Reconstruction-based Anomaly Detection for Industrial Control Systems.
This thesis aims to implement a deep learning anomaly detector for industrial control systems.
Reconstruction-based Anomaly Detection for Industrial Control Systems.
This thesis aims to implement a deep learning anomaly detector for industrial control systems.
Analysis of the paper "Cerberus: Enabling Effective and Effective In-Network Monitoring on Programmable Switches", analysis of the code in the repository, and execution of some simple tests.
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Employment typeEmployment typeFull-time
Job functionJob functionOtherIndustriesComputer and Network Security
Explainable Anomaly Detection for Industrial Control Systems.
This thesis aims to analyze and implement XAI methods for anomaly detection in industrial control systems.
In-depth study of the paper "Time Series Anomaly Detection using Diffusion-based Models" and implementation of its diffusion-based pipelines to detect anomalies in multivariate 5G traffic time series.
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Energy-Efficient Decentralised Training Frameworks for Large-Scale AI Models on Geo-Distributed Infrastructure
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Computer Vision Scientist, International Machine Learning, Australia
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Analysis of the Orion machine learning library for unsupervised time series anomaly detection and configuration of Orion pipelines to detect anomalies in 5G network metrics and connection time series.
Analysis of the paper "Information Hiding in Industrial Control Systems: An OPC UA based Supply Chain Attack and its Detection" and implementation of the attack in an OT testbed based on OpenPLC.
Analysis of the paper "ICSML: Industrial Control Systems ML Framework for native inference using IEC ******* code", analysis of the code in the repository, and execution of some simple tests.
Experiments with the Industrial Control Systems Modelica Library for IC real-time simulation.
Analysis of the paper "IFFSET: In-Field Fuzzing of Industrial Control Systems using System Emulation" and execution of fuzzing tests over the firmware image from a WAGO PLC.
Study of the catch22 feature set for time series, extraction of canonical features from 5G connection time series, and evaluation for clustering, characterization, and anomaly/usage pattern detection.
Study of the TAB benchmark "Unified Benchmarking of Time Series Anomaly Detection Methods" and use of its framework to test models on 5G anomaly detection tasks.
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📌 Industrial Control Systems: Deep Learning Anomaly Detector (Victoria)
🏢 ULISSecurity
📍 Victoria