Article Per Year (5 Year)
p-Index From 2020 - 2025
1.318
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) InComTech: Jurnal Telekomunikasi dan Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Telematika Jurnal Sistem Informasi
Devi Fitrianah
Fakultas Ilmu Komputer, Universitas Mercu Buana
Computer Science & IT Education Electrical & Electronics Engineering Engineering

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : International Journal of Electrical and Computer Engineering

 1 
Exploring the recurrent and sequential security patch data using deep learning approaches Alam, Falah Muhammad; Fitrianah, Devi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4160-4171

Abstract

The ever-changing nature of vulnerabilities and the intricacy of temporal connections make the classification of security patch data, both sequential and recurrent, a formidable challenge in cybersecurity. The goal of this research is to improve the efficacy and precision of security patch management by optimizing deep learning models to deal with these issues. In order to assess their performance on the PatchDB dataset, four models were used: recurrent neural networks (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (Bi-LSTM). Metrics like F1-score, area under the receiver operating characteristic curve (AUC-ROC), recall, accuracy, and precision were used to evaluate performance. When it came to processing sequential data, the GRU model was the most efficient, with the best accuracy (77.39%), recall (65.63%), and AUC-ROC score (0.8127). With a 75.17% accuracy rate and an AUC-ROC score of 0.7752, the RNN model successfully reduced false negatives. With AUC-ROC scores of 0.7792 and 0.8055, respectively, LSTM and Bi-LSTM had better specificity but more false negatives. To improve cybersecurity operations, decrease mitigation time, and automate the classification of security updates, this study presents a methodology. To improve the models' practicality, future efforts will center on increasing datasets and testing them in real-world settings.
Co-Authors Alam, Falah Muhammad Amalia Fitri Hardiyanti Aprilia, Sella Joanita Nur Ayuni, Ghebyla Najla Bodhi, Gabriel Charleen Deni Triyansyah Fitria Habibatul Hamdanah Hamdanah, Fitria Habibatul Hasana, Silviya Hisyam Fahmi Rendi Rendi, Rendi Rojali, Rojali S.Pd. M Kes I Ketut Sudiana . Sari, Wina Permana Yudho Giri Sucahyo Yudho Giri Sucahyo Yudho Giri Sucahyo