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.

TinyML-based Intrusion Detection for Electric Vehicle Charging Infrastructure (EVCI), utilizing feature selection and hybrid pruning for efficient real-time cybersecurity.

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.

Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)

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" 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.

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 the implementation to the Feature Drift Explanation method for identifying drift source in a cyber-security attack scenario.