Study: Feature selection for EMBER dataset

Module 4: Supervised Learning for Malware Detection

Classification algorithms overview
Decision trees, random forests, SVMs
Labeling malware datasets
Training and validation
Overfitting and model tuning
Case Study: SVM model for classifying ransomware

Module 5: Unsupervised Learning Techniques

Clustering malware families
Dimensionality reduction (PCA, t-SNE)
Outlier detection
Similarity detection
Visualizing clustering results
Case Study: Uncovering malware variants with K-means

Module 6: Deep Learning for Malware Analysis

CNNs and RNNs for sequence data
Handling binary files as images
LSTM for time-series behavior
Transfer learning for malware datasets
Building deep nets with TensorFlow/PyTorch
Case Study: Image-based CNN malware classifier

Module 7: Anomaly Detection and Zero-Day Threats

Anomaly detection methods
Gaussian models and One-Class SVM
Autoencoders and reconstruction errors
Behavioral anomaly modeling
Identifying zero-day exploits
Case Study: Detecting zero-day with unsupervised autoencoders

Module 8: Adversarial Machine Learning

Adversarial example generation
Evasion attacks on classifiers
Robust model training
Defense strategies against adversarial inputs
Model interpretability techniques
Case Study: Evasion of deep learning malware classifier

Module 9: Malware Datasets and Benchmarking

Public malware datasets overview
Dataset curation and challenges
Balancing and cleaning data
Benchmarking and metrics
Dataset licensing and ethics
Case Study: Comparative analysis using EMBER and Malimg

Module 10: Evaluating AI/ML Models

Confusion matrix, F1-score, ROC
Cross-validation and grid search
Real-time performance monitoring
Explainable AI techniques
Debugging underperforming models
Case Study: Performance evaluation of CNN vs Random Forest

Module 11: AutoML in Malware Detection

What is AutoML and its benefits
Tools: Google AutoML, H2O.ai, TPOT
Configuring AutoML pipelines
Feature selection automation
Hyperparameter optimization
Case Study: Rapid model development using H2O AutoML

Module 12: Malware Detection in Cloud Environments

Cloud-native security challenges
Using AI in AWS/Azure/GCP environments
CI/CD for security ML models
Streaming data ingestion for detection
Cloud logging and analysis
Case Study: Deploying AI-based detection in AWS Lambda

Module 13: Threat Intelligence and AI Integration

Threat intelligence platforms (TIPs)
Integrating ML with TI feeds
IOC extraction and enrichment
Real-time alerting with AI logic
Threat hunting automation
Case Study: Enhancing TI with ML-based enrichment

Module 14: Explainable and Ethical AI in Malware Analysis

Need for explainable AI in cybersecurity
Tools for model explainability (SHAP, LIME)
Ethical concerns in malware data use
AI bias in classification
Regulatory and privacy considerations
Case Study: Ethical implications of malware model deployment

Module 15: Capstone Project and Practical Lab

Full project: Design malware detection pipeline
Dataset selection and labeling
Model training, tuning, and evaluation
Deployment on a simulated environment
Presentation and peer review
Case Study: End-to-end pipeline using real malware samples

Training Methodology

Interactive lectures with industry experts
Hands-on labs and exercises with real datasets
Group discussions and live Q&A sessions
Access to cloud-based virtual labs
Real-world case studies and capstone projects
Course certification upon completion

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

ML frameworks (TensorFlow PyTorch) anomaly detection data labeling

Programme Fee

Duration: 10 Days

Method: Online or Physical

Online Price: $2,000

Physical Price: $3,000

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 30 Oct 2026

💻 Online / Virtual session

Online $2,000