Western OC2 Lab
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31 repositories
- This repository includes code for the paper "Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion Detection" accepted in AutonomousCyber, ACM CCS, 2024.
EVCI-Pruning
PublicWith the increasing demand for real-time processing on IoT devices, optimizing machine learning models' size, latency, and efficiency is crucial. This repository implements pruning techniques aimed at improving computational efficiency in resource-constrained environments, specifically targeting Electric Vehicle Charging Infrastructure (EVCI).- This repository serves as a platform for posting a diverse collection of Python codes for signal processing, facilitating various operations within a typical signal processing pipeline (pre-processing, processing, and application).
- This repository includes code for the AutoML-based IDS and adversarial attack defense case studies presented in the paper "Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis" published in IEEE Transactions on Network and Service Management.
- This repository includes code for the paper "Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks" submitted to IEEE TCOM, focusing on autonomous cybersecurity (physical-layer authentication and cross-layer intrusion detection) using AutoML techniques.
FDE
PublicTRL-HPO
PublicWrapperRL
PublicCsFL
Public- Python codes “Jupyter notebooks” for the paper entitled "A Hybrid Method for Condition Monitoring and Fault Diagnosis of Rolling Bearings With Low System Delay, IEEE Trans. on Instrumentation and Measurement, Aug. 2022. Techniques used: Wavelet Packet Transform (WPT) & Fast Fourier Transform (FFT). Application: vibration-based fault diagnosis.
- Implementation/Tutorial of using Automated Machine Learning (AutoML) methods for static/batch and online/continual learning
TinyML_EVCI
PublicThis repository contains code for comparing traditional Machine Learning (ML) and Tiny Machine Learning (TinyML) in terms of time, memory usage, and performance, specifically in the context of electric vehicle charging infrastructure. It also offers practical insights by implementing TinyML on the ESP32 microcontroller.- An online learning method used to address concept drift and model drift. Code for the paper entitled "A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams" published in IEEE Internet of Things Magazine.
- Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
- Data stream analytics: Implement online learning methods to address concept drift and model drift in data streams using the River library. Code for the paper entitled "PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams" published in IEEE GlobeCom 2021.
hierarchical-CO2
PublicCorrFL
Public- Code for intrusion detection system (IDS) development using CNN models and transfer learning
- SB-PdM is a non-machine learning code to perform Predictive Maintenance (PdM) of rolling bearings without the need to train a classifier. In SM-PdM, the classification task is performed by applying a similarity measure between test sample and class-reference labeled samples in the feature space.
- Python code “Jupyter notebooks” for the paper entitled " Similarity-Based Predictive Maintenance Framework for Rotating Machinery" has been presented in the Fifth International Conference on Communications, Signal Processing, and their Applications (ICCSPA’22), Cairo, Egypt, 27-29 December 2022. Techniques used: statistical analysis, FFT, and STFT.
- Data stream analytics: Implement online learning methods to address concept drift and model drift in dynamic data streams. Code for the paper entitled "A Multi-Stage Automated Online Network Data Stream Analytics Framework for IIoT Systems" published in IEEE Transactions on Industrial Informatics.
FL-IOV-ITS
PublicCode for the case study presented in "Making a Case for Federated Learning in the Internet of Vehicles and Intelligent Transportation Systems" accepted for publication in the IEEE Network Magazine May 2021 Special Issue on AI-empowered Mobile Edge Computing in the Internet of Vehicles.