Vulnerability of machine learning approaches applied in iot-based smart grid: A review

Z Zhang, M Liu, M Sun, R Deng… - IEEE Internet of …, 2024 - ieeexplore.ieee.org
Machine learning (ML) sees an increasing prevalence of being used in the Internet of Things
(IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must be …

5g network slicing: Analysis of multiple machine learning classifiers

M Malkoc, HA Kholidy - arXiv preprint arXiv:2310.01747, 2023 - arxiv.org
The division of one physical 5G communications infrastructure into several virtual network
slices with distinct characteristics such as bandwidth, latency, reliability, security, and service …

Innovative Routing Solutions: Centralized Hypercube Routing Among Multiple Clusters in 5G Networks

AA Abushgra, HA Kholidy… - 2023 20th ACS/IEEE …, 2023 - ieeexplore.ieee.org
In the ever-evolving landscape of real-time usage, there is a constant pursuit of
advancements in infrastructures and technologies to meet the growing demand for network …

Secure the 5G and beyond networks with zero trust and access control systems for cloud native architectures

HA Kholidy, K Disen, A Karam… - 2023 20th ACS/IEEE …, 2023 - ieeexplore.ieee.org
5G networks are highly distributed, built on an open service-based architecture that requires
multi-vendor hardware and software development environments, all of which create a high …

SIoTSim: Simulator for Social Internet of Things

MC Zouzou, M Shahawy, E Benkhelifa… - … on Internet of Things …, 2023 - ieeexplore.ieee.org
The Social Internet of Things (SIoT) concept extends beyond the Internet of Things (IoT) by
integrating principles from social networking to form interconnected networks of intelligent …

[PDF][PDF] Current 5G Federation Trends: A Literature Review

A Fox, HA Kholidy, I Almazyad - 2023 - easychair.org
5G is the latest generation of mobile networks, developed with the purpose of faster
communication, more spectrum use, and lower latency. With 5G in such high demand, the …

Deep Anomaly Detection Framework Utilizing Federated Learning for Electricity Theft Zero-Day Cyberattacks

A Alshehri, MM Badr, M Baza, H Alshahrani - Sensors, 2024 - mdpi.com
Smart power grids suffer from electricity theft cyber-attacks, where malicious consumers
compromise their smart meters (SMs) to downscale the reported electricity consumption …

Adaptative Access Management in 5G IoE using Device Fingerprinting: Discourse, Mechanisms, Challenges, and Opportunities

S Oukemeni, M Elkotob - 2023 20th ACS/IEEE International …, 2023 - ieeexplore.ieee.org
As we move towards the era of 6G and IoE (Internet of Everything), there is more and more
seamless interconnection and interaction among things, people, data, and processes in one …

A Distillation-Based Attack Against Adversarial Training Defense for Smart Grid Federated Learning

AH Bondok, M Mahmoud, MM Badr… - 2024 IEEE 21st …, 2024 - ieeexplore.ieee.org
In the advanced metering infrastructure (AMI) of the smart grid, smart meters (SMs) are
deployed to collect fine-grained electricity consumption data, enabling billing, load …

Electricity Theft Detection Approach Using One-Class Classification for AMI

M Miller, H Habbak, M Badr, M Baza… - 2024 IEEE 21st …, 2024 - ieeexplore.ieee.org
The utilization of Advanced Metering Infrastructure (AMI) technology is for recording and
billing customers for electricity consumption. This technology is vulnerable to cyber-attacks …