Seok-Jun Buu

Assistant Professor

Gyeongsang National University

South Korea

Research Interests

Explore related searches

Professors in South Korea Professors in Artificial Intelligence Artificial Intelligence professors in South Korea

About

Dr. Seok-Jun Buu is an Assistant Professor at Gyeongsang National University in South Korea. His research focuses on advancing detection methods for phishing scams and cryptocurrencies, with recent articles exploring topics such as transformer networks and hyperparameter optimization. Additionally, he investigates novel approaches in continual learning and audio classification, contributing significantly to the fields of security and machine learning.

Articles (18)

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2022

Year:

2022

Year:

2021

Year:

2020

Year:

2020

Year:

2020

Collaborators (1)

Sung-Bae Cho

Yonsei University

SOUTH KOREA

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Seok-Jun Buu

Research Interests

Explore related searches

About

Articles (18)

A Transformer network calibrated with fuzzy logic for phishing URL detection

Proximal Policy-Guided Hyperparameter Optimization for Mitigating Model Decay in Cryptocurrency Scam Detection

Reinforced Disentangled HTML Representation Learning with Hard-Sample Mining for Phishing Webpage Detection

Triplet-Style Dynamic Graph Network With Transformer Encoder for Scam Detection in Cryptocurrency Transactions

Wi-Fi-Enabled Vision via Spatially-Variant Pose Estimation Based on Convolutional Transformer Network

Phishing Webpage Detection via Multi-Modal Integration of HTML DOM Graphs and URL Features Based on Graph Convolutional and Transformer Networks

Learning to Traverse Cryptocurrency Transaction Graphs Based on Transformer Network for Phishing Scam Detection

Anchor-Net: Distance-Based Self-Supervised Learning Model for Facial Beauty Prediction

Deep Generative Replay With Denoising Diffusion Probabilistic Models for Continual Learning in Audio Classification

Disentangled Prototype-Guided Dynamic Memory Replay for Continual Learning in Acoustic Signal Classification

Triplet-trained graph transformer with control flow graph for few-shot malware classification

Malware classification with disentangled representation learning of evolutionary triplet network

Optimized URL Feature Selection Based on Genetic-Algorithm-Embedded Deep Learning for Phishing Website Detection

Ensemble of Deep Convolutional Learning Classifier System Based on Genetic Algorithm for Database Intrusion Detection

Deep Character-Level Anomaly Detection Based on a Convolutional Autoencoder for Zero-Day Phishing URL Detection

Time Series Forecasting with Multi-Headed Attention-Based Deep Learning for Residential Energy Consumption

A convolutional neural-based learning classifier system for detecting database intrusion via insider attack

3D-Convolutional Neural Network with Generative Adversarial Network and Autoencoder for Robust Anomaly Detection in Video Surveillance

Collaborators (1)

Sung-Bae Cho

Make your next move.