Chapter 3 – AI‑Driven Threat Detection

3.1 The Threat‑Detection Pipeline

  1. Data Collection – Logs, network flows, endpoint telemetry, and threat‑intel feeds are ingested into a central store (Elasticsearch, PostgreSQL, or a vector database).
  2. Pre‑processing & Normalization – Structured logs are parsed, timestamps aligned, and categorical fields encoded.
  3. Feature Extraction – Textual fields (e.g., error messages, command lines) are embedded with Sentence‑Transformers; numeric fields are scaled.
  4. Model Scoring – A trained model (e.g., an autoencoder or a fine‑tuned LLM) assigns a risk score to each event.
  5. Enrichment – Scores are cross‑checked against known indicators of compromise (IOCs) from OpenCTI and enriched with threat‑intel feeds.
  6. Alert Generation – High‑score events trigger alerts in Elastic SIEM or a custom dashboard.
  7. Feedback Loop – Analysts label alerts; the labels feed back into the model for continual improvement.

3.2 Log Analysis with LLMs

  • Log Summarization – Llama‑2 can condense long log entries into concise, human‑readable summaries.
  • Pattern Extraction – The model can identify recurring malicious patterns (e.g., credential dumping, lateral movement) and suggest new IOC patterns.
  • Zero‑Shot Classification – Using prompt engineering, the LLM can classify logs into categories (malicious, benign, suspicious) without explicit training data.

3.3 IOC Enrichment via OpenCTI

  • STIX/TAXII Integration – OpenCTI exposes a REST/TAXII API that can be queried for the latest threat actors, malware, and vulnerability data.
  • Automated Matching – A lightweight Python script pulls new IOCs from OpenCTI, stores them in a vector index (FAISS), and performs similarity search against incoming logs.
  • Contextualization – Each alert can be annotated with the actor’s TTPs, known malware families, and historical campaign data.

3.4 Anomaly Detection with FAISS & Sentence‑Transformers

  • Embedding Generation – Logs are converted to dense vectors using Sentence‑Transformers.
  • Indexing – FAISS builds an approximate nearest‑neighbor index for millions of vectors.
  • Distance Thresholding – Events that fall outside a defined similarity radius are flagged as anomalies.
  • Explainability – The nearest neighbors are displayed to analysts, providing context for why an event is anomalous.

3.5 Elastic SIEM as the Alert Hub

  • Alert Correlation – Elastic SIEM aggregates alerts from multiple sources and correlates them using machine‑learning‑based correlation rules.
  • Dashboards – Kibana dashboards provide real‑time visibility into threat‑detection metrics (alert volume, false‑positive rate, detection latency).
  • Playbook Integration – Alerts can trigger automated playbooks (e.g., Wazuh rules, SOAR actions) for rapid response.

3.6 Continuous Improvement

  • Model Retraining – Periodically retrain models on newly labeled data to adapt to evolving attack patterns.
  • Feedback Channels – Analysts can provide feedback via a simple web form; the data is fed back into the training pipeline.
  • Metrics – Track precision, recall, and mean time to detect (MTTD) to measure the effectiveness of the AI‑driven pipeline.

This chapter demonstrates how to combine open‑source AI tools—OpenCTI, Elastic SIEM, FAISS, Sentence‑Transformers, and Llama‑2—to build a robust, automated threat‑detection system that scales with a small security team.