Malware Filter Framework (MFF) — CNN Optimization

Overview

Optimization and engineering overhaul of Huawei’s production Malware Filter Framework (MFF) at Anshi Lab. The work combined architectural improvements to the deep learning model with low-level systems engineering to achieve a dramatic performance improvement in a production security pipeline.

CNN Optimization via Atrous Spatial Pyramid Pooling

Replaced standard convolutions in MFF with dilated (atrous) convolutions using Spatial Pyramid Pooling, enabling the model to capture multi-scale features without increasing the number of parameters. Combined with feature-profiling and memory caching, this achieved a 315% performance boost over the baseline.

Model Lifecycle & Engineering Excellence

• LLMOps Pipelines Management: Directed multiple lifecycle components including Model Versioners, Validators, Regression Testing, Runtimes, Schedulers, Domain/Data-Drift Detectors, and Retrainers.
• Module Refactoring: Drove codebase restructuring and introduced industry-leading testing and software build practices to improve engineering efficiency.
• Technology Map: Linux C, user-space process development, kernel module development, memory allocation optimization, and low-level performance instrumentation.

Tech Stack: C, TensorFlow, Linux Kernel, CI/CD, MLflow, Weights & Biases, Docker, Kubernetes