A Hybrid System of Deep Learning and Learning Classifier System for Database Intrusion Detection. - Seok-Jun Bu; Sung-Bae Cho (HAIS, 2017)

Malware Detection Using Deep Transferred Generative Adversarial Networks. - Jin-Young Kim; Seok-Jun Bu; Sung-Bae Cho (ICONIP (1), 2017)

A Hybrid Deep Learning System of CNN and LRCN to Detect Cyberbullying from SNS Comments. - Seok-Jun Bu; Sung-Bae Cho (HAIS, 2018)

Hybrid Deep Learning Based on GAN for Classifying BSR Noises from Invehicle Sensors. - Jin-Young Kim; Seok-Jun Bu; Sung-Bae Cho (HAIS, 2018)

Learning Optimal Q-Function Using Deep Boltzmann Machine for Reliable Trading of Cryptocurrency. - Seok-Jun Bu; Sung-Bae Cho (IDEAL (1), 2018)

Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders. - Jin-Young Kim; Seok-Jun Bu; Sung-Bae Cho (Inf. Sci., 2018)

Classifying In-vehicle Noise from Multi-channel Sound Spectrum by Deep Beamforming Networks. - Seok-Jun Bu; Sung-Bae Cho (IEEE BigData, 2019)

Genetic Algorithm-Based Deep Learning Ensemble for Detecting Database Intrusion via Insider Attack. - Seok-Jun Bu; Sung-Bae Cho (HAIS, 2019)

A Deep Learning-Based Surface Defect Inspection System for Smartphone Glass. - Gwang-Myong Go; Seok-Jun Bu; Sung-Bae Cho (IDEAL (1), 2019)

Automatic Financial Trading Agent for Low-risk Portfolio Management using Deep Reinforcement Learning. - Wonsup Shin; Seok-Jun Bu; Sung-Bae Cho (CoRR, 2019)

Detecting Intrusion via Insider Attack in Database Transactions by Learning Disentangled Representation with Deep Metric Neural Network. - Gwang-Myong Go; Seok-Jun Bu; Sung-Bae Cho (CISIS, 2020)

A Monte Carlo Search-Based Triplet Sampling Method for Learning Disentangled Representation of Impulsive Noise on Steering Gear. - Seok-Jun Bu; Namu Park; Gue-Hwan Nam; Jae-Yong Seo; Sung-Bae Cho (ICASSP, 2020)

Data Augmentation Using Empirical Mode Decomposition on Neural Networks to Classify Impact Noise in Vehicle. - Gue-Hwan Nam; Seok-Jun Bu; Namu Park; Jae-Yong Seo; Hyeon-Cheol Jo; Won-Tae Jeong (ICASSP, 2020)

Learning Disentangled Representation of Residential Power Demand Peak via Convolutional-Recurrent Triplet Network. - Hyung-Jun Moon; Seok-Jun Bu; Sung-Bae Cho (ICDM (Workshops), 2020)

Automated Learning of In-vehicle Noise Representation with Triplet-Loss Embedded Convolutional Beamforming Network. - Seok-Jun Bu; Sung-Bae Cho (IDEAL (2), 2020)

A Deep Metric Neural Network with Disentangled Representation for Detecting Smartphone Glass Defects. - Gwang-Myong Go; Seok-Jun Bu; Sung-Bae Cho (IDEAL (2), 2020)

3D-Convolutional Neural Network with Generative Adversarial Network and Autoencoder for Robust Anomaly Detection in Video Surveillance. - Wonsup Shin; Seok-Jun Bu; Sung-Bae Cho (Int. J. Neural Syst., 2020)

A convolutional neural-based learning classifier system for detecting database intrusion via insider attack. - Seok-Jun Bu; Sung-Bae Cho (Inf. Sci., 2020)

Adversarial Signal Augmentation for CNN-LSTM to Classify Impact Noise in Automobiles. - Seok-Jun Bu; Hyung-Jun Moon; Sung-Bae Cho (BigComp, 2021)

Learning Disentangled Representation of Web Address via Convolutional-Recurrent Triplet Network for Classifying Phishing URLs. - Seok-Jun Bu; Hae-Jung Kim (ICEIC, 2021)

Evolutionary Optimization of Neuro-Symbolic Integration for Phishing URL Detection. - Kyoung-Won Park; Seok-Jun Bu; Sung-Bae Cho (HAIS, 2021)

Integrating Deep Learning with First-Order Logic Programmed Constraints for Zero-Day Phishing Attack Detection. - Seok-Jun Bu; Sung-Bae Cho (ICASSP, 2021)

Directional Graph Transformer-Based Control Flow Embedding for Malware Classification. - Hyung-Jun Moon; Seok-Jun Bu; Sung-Bae Cho (IDEAL, 2021)

Learning Dynamic Connectivity with Residual-Attention Network for Autism Classification in 4D fMRI Brain Images. - Kyoung-Won Park; Seok-Jun Bu; Sung-Bae Cho (IDEAL, 2021)

Mutagenic Prediction for Chemical Compound Discovery with Partitioned Graph Convolution Network. - Hyung-Jun Moon; Seok-Jun Bu; Sung-Bae Cho (SOCO, 2021)

Evolutionary Triplet Network of Learning Disentangled Malware Space for Malware Classification. - Kyoung-Won Park; Seok-Jun Bu; Sung-Bae Cho (HAIS, 2022)

A Neuro-Symbolic AI System for Visual Question Answering in Pedestrian Video Sequences. - Jaeil Park; Seok-Jun Bu; Sung-Bae Cho (HAIS, 2022)

Insider attack detection in database with deep metric neural network with Monte Carlo sampling. - Gwang-Myong Go; Seok-Jun Bu; Sung-Bae Cho (Log. J. IGPL, 2022)

Phishing URL Detection with Prototypical Neural Network Disentangled by Triplet Sampling. - Seok-Jun Bu; Sung-Bae Cho (CISIS-ICEUTE, 2023)

A Causally Explainable Deep Learning Model with Modular Bayesian Network for Predicting Electric Energy Demand. - Seok-Jun Bu; Sung-Bae Cho (HAIS, 2023)

A graph convolution network with subgraph embedding for mutagenic prediction in aromatic hydrocarbons. - Hyung-Jun Moon; Seok-Jun Bu; Sung-Bae Cho (Neurocomputing, 2023)

Malware classification with disentangled representation learning of evolutionary triplet network. - Seok-Jun Bu; Sung-Bae Cho (Neurocomputing, 2023)

Triplet-trained graph transformer with control flow graph for few-shot malware classification. - Seok-Jun Bu; Sung-Bae Cho (Inf. Sci., 2023)

Anchor-Net: Distance-Based Self-Supervised Learning Model for Facial Beauty Prediction. - Jiho Bae; Seok-Jun Buu; Suwon Lee (IEEE Access, 2024)

Disentangled Prototype-Guided Dynamic Memory Replay for Continual Learning in Acoustic Signal Classification. - Seok-Hun Choi; Seok-Jun Buu (IEEE Access, 2024)

Graph Anomaly Detection With Disentangled Prototypical Autoencoder for Phishing Scam Detection in Cryptocurrency Transactions. - Junha Kang; Seok-Jun Bu (IEEE Access, 2024)

Disentangled Prototypical Convolutional Network for Few-Shot Learning in In-Vehicle Noise Classification. - Robin Inho Kee; Dahyun Nam; Seok-Jun Bu; Sung-Bae Cho (IEEE Access, 2024)

Deep Generative Replay With Denoising Diffusion Probabilistic Models for Continual Learning in Audio Classification. - Hyeon-Ju Lee; Seok-Jun Buu (IEEE Access, 2024)

Speech-Guided Drone Control System Based on Large Language Model. - Seok-Hun Choi; Zeen-Chul Kim; Seok-Jun Buu (ICEIC, 2025)

Predicting Glucose Levels via Disentangled Patterns with Triplet Network. - Janghee Lee; Heung-Yong Jin; Seok-Jun Buu (ICEIC, 2025)

Wi-Fi-Enabled Vision via Spatially-Variant Pose Estimation Based on Convolutional Transformer Network. - Hyeon-Ju Lee; Seok-Jun Buu (IEEE Access, 2025)

Triplet-Style Dynamic Graph Network With Transformer Encoder for Scam Detection in Cryptocurrency Transactions. - Min-Woo Nam; Hyeon-Ju Lee; Seok-Jun Buu (IEEE Access, 2025)

Vision-Ultrasound Robotic System based on Deep Learning for Gas and Arc Hazard Detection in Manufacturing. - Jin-Hee Lee; Dahyun Nam; Robin Inho Kee; YoungKey Kim; Seok-Jun Buu (CoRR, 2025)