🔍 Automate dissolved gas analysis for transformer health assessment with a MATLAB tool that streamlines fault classification and plotting.
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Updated
Apr 30, 2026 - MATLAB
🔍 Automate dissolved gas analysis for transformer health assessment with a MATLAB tool that streamlines fault classification and plotting.
🔍 Automate Dissolved Gas Analysis (DGA) for power transformers, ensuring swift fault classification and eliminating manual plotting with MATLAB.
An application designed to automate the calculation and visualization of Dissolved Gas Analysis (DGA) for power transformers.
Data-parallel pipeline for detecting DGA-based malware using linguistic feature extraction on multi-core CPUs. Achieves 5.75× speedup on 8 cores with 93.18% classification accuracy. Built with Python multiprocessing and Random Forest. [AMLCCZG516 — BITS Pilani]
Machine Learning based transformer fault detection using DGA data
Advanced DGA Detection Framework: Hybrid Transformer Architecture with TLD-Aware Multi-Task Learning
Data Intermediary and Altruist Fronend
heiDGAF - a machine learning based DNS inspector to detect DGAs in the wild!
Real Intelligence Threat Analytics (RITA) is a framework for detecting command and control communication through network traffic analysis.
Backend for dc-dga (storage for data intermediaries and altruists)
Real-time AI ensemble (RF + SVM + BiLSTM) for DGA domain detection. Final Year Project.
Some results of my DGA reversing efforts
RAMPAGE is a framework aimed at training and comparing machine learning models for the detection of Algorithmically Generated Domains.
Generation of malicious DoH traffic using Domain Generation Algorithms and DNS Tunneling tools. and classification of DoH traffic as DGA or DNS Tunneling
DGA Detective - Hunt domains generated by Domain Generation Algorithms to identify malware traffic
Fall21 Semester Research in Applying NLP methods to Malware Domain Generation Algorithms (DGA) to improve Command-and-Control Center identification.
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