ETWProcessMon2 is for Monitoring Process/Thread/Memory/Imageloads/TCPIP via ETW + Detection for Remote-Thread-Injection & Payload Detection by VirtualMemAlloc Events (in-memory) etc.

A C# process filter driver example which was implemented with the Process Filter Driver SDK. The Process Filter Driver SDK is a kernel-mode driver that filters process/thread creation and termination, it provides you an easy way to develop Windows application for the Windows process monitoring and protection.

Deep transfer learning of additive manufacturing mechanisms across materials in metal-based laser powder bed fusion process

Code repository for the book 'Machine Learning in Python for Process and Equipment Condition Monitoring, and Predictive Maintenance'

一个Python3程序，可以监视系统内各个或者某个进程的资源占用，帮助你找出问题，揪出高占用或者自动启动的进程

Repo for the project GuardCode.

Terminal tool targeted for Windows, its purpose is to find the full list of parent processes based on supplied PIDs or process names by stdin.

This is a project to analyze files to generate procmon logs,windump pcap,and extact codechunks and analyze

Monitoring of direct energy deposition process using deep-net based manifold learning and co-axial melt pool imaging

In Situ Quality Monitoring in Direct Energy Deposition Process using Co-axial Process Zone Imaging and Deep Contrastive Learning

Programming in Python

Sensor selection for process monitoring based on deciphering acoustic emissions from different dynamics of the Laser Powder Bed Fusion process using Empirical Mode Decompositions and Interpretable Machine Learning

Real-Time Monitoring and Quality Assurance for Laser-Based Directed Energy Deposition: Integrating Coaxial Imaging and Self-Supervised Deep Learning Framework

Deep learning-based monitoring of laser powder bed fusion process on variable time-scales using heterogeneous sensing and operando X-ray radiography guidance

Code repository for the book 'Machine Learning in Python for Visual and Acoustic Data-based Process Monitoring'

특정 프로세스의 런타임 파일 API 호출 로그를 분석하여 파일 API 사용 상의 오류를 자동으로 탐지합니다.

Semi-supervised monitoring of laser powder bed fusion process based on acoustic emissions

Self-Supervised Bayesian Representation Learning of Acoustic Emissions from Laser Powder Bed Fusion Process for In-situ Monitoring

Repositry supporting two publications on LPBF process monitoring using acoustic emissions

Monitoring Of Laser Powder Bed Fusion Process By Bridging Dissimilar Process Maps Using Deep Learning-based Domain Adaptation on Acoustic Emissions