To build a strong foundation in understanding and identifying cyber threats and security principles, preparing learners for advanced threat detection using AI, ethical hacking, and automation tools.

• Understand foundational concepts in cybersecurity
• Cybersecurity Principles: CIA Triad, Risk Management
• Types of Threats: Malware, Ransomware, Phishing, DDoS
• OS & Network Security Basics
• Firewalls, IDS, IPS, VPN
• OWASP Top 10 Vulnerabilities
• Case Studies of Famous Attacks

To build a strong foundation in using Python for automating cybersecurity tasks and understanding core AI concepts, preparing learners for applying machine learning and data-driven techniques in threat detection and analysis.

• Learn scripting to automate security tasks and understand AI basics
• Python for Security: File I/O, Regex, Network Sniffing
• Log Parsing and Analysis
• Packet Inspection using Scapy
• Intro to AI/ML: Supervised/Unsupervised Learning, Workflow
• Data Preprocessing & Feature Engineering
• Log File Analyzer or Packet Sniffer Script

To build a foundational understanding of how machine learning can be applied to detect and analyze cyber threats, enabling students to apply data-driven techniques and algorithms for threat identification, anomaly detection, and cybersecurity automation.

• Use machine learning to detect cyber threats and anomalies
• Data Collection & Labeling
• Supervised Models: Logistic Regression, Random Forest, SVM
• Unsupervised Models: Clustering for anomaly detection
• Feature extraction from logs and network flows
• Model Evaluation: Confusion Matrix, ROC-AUC, Precision/Recall
• ML-based IDS for detecting anomalies or intrusions

To develop foundational skills in applying deep learning and natural language processing (NLP) techniques to cybersecurity tasks, enabling students to analyze threat intelligence, detect phishing, and process unstructured data such as logs and security reports for enhanced threat detection and response.

• Apply deep learning and NLP for threat classification
• NLP: Tokenization, TF-IDF, Word2Vec
• Email Analysis: Phishing/Spam Detection using NLP
• CNN for Malware Classification using bytecode or images
• RNNs/LSTMs for log sequence modeling
• Transfer Learning for security tasks
• Phishing Detector or Malware Classifier using DL

To equip students with a practical understanding of ethical hacking principles and how AI-driven tools can enhance threat hunting processes, enabling them to proactively identify vulnerabilities, simulate cyberattacks, and apply intelligent automation to detect advanced threats.

• Understand ethical hacking methodologies and AI-driven hunting
• Penetration Testing Phases: Recon, Scanning, Gaining Access
• Tools: Nmap, Metasploit, Wireshark
• Threat Intelligence Sources & Feeds
• AI for Threat Correlation & Automation
• Blue Team vs Red Team Tactics
• Simulated Pen Test Report + Automated Threat Scanner

To integrate and apply the knowledge and skills gained throughout the course by designing and implementing a practical cybersecurity solution using machine learning, deep learning, or AI-based methods—demonstrating students’ ability to solve real-world cyber threat challenges through data-driven approaches.

• Implement an AI-driven solution to detect and respond to threats
• AI-Powered SIEM Dashboard
• Smart Phishing Detection Platform
• AI-based Insider Threat Detection Tool
• End-to-End Network Anomaly Detector

To demonstrate the ability to apply machine learning, deep learning, and AI-based techniques to real-world cybersecurity challenges by designing, developing, and presenting a comprehensive project that showcases problem-solving, technical implementation, and analytical skills gained throughout the course.

• Functional Capstone Tool
• Project Report + Code Repo + Presentation