@article{ART003300031},
author={Ha Young Kim and Seong-Cho Hong and Ah Reum Kang},
title={A Proposal of an LSTM-Based Machine Learning and Hybrid Application Security Testing Architecture for Web Vulnerability Detection},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2026},
volume={31},
number={1},
pages={1-12}
TY - JOUR
AU - Ha Young Kim
AU - Seong-Cho Hong
AU - Ah Reum Kang
TI - A Proposal of an LSTM-Based Machine Learning and Hybrid Application Security Testing Architecture for Web Vulnerability Detection
JO - Journal of The Korea Society of Computer and Information
PY - 2026
VL - 31
IS - 1
PB - The Korean Society Of Computer And Information
SP - 1
EP - 12
SN - 1598-849X
AB - This study proposes an HAST architecture that integrates a machine learning–based LSTM model with SAST and DAST to address the growing number of vulnerabilities in web application environments. An analysis of previous studies reveals several limitations in existing web vulnerability detection approaches, including the lack of standardized datasets, limited domain generalization, and insufficient responsiveness to real-time attack scenarios. To overcome these challenges, the proposed architecture combines LSTM-based request sequence analysis with a unified SAST–DAST pipeline. The proposed HAST structure supports real-time request detection, coordinated static and dynamic analysis, and a retrainable expansion mechanism, enabling a stepwise response to evolving web application environments and emerging attack patterns. The results are expected to support the development of an integrated response framework for web vulnerability detection and to provide a structural design foundation for future research.
KW - Web Vulnerability;LSTM;SAST;DAST;HAST
DO -
UR -
ER -
Ha Young Kim, Seong-Cho Hong and Ah Reum Kang. (2026). A Proposal of an LSTM-Based Machine Learning and Hybrid Application Security Testing Architecture for Web Vulnerability Detection. Journal of The Korea Society of Computer and Information, 31(1), 1-12.
Ha Young Kim, Seong-Cho Hong and Ah Reum Kang. 2026, "A Proposal of an LSTM-Based Machine Learning and Hybrid Application Security Testing Architecture for Web Vulnerability Detection", Journal of The Korea Society of Computer and Information, vol.31, no.1 pp.1-12.
Ha Young Kim, Seong-Cho Hong, Ah Reum Kang "A Proposal of an LSTM-Based Machine Learning and Hybrid Application Security Testing Architecture for Web Vulnerability Detection" Journal of The Korea Society of Computer and Information 31.1 pp.1-12 (2026) : 1.
Ha Young Kim, Seong-Cho Hong, Ah Reum Kang. A Proposal of an LSTM-Based Machine Learning and Hybrid Application Security Testing Architecture for Web Vulnerability Detection. 2026; 31(1), 1-12.
Ha Young Kim, Seong-Cho Hong and Ah Reum Kang. "A Proposal of an LSTM-Based Machine Learning and Hybrid Application Security Testing Architecture for Web Vulnerability Detection" Journal of The Korea Society of Computer and Information 31, no.1 (2026) : 1-12.
Ha Young Kim; Seong-Cho Hong; Ah Reum Kang. A Proposal of an LSTM-Based Machine Learning and Hybrid Application Security Testing Architecture for Web Vulnerability Detection. Journal of The Korea Society of Computer and Information, 31(1), 1-12.
Ha Young Kim; Seong-Cho Hong; Ah Reum Kang. A Proposal of an LSTM-Based Machine Learning and Hybrid Application Security Testing Architecture for Web Vulnerability Detection. Journal of The Korea Society of Computer and Information. 2026; 31(1) 1-12.
Ha Young Kim, Seong-Cho Hong, Ah Reum Kang. A Proposal of an LSTM-Based Machine Learning and Hybrid Application Security Testing Architecture for Web Vulnerability Detection. 2026; 31(1), 1-12.
Ha Young Kim, Seong-Cho Hong and Ah Reum Kang. "A Proposal of an LSTM-Based Machine Learning and Hybrid Application Security Testing Architecture for Web Vulnerability Detection" Journal of The Korea Society of Computer and Information 31, no.1 (2026) : 1-12.
