Blockchain-Enabled Anomaly Detection for Secure CI/CD Pipelines

Abstract

Blockchain technology has emerged as a transformative force in securing distributed systems, offering tamper‑evident, decentralized ledgers that ensure data integrity and transparency. As software development organizations increasingly adopt Continuous Integration and Continuous Deployment (CI/CD) pipelines to accelerate release cycles, they concurrently expose themselves to sophisticated threats such as supply‑chain attacks, insider tampering, and configuration drifts that traditional security measures struggle to detect in real time. This manuscript presents ChainSec‑CI, a novel framework that marries the immutability guarantees of a permissioned blockchain—specifically Hyperledger Fabric—with AI‑driven anomaly detection to secure CI/CD pipelines comprehensively. Within ChainSec‑CI, every critical pipeline event—ranging from source code commits and build artifacts to test executions and deployment actions—is recorded on‑chain via lightweight smart contracts, creating a verifiable, append‑only audit trail. To address the high‑volume, heterogeneous nature of pipeline metadata, we extract key features (e.g., stage durations, failure frequencies, configuration hash changes, and sequence anomalies) and feed these into an unsupervised isolation forest model, enabling the system to learn “normal” pipeline behavior without requiring labeled attack data. We developed a Jenkins–Hyperledger Fabric integration prototype and evaluated it using 10,000 synthetic pipeline runs containing both benign operations and injected malicious scenarios such as unauthorized config modifications, abnormally prolonged build or test steps, and out‑of‑order stage executions.