Innovative Threat Research

We specialize in advanced threat detection through multi-source data analysis and cutting-edge AI model development.

A person with dark hair and a facial mask is seated, focused on a laptop. The setting appears informal, as the individual is leaning back in a black shirt. There are blurred figures in the foreground and background, suggesting a casual or relaxed environment.

A laptop with a closed lid placed on a dark surface. A face mask is laid partially over the laptop, suggesting themes of work-from-home or remote environments.

Threat Analysis

Comprehensive research on threat detection and model performance evaluation.

A laboratory setting with several advanced machines, including a prominent white machine labeled 'SINIC-TEK' featuring a large monitor on top displaying technical graphics. Beside it, another machine displays the label 'GWEI' with a smaller screen. The setting has a high-tech and industrial appearance with a polished green floor and metallic curtains.

Data Collection

Gathering anonymized multi-source data for analysis and training.

A close-up view of computer code or data displayed on a screen. The text appears to be part of a JSON-like structure with key-value pairs, featuring words such as 'protected', 'verified', and 'followers'. The text is in white, while some metadata or index numbers are in green.

Model Fine-Tuning

Developing a two-stage training framework for threat patterns.

A person with a striped shirt and a cap is standing in front of a computer screen displaying data and analytics. Another laptop is open on a desk below the monitor, and a large metallic tank with a pressure gauge is nearby.

A person wearing a white mask and a black hooded sweatshirt is sitting at a desk with a laptop in front of them. The background is a solid orange color, creating a stark contrast with the dark clothing and laptop.

Benchmarking Performance

Comparing efficiency and accuracy against traditional machine learning models.

Interpretability Analysis

Decoding decision logic to enhance analyst trust and understanding.

Expected outcomes include advancements in:

Technical Understanding:

Reveal GPT-4’s potential and limitations in cybersecurity, such as its generalization boundaries in multimodal threat analysis (logs + network traffic metadata).

Model Mechanism:

Societal Impact:

By comparing pre- and post-fine-tuning decision paths (e.g., attention weight analysis), explore the explainability of LLMs in attack pattern detection, addressing practitioners’ distrust of "black-box AI."

Provide methodologies for AI-driven proactive defense systems, reducing data breach risks for SMEs lacking security teams, and promoting AI ethics frameworks in cybersecurity.