'Securing the Browser: A Hybrid Static Analysis Framework for Detecting Malicious Chrome Extensions via Local Threat Intelligence'

Modern web browsers have evolved into sophisticated platforms where extensions play a crucial role in enhancing user productivity and customization. However, this extensibility significantly increases the attack surface, as malicious extensions often abuse excessive permissions to harvest cookies, manipulate the Document Object Model (DOM), and exfiltration sensitive user data. Existing browser sandboxing mechanisms provide limited visibility into such threats, while cloud-based detection approaches raise privacy concerns. This paper proposes a hybrid static analysis framework for detecting malicious Google Chrome extensions that preserves user privacy while enabling deep security inspection. The detection logic is decoupled from the browser environment and implemented as a Python-based local analysis service, which securely communicates with a lightweight Chrome extension frontend via Restful APIs. The framework employs a dual-layer detection strategy: a weighted permission risk scoring model to assess privilege abuse potential, and Signature-based correlation against a local threat intelligence repository containing known malicious patterns and indicators of compromise. Experimental results demonstrate that the proposed approach improves detection accuracy and significantly reduces false positives compared to standalone permission-based techniques, offering an effective and privacy-preserving defense for end-users.