Cyber Security with AI

Goal: Apply ML to detect anomalous activities.
• Feature engineering from logs, traffic & security events
• Classification & clustering for threat detection
• Supervised models: SVM, Random Forest, Logistic Regression
• Unsupervised models: K-Means, Isolation Forest
• Model evaluation metrics (Precision, Recall, ROC AUC)

Goal: Explore advanced neural approaches.
• CNN/RNN/LSTM for malware & traffic anomaly detection
• Autoencoders for anomaly detection
• Transfer learning & embeddings for security data
• Using AI frameworks: TensorFlow, PyTorch, Keras

Goal: Detect threats at scale and in real-time.
• AI-powered IDS/IPS
• Behavioral analytics & pattern recognition
• UEBA analytics (User & Entity Behavior Analytics)
• Zero-day detection with ML baselines
• Threat scoring & prioritization

Goal: Automate incident lifecycle with intelligence.
• SOC workflows & AI-augmented triage
• SOAR integrations for automated playbooks
• Threat hunting with ML insights
• Forensic analysis assisted by AI
• Case studies & simulation labs

Goal: Secure networks & cloud with smart automation.
• Anomaly detection in NetFlow data
• AI for cloud security (IAM, policies, misconfigurations)
• Real-time DDoS detection & mitigation
• IoT & API security analytics

Goal: Use NLP to analyze security text data.
• Spam & phishing detection with NLP
• Threat intelligence extraction from feeds
• Dark web monitoring with text analytics
• Prompt engineering for security task automation

Goal: Learn attack vectors on AI and defenses.
• Adversarial attacks: evasion, poisoning, model theft
• OWASP/ATLAS threats to AI systems
• Defensive AI techniques & robust modeling
• Governance, bias, explainability & ethics

Goal: Hands-on experiments with real tooling.
• Python toolchains: Scikit-Learn, Pandas, NumPy, Matplotlib
• Jupyter & Google Colab practical sessions
• SIEM & AI analytics (Splunk/ELK with MLplug-ins)
• Cloud SDKs (AWS/GCP/Azure) for security automation
• AutoML & low-code AI-security platforms

Goal: Integrate and deploy a full AI-based security solution.
Project Themes (Choose 1–2):
• AI-Based SOC Response Workflow
• Automated Malware Classifier
• UEBA for Insider Threat Detection
• GenAI Assistant for Security Operations
• Cloud Misconfiguration Detector
Each capstone includes design, implementation, evaluation, and deployment
documentation.