LSET’s Machine Learning Threat Detection Engineer Course offers a specialised deep dive into the intersection of artificial intelligence and cyber security operations. This course is designed to equip professionals with both foundational knowledge and advanced, hands-on skills for identifying, analysing, and mitigating cyber threats using cutting-edge AI techniques.
You will explore how machine learning models can be applied to malware and phishing detection, automate incident response processes, and enhance your overall threat intelligence capabilities. With a strong focus on practical implementation, this course prepares you to tackle today’s most pressing security challenges using AI-driven solutions.
Technologies & Tools Covered in This Course
VirusShare – A popular repository of real-world malware samples used to train and test machine learning models for malware classification and behavioural analysis.
PhishTank – A collaborative clearing house for data and information about phishing websites. You’ll learn how to leverage this dataset for building AI models that detect and flag phishing attacks.
URLNet – A deep learning-based dataset and model framework designed to detect malicious URLs using URL string patterns, often employed in phishing and spam detection.
Scikit-learn & TensorFlow – Core machine learning frameworks used to build, train, and evaluate models for threat detection. You’ll use these to create custom classifiers and anomaly detectors.
Natural Language Processing (NLP) – Applied in the analysis of phishing emails and messages. Techniques such as tokenisation, TF-IDF, and transformer models are used to identify linguistic patterns in fraudulent content.
Convolutional Neural Networks (CNNs) – Used for detecting obfuscated malware by analysing visual patterns in binary or assembly code converted into images, a cutting-edge approach in static malware analysis.
Jupyter Notebooks – A flexible and interactive development environment where you’ll experiment with datasets, visualisations, and model prototypes for threat detection.
Elastic Stack (ELK) – Used for log aggregation, threat visualisation, and incident tracking. You’ll learn how to integrate AI models with real-time monitoring systems.
OpenAI & GPT Models – Explore how large language models (LLMs) can support incident triage, threat classification, and report generation in modern SOC environments.
Career Accelerator Program
Premium Career-Ready Track
Note: Please note that all prices listed are exclusive of VAT. VAT will be charged separately and added to the total amount payable.
