Seok-Jun Bu

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 35

Top Topics

Monte Carlo Search

Malware Detection

Top Venues

Publications

Robin Inho Kee, Dahyun Nam, Seok-Jun Bu, Sung-Bae Cho
Disentangled Prototypical Convolutional Network for Few-Shot Learning in In-Vehicle Noise Classification. IEEE Access 12 (2024)
Junha Kang, Seok-Jun Bu
Graph Anomaly Detection With Disentangled Prototypical Autoencoder for Phishing Scam Detection in Cryptocurrency Transactions. IEEE Access 12 (2024)
Seok-Jun Bu, Sung-Bae Cho
Phishing URL Detection with Prototypical Neural Network Disentangled by Triplet Sampling. CISIS-ICEUTE (2023)
Seok-Jun Bu, Sung-Bae Cho
Triplet-trained graph transformer with control flow graph for few-shot malware classification. Inf. Sci. 649 (2023)
Seok-Jun Bu, Sung-Bae Cho
Malware classification with disentangled representation learning of evolutionary triplet network. Neurocomputing 552 (2023)
Hyung-Jun Moon, Seok-Jun Bu, Sung-Bae Cho
A graph convolution network with subgraph embedding for mutagenic prediction in aromatic hydrocarbons. Neurocomputing 530 (2023)
Seok-Jun Bu, Sung-Bae Cho
A Causally Explainable Deep Learning Model with Modular Bayesian Network for Predicting Electric Energy Demand. HAIS (2023)
Gwang-Myong Go, Seok-Jun Bu, Sung-Bae Cho
Insider attack detection in database with deep metric neural network with Monte Carlo sampling. Log. J. IGPL 30 (6) (2022)
Kyoung-Won Park, Seok-Jun Bu, Sung-Bae Cho
Evolutionary Triplet Network of Learning Disentangled Malware Space for Malware Classification. HAIS (2022)
Jaeil Park, Seok-Jun Bu, Sung-Bae Cho
A Neuro-Symbolic AI System for Visual Question Answering in Pedestrian Video Sequences. HAIS (2022)
Hyung-Jun Moon, Seok-Jun Bu, Sung-Bae Cho
Mutagenic Prediction for Chemical Compound Discovery with Partitioned Graph Convolution Network. SOCO (2021)
Kyoung-Won Park, Seok-Jun Bu, Sung-Bae Cho
Evolutionary Optimization of Neuro-Symbolic Integration for Phishing URL Detection. HAIS (2021)
Kyoung-Won Park, Seok-Jun Bu, Sung-Bae Cho
Learning Dynamic Connectivity with Residual-Attention Network for Autism Classification in 4D fMRI Brain Images. IDEAL (2021)
Seok-Jun Bu, Sung-Bae Cho
Integrating Deep Learning with First-Order Logic Programmed Constraints for Zero-Day Phishing Attack Detection. ICASSP (2021)
Seok-Jun Bu, Hyung-Jun Moon, Sung-Bae Cho
Adversarial Signal Augmentation for CNN-LSTM to Classify Impact Noise in Automobiles. BigComp (2021)
Seok-Jun Bu, Hae-Jung Kim
Learning Disentangled Representation of Web Address via Convolutional-Recurrent Triplet Network for Classifying Phishing URLs. ICEIC (2021)
Hyung-Jun Moon, Seok-Jun Bu, Sung-Bae Cho
Directional Graph Transformer-Based Control Flow Embedding for Malware Classification. IDEAL (2021)
Wonsup Shin, Seok-Jun Bu, Sung-Bae Cho
3D-Convolutional Neural Network with Generative Adversarial Network and Autoencoder for Robust Anomaly Detection in Video Surveillance. Int. J. Neural Syst. 30 (6) (2020)
Gue-Hwan Nam, Seok-Jun Bu, Namu Park, Jae-Yong Seo, Hyeon-Cheol Jo, Won-Tae Jeong
Data Augmentation Using Empirical Mode Decomposition on Neural Networks to Classify Impact Noise in Vehicle. ICASSP (2020)
Seok-Jun Bu, Namu Park, Gue-Hwan Nam, Jae-Yong Seo, Sung-Bae Cho
A Monte Carlo Search-Based Triplet Sampling Method for Learning Disentangled Representation of Impulsive Noise on Steering Gear. ICASSP (2020)
Hyung-Jun Moon, Seok-Jun Bu, Sung-Bae Cho
Learning Disentangled Representation of Residential Power Demand Peak via Convolutional-Recurrent Triplet Network. ICDM (Workshops) (2020)
Gwang-Myong Go, Seok-Jun Bu, Sung-Bae Cho
A Deep Metric Neural Network with Disentangled Representation for Detecting Smartphone Glass Defects. IDEAL (2) (2020)
Seok-Jun Bu, Sung-Bae Cho
A convolutional neural-based learning classifier system for detecting database intrusion via insider attack. Inf. Sci. 512 (2020)
Seok-Jun Bu, Sung-Bae Cho
Automated Learning of In-vehicle Noise Representation with Triplet-Loss Embedded Convolutional Beamforming Network. IDEAL (2) (2020)
Gwang-Myong Go, Seok-Jun Bu, Sung-Bae Cho
Detecting Intrusion via Insider Attack in Database Transactions by Learning Disentangled Representation with Deep Metric Neural Network. CISIS (2020)
Gwang-Myong Go, Seok-Jun Bu, Sung-Bae Cho
A Deep Learning-Based Surface Defect Inspection System for Smartphone Glass. IDEAL (1) (2019)
Wonsup Shin, Seok-Jun Bu, Sung-Bae Cho
Automatic Financial Trading Agent for Low-risk Portfolio Management using Deep Reinforcement Learning. CoRR (2019)
Seok-Jun Bu, Sung-Bae Cho
Genetic Algorithm-Based Deep Learning Ensemble for Detecting Database Intrusion via Insider Attack. HAIS (2019)
Seok-Jun Bu, Sung-Bae Cho
Classifying In-vehicle Noise from Multi-channel Sound Spectrum by Deep Beamforming Networks. IEEE BigData (2019)
Seok-Jun Bu, Sung-Bae Cho
A Hybrid Deep Learning System of CNN and LRCN to Detect Cyberbullying from SNS Comments. HAIS (2018)
Seok-Jun Bu, Sung-Bae Cho
Learning Optimal Q-Function Using Deep Boltzmann Machine for Reliable Trading of Cryptocurrency. IDEAL (1) (2018)
Jin-Young Kim, Seok-Jun Bu, Sung-Bae Cho
Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders. Inf. Sci. (2018)
Jin-Young Kim, Seok-Jun Bu, Sung-Bae Cho
Hybrid Deep Learning Based on GAN for Classifying BSR Noises from Invehicle Sensors. HAIS (2018)
Seok-Jun Bu, Sung-Bae Cho
A Hybrid System of Deep Learning and Learning Classifier System for Database Intrusion Detection. HAIS (2017)
Jin-Young Kim, Seok-Jun Bu, Sung-Bae Cho
Malware Detection Using Deep Transferred Generative Adversarial Networks. ICONIP (1) (2017)