Machine Learning Threat Detection Engineer

Welcome to LSET’s Machine Learning Threat Detection Engineer – your gateway to mastering modern cyber defence strategies using artificial intelligence. This specialised programme is designed to equip security professionals, malware analysts, and developers with the advanced skills needed to identify, analyse, and respond to threats in real time.

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.