PortfolioResearch

LOG INTELLIGENCE & AI FOR SECURITY

Research

From detecting anomalies to understanding their causes.

Exploring temporal neural models and causal event graphs to make sense of network logs and operational anomalies.

Pham Quoc HuyCybersecurity · HUFLIT
01
Accepted paper
01
Under review
01
Student research

Selected research

03 works

EIDT 2026

Accepted

Conference paper

TEMPORAL NEURAL MODELING

Ablation-Aware Operational Anomaly Detection in Controlled SDN Logs Using Temporal Neural Models

Accepted conference paper in AI-driven log intelligence, temporal neural modeling, and operational anomaly detection.

AUTHORS

ThS. Cao Tiến Thành, Phạm Quốc Huy, Nguyễn Văn Tiến Phát, PGS.TS. Trần Mạnh Hà, TS. Trần Thị Minh Khoa

  • SDN Logs
  • Temporal Neural Models
  • Anomaly Detection
  • Ablation Studies
Related project

SOICT 2026

Under Review

Conference submission

CAUSAL ROOT CAUSE IDENTIFICATION

CERES-RCA: Causal Event-Graph Modeling for Root Cause Identification under Chronological Benchmark Saturation

Causal event-graph modeling for root cause identification, with a focus on chronological benchmark saturation.

Submitted to SOICT 2026 · Currently under peer review.

AUTHORS

ThS. Cao Tiến Thành, Phạm Quốc Huy, ThS. Lê Đức Anh, PGS.TS. Trần Mạnh Hà

  • CERES-RCA
  • Causal Event Graphs
  • Root Cause Identification
  • Chronological Evaluation

HUFLIT

Student Research

Faculty of Information Technology

NETWORK LOG ANOMALY DETECTION

Ứng dụng mạng TCN (Transformer Temporal Convolution Network) trong phát hiện lỗi bất thường từ dữ liệu log mạng doanh nghiệp

Nghiên cứu khoa học sinh viên khoa Công nghệ thông tin, Trường Đại học Ngoại ngữ – Tin học TP. Hồ Chí Minh (HUFLIT).

AUTHOR

Phạm Quốc Huy

  • TCN
  • Enterprise Network Logs
  • Anomaly Detection
Related project

LET'S CONNECT

Research & collaboration

AI-driven security, log intelligence, and anomaly detection.

huy2k57@gmail.com