RANCANG BANGUN EKSTENSI VISUAL STUDIO CODE UNTUK DETEKSI DINI HARCODED CREDENTIAL MENGGUNAKAN METODE PATTERN MATCHING (REGEX) DAN SHANNON ENTROPY

Aswangga Sujati, NIM.: 22106050053 (2026) RANCANG BANGUN EKSTENSI VISUAL STUDIO CODE UNTUK DETEKSI DINI HARCODED CREDENTIAL MENGGUNAKAN METODE PATTERN MATCHING (REGEX) DAN SHANNON ENTROPY. Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

Preview	Text (RANCANG BANGUN EKSTENSI VISUAL STUDIO CODE UNTUK DETEKSI DINI HARCODED CREDENTIAL MENGGUNAKAN METODE PATTERN MATCHING (REGEX) DAN SHANNON ENTROPY) 22106050053_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf - Published Version Download (7MB) | Preview
	Text (RANCANG BANGUN EKSTENSI VISUAL STUDIO CODE UNTUK DETEKSI DINI HARCODED CREDENTIAL MENGGUNAKAN METODE PATTERN MATCHING (REGEX) DAN SHANNON ENTROPY) 22106050053_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf - Published Version Restricted to Registered users only Download (12MB) | Request a copy

Abstract

Hardcoded credentials embedded in source code can pose security risks because credentials stored directly in source code may be exposed before they are identified by developers. This research aims to design and develop a Visual Studio Code extension named CredGuard that applies the Shift-Left Security concept to detect hardcoded credentials automatically and in real time during the coding process. The research employs the Extreme Programming (XP) methodology, consisting of iterative planning, design, coding, and testing activities. CredGuard combines Pattern Matching based on Regular Expression (Regex) to detect credentials with specific patterns and Shannon Entropy to analyze strings with high levels of randomness. Testing was conducted using Black-box Testing to verify system functionality and accuracy evaluation using a confusion matrix on 300 samples consisting of 150 Secret and 150 Non-Secret samples. The final evaluation resulted in a Precision of 80.12%, a Recall of 86.00%, and an F1-Score of 82.96%. These results indicate that CredGuard is capable of detecting hardcoded credentials in five supported file types, namely JavaScript, TypeScript, Python, PHP, and .env, with improved detection performance after refinement in the third iteration. Based on the results, CredGuard can be used as an early hardcoded credential detection mechanism within the Visual Studio Code development environment by combining pattern-based detection and string randomness analysis.

Share this knowledge with your friends :

Actions (login required)

View Item