AI & Machine Learning in Cyber Threat Detection

Executive Overview

As cyberattacks become more sophisticated and large-scale, traditional rule-based security systems struggle to keep up. Artificial Intelligence (AI) and Machine Learning (ML) are transforming the cybersecurity landscape by enabling predictive, adaptive, and automated threat detection. This 5-day enterprise training program is designed to empower cybersecurity and data professionals to harness AI and ML for identifying anomalies, detecting intrusions, and mitigating cyber threats in real time. Participants will gain a deep understanding of ML models, feature engineering for security datasets, and the integration of AI-driven tools within SOC (Security Operations Center) environments. The program bridges cybersecurity, data science, and automation, preparing teams to build next-generation threat detection systems that learn and evolve continuously.

Objectives of the Training

Understand the fundamentals of AI and ML applications in cybersecurity.
Learn to preprocess and analyze security logs, network traffic, and event data for threat detection.
Develop ML models for intrusion detection, anomaly detection, and malware classification.
Gain hands-on experience with AI-powered SOC tools and SIEM integrations.
Implement supervised, unsupervised, and deep learning techniques in cyber threat analytics.
Explore AI-driven automation, predictive defense, and adaptive security systems.

Prerequisites

Basic understanding of cybersecurity concepts and network protocols.
Familiarity with Python programming and data analysis.
Foundational knowledge of machine learning or data science is beneficial but not mandatory.

What You Will Learn

Fundamentals of AI and ML techniques applied to cybersecurity.
Data preprocessing, feature selection, and anomaly detection in security data.
Building and evaluating ML models for malware detection and phishing identification.
Deep learning architectures for behavioral analytics and network threat modeling.
Integration of ML with SIEM tools and real-time alerting systems.
Emerging trends in predictive security and AI-driven SOC automation.

Target Audience

This course is designed for Cybersecurity Analysts, SOC Engineers, Data Scientists, and IT Security Managers interested in leveraging AI and ML for proactive threat detection. It is also suitable for professionals in DevSecOps, risk management, and incident response teams looking to modernize their detection and response capabilities.

Detailed 5-Day Curriculum

Day 1 – AI and Machine Learning for Cybersecurity Foundations (6 Hours)

Session 1: Introduction to AI/ML in Cybersecurity – Evolution, Use Cases, and Benefits.
Session 2: Understanding Cyber Threats and Attack Vectors – Data Perspective.
Session 3: Overview of Machine Learning Types – Supervised, Unsupervised, and Reinforcement Learning.
Hands-on: Setting up the AI Cybersecurity Lab (Python, Scikit-learn, TensorFlow, ELK).

Day 2 – Security Data Processing and Feature Engineering (6 Hours)

Session 1: Working with Security Logs, Network Traffic, and SIEM Data.
Session 2: Data Cleaning, Normalization, and Feature Extraction for Threat Detection.
Session 3: Dimensionality Reduction Techniques – PCA, t-SNE for Anomaly Detection.
Workshop: Building a Dataset for Intrusion Detection Using Open-Source Network Data.

Day 3 – ML Models for Threat Detection and Classification (6 Hours)

Session 1: Implementing Supervised Models – Decision Trees, Random Forest, SVM for Attack Detection.
Session 2: Unsupervised Learning – Clustering, Isolation Forest, and Autoencoders for Anomaly Detection.
Session 3: Evaluating Model Performance – Accuracy, Precision, Recall, and ROC Curves.
Hands-on: Building an Intrusion Detection Model Using KDD or UNSW-NB15 Dataset.

Day 4 – Deep Learning and AI in SOC Automation (6 Hours)

Session 1: Deep Learning for Cybersecurity – CNNs, RNNs, and LSTMs for Behavioral Analysis.
Session 2: AI-Powered SOCs – Integrating ML Models into SIEM (Splunk, ELK, QRadar).
Session 3: Automating Threat Detection Using SOAR and AI Playbooks.
Workshop: Developing a Deep Learning Model to Classify Malware Samples.

Day 5 – Predictive Threat Intelligence and Capstone Project (6 Hours)

Session 1: Predictive Defense – Leveraging Threat Intelligence and ML Insights.
Session 2: Capstone Project – Building an AI-Driven Threat Detection Framework for an Enterprise Network.
Session 3: Future Trends – Generative AI, Federated Learning, and Autonomous SOCs.
Panel Discussion: AI Ethics and Responsible AI in Cybersecurity.

Capstone Project

Participants will design and implement an AI-powered threat detection system capable of identifying network anomalies and suspicious activities. The project includes data collection, model development, training, and integration with a simulated SOC dashboard. Teams will showcase how AI can enhance threat visibility, detection speed, and response accuracy.

Future Trends in AI and Machine Learning for Cybersecurity

The future of cybersecurity lies in adaptive and predictive AI systems. Emerging trends include the integration of Generative AI for threat simulation, reinforcement learning for autonomous defense, and graph neural networks for advanced attack path detection. AI-driven SOCs will evolve into self-healing ecosystems capable of continuous learning and real-time protection. Organizations that integrate AI across their defense layers will gain a competitive edge in cyber resilience and operational agility.