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AI in Cybersecurity Over

Ongoing Registration
Registration Fees
£450
Mode
Online

Course Description

Course Description

GIRSD’s AI in Cybersecurity Course offers a deep dive into the integration of artificial intelligence with core cybersecurity practices. This course is structured to deliver a strong foundation in AI technologies while applying them to real-world security challenges. You’ll gain both theoretical understanding and hands-on expertise in using intelligent systems to detect, analyse, and respond to cyber threats. Learn how AI is revolutionising threat detection, incident response, and predictive defence to stay ahead in an ever-evolving digital landscape.

Technologies Covered in This Course

TensorFlow is a leading open-source machine learning framework used to build and train AI models. In this course, you’ll use TensorFlow to create systems capable of recognising patterns and anomalies in cybersecurity data.

Scikit-learn provides simple yet powerful tools for data analysis and machine learning. You’ll use it for implementing classification, regression, and clustering models tailored to cyber threat identification.

Keras is a user-friendly deep learning API built on top of TensorFlow. It allows you to rapidly prototype neural networks for tasks such as intrusion detection and malware classification.

Pandas & NumPy are essential libraries for data manipulation and numerical analysis. They play a critical role in preparing, cleaning, and structuring cybersecurity data for model training and evaluation.

ELK Stack (Elasticsearch, Logstash, Kibana) is a powerful platform for log aggregation, search, and visualisation. You’ll learn how to use it to monitor network activity, identify anomalies, and detect potential intrusions.

Snort + AI Integration teaches you how to extend traditional intrusion detection systems with machine learning capabilities to increase accuracy and reduce false positives.

Hugging Face Transformers provide pre-trained natural language models that can be fine-tuned for cybersecurity applications, including phishing detection and threat intelligence analysis.

Jupyter Notebooks offer an interactive environment for writing and visualising machine learning workflows. You’ll use them to document your process and build explainable AI security solutions.

OpenCV enables image and video analysis using AI-perfect for tasks like visual anomaly detection in CCTV feeds or security footage.

Course Units

Course Units

Unit 1: Introduction to AI in Cybersecurity

Unit 2: Machine Learning Fundamentals for Cybersecurity

Unit 3: Threat Detection using AI

Unit 4: Natural Language Processing (NLP) for Cybersecurity

Unit 5: Tools and Technologies in AI Cybersecurity

Prerequisites

  • No prior programming knowledge
  • Ability to work in Group
  • Have access to personal laptop

Course Duration & Online Support

  • 8 Hours (4 weeks)
  • Online Sessions & Self paced
  • Project-based Learning
  • Capstone Project
  • Real world development experience
  • Interactive Teaching Methodologies

Assessment

  • 18 Coding exercises
  • 5 Assignments
  • 5 Quizzes
  • Capstone Project
  • Presentations

Certification

Course Details

• Overview of AI and machine learning in cybersecurity
• Key concepts and terminology
• Evolution of cyber threats and AI defence strategies
• Basics of machine learning algorithms
• Data preprocessing and feature engineering
• Supervised vs unsupervised learning in security contexts
• Anomaly detection techniques
• Behavioural analysis with machine learning
• AI-driven intrusion detection systems (IDS)
• Using NLP for threat intelligence and phishing detection
• Fine-tuning transformer models (BERT, GPT)
• Analysing security logs and alerts with NLP
• Hands-on with TensorFlow, Scikit-learn, ELK Stack
• Integration of AI models with security infrastructure
• Ethical considerations and AI governance in cybersecurity

Description

GIRSD’s AI in Cybersecurity Course offers a deep dive into the integration of artificial intelligence with core cybersecurity practices. This course is structured to deliver a strong foundation in AI technologies while applying them to real-world security challenges.