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All Journal INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi
Asep Ramdhani Mahbub
Bhayangkara Jakarta Raya University
Computer Science & IT Decision Sciences, Operations Research & Management

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A Deep Learning NLP Framework with Directional Augmentation for Cybersecurity Compliance Monitoring Tri Ginanjar Laksana; Prima Dina Atika; Asep Ramdhani Mahbub; Ade Rahmat Iskandar; Wan Nooraishy Wan Ahmad
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 10 No 2 (2026)
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v10i2.28533

Abstract

Background: The increasing complexity of cybersecurity mandates, such as BSSN and Kominfo regulations, presents a significant challenge for automated compliance auditing in Indonesia. Traditional NLP models often struggle with the semantic gap between formal regulatory language and raw technical system telemetry, leading to high false-negative rates in security monitoring. Objective: The purpose of this research to develop a robust deep learning framework to automate cybersecurity compliance assessments while addressing the linguistic challenges of the Indonesian regulatory landscape. The primary goal is to enhance the detection of non-compliant system behaviors by bridging the gap between documentation and real-time logs. Methods: The proposed framework utilized a Transformer-based BERT architecture integrated with a novel Directional Augmentation (DA) mechanism. The methodology follows a four-phase process: (1) data collection of 8,240 labeled points, (2) an Anti-Leak Grouping Strategy to prevent data memorization, (3) implementation of DA through Semantic Polarization and Technical Jargon Injection, and (4) model training and evaluation.  Result: The findings of this research are indicate that the proposed framework significantly outperformed the baseline BERT model. Test Accuracy rose from 90.15% to 95.72%, while Validation Accuracy improved from 91.20% to 96.88%. The final model achieved an Overall Accuracy of 94.39%, maintaining a balanced F1-score and effectively reducing False Negatives to only 32 cases in the detection of security violations, Conlussion : Integrating Directional Augmentation into BERT optimizes Indonesian cybersecurity auditing by synchronizing BSSN/Kominfo regulatory language with technical telemetry through semantic polarization and jargon injection.
Co-Authors Ade Rahmat Iskandar Prima Dina Atika Tri Ginanjar Laksana Wan Nooraishy Wan Ahmad