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 …
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 M…
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 …
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.
Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
TinyML-based Intrusion Detection for Electric Vehicle Charging Infrastructure (EVCI), utilizing feature selection and hybrid pruning for efficient real-time cybersecurity.
With 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 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.
This repository includes code for the paper “Toward Autonomous and Efficient Cybersecurity: A Multi Objective AutoML based Intrusion Detection System” published in IEEE TMLCN, implementing AutoML and MOO-based intrusion detection systems that optimize both ML model effectiveness and efficiency for IoT systems.
This repository includes code for the paper "Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks" published in IEEE TCOM, focusing on autonomous cybersecurity (physical-layer authentication and cross-layer intrusion detection) using AutoML techniques.
This repository includes code for the paper "Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion Detection" accepted in AutonomousCyber, ACM CCS, 2024.
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