Seok-Jun Bu
Orcid: 0000-0002-3940-3611
According to our database1,
Seok-Jun Bu
authored at least 33 papers
between 2017 and 2023.
Collaborative distances:
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Bibliography
2023
Triplet-trained graph transformer with control flow graph for few-shot malware classification.
Inf. Sci., November, 2023
Malware classification with disentangled representation learning of evolutionary triplet network.
Neurocomputing, October, 2023
A graph convolution network with subgraph embedding for mutagenic prediction in aromatic hydrocarbons.
Neurocomputing, April, 2023
A Causally Explainable Deep Learning Model with Modular Bayesian Network for Predicting Electric Energy Demand.
Proceedings of the Hybrid Artificial Intelligent Systems - 18th International Conference, 2023
Phishing URL Detection with Prototypical Neural Network Disentangled by Triplet Sampling.
Proceedings of the International Joint Conference 16th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2023) 14th International Conference on EUropean Transnational Education (ICEUTE 2023), 2023
2022
Insider attack detection in database with deep metric neural network with Monte Carlo sampling.
Log. J. IGPL, 2022
A Neuro-Symbolic AI System for Visual Question Answering in Pedestrian Video Sequences.
Proceedings of the Hybrid Artificial Intelligent Systems - 17th International Conference, 2022
Evolutionary Triplet Network of Learning Disentangled Malware Space for Malware Classification.
Proceedings of the Hybrid Artificial Intelligent Systems - 17th International Conference, 2022
2021
Mutagenic Prediction for Chemical Compound Discovery with Partitioned Graph Convolution Network.
Proceedings of the 16th International Conference on Soft Computing Models in Industrial and Environmental Applications, 2021
Learning Dynamic Connectivity with Residual-Attention Network for Autism Classification in 4D fMRI Brain Images.
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2021, 2021
Directional Graph Transformer-Based Control Flow Embedding for Malware Classification.
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2021, 2021
Integrating Deep Learning with First-Order Logic Programmed Constraints for Zero-Day Phishing Attack Detection.
Proceedings of the IEEE International Conference on Acoustics, 2021
Proceedings of the Hybrid Artificial Intelligent Systems - 16th International Conference, 2021
Learning Disentangled Representation of Web Address via Convolutional-Recurrent Triplet Network for Classifying Phishing URLs.
Proceedings of the International Conference on Electronics, Information, and Communication, 2021
Adversarial Signal Augmentation for CNN-LSTM to Classify Impact Noise in Automobiles.
Proceedings of the IEEE International Conference on Big Data and Smart Computing, 2021
2020
A convolutional neural-based learning classifier system for detecting database intrusion via insider attack.
Inf. Sci., 2020
3D-Convolutional Neural Network with Generative Adversarial Network and Autoencoder for Robust Anomaly Detection in Video Surveillance.
Int. J. Neural Syst., 2020
A Deep Metric Neural Network with Disentangled Representation for Detecting Smartphone Glass Defects.
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2020, 2020
Automated Learning of In-vehicle Noise Representation with Triplet-Loss Embedded Convolutional Beamforming Network.
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2020, 2020
Learning Disentangled Representation of Residential Power Demand Peak via Convolutional-Recurrent Triplet Network.
Proceedings of the 20th International Conference on Data Mining Workshops, 2020
Data Augmentation Using Empirical Mode Decomposition on Neural Networks to Classify Impact Noise in Vehicle.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020
A Monte Carlo Search-Based Triplet Sampling Method for Learning Disentangled Representation of Impulsive Noise on Steering Gear.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020
Detecting Intrusion via Insider Attack in Database Transactions by Learning Disentangled Representation with Deep Metric Neural Network.
Proceedings of the 13th International Conference on Computational Intelligence in Security for Information Systems, 2020
2019
Automatic Financial Trading Agent for Low-risk Portfolio Management using Deep Reinforcement Learning.
CoRR, 2019
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2019, 2019
Genetic Algorithm-Based Deep Learning Ensemble for Detecting Database Intrusion via Insider Attack.
Proceedings of the Hybrid Artificial Intelligent Systems - 14th International Conference, 2019
Classifying In-vehicle Noise from Multi-channel Sound Spectrum by Deep Beamforming Networks.
Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019
2018
Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders.
Inf. Sci., 2018
Learning Optimal Q-Function Using Deep Boltzmann Machine for Reliable Trading of Cryptocurrency.
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2018, 2018
Proceedings of the Hybrid Artificial Intelligent Systems - 13th International Conference, 2018
A Hybrid Deep Learning System of CNN and LRCN to Detect Cyberbullying from SNS Comments.
Proceedings of the Hybrid Artificial Intelligent Systems - 13th International Conference, 2018
2017
Proceedings of the Neural Information Processing - 24th International Conference, 2017
A Hybrid System of Deep Learning and Learning Classifier System for Database Intrusion Detection.
Proceedings of the Hybrid Artificial Intelligent Systems - 12th International Conference, 2017