Machine Learning Models for Heterogenous Network Security ...

The article contributes a novel ML-based approach to network security by developing a model specifically designed for detecting anomalies in network traffic.

This project aims to greatly strengthen network security by addressing emerging cyber threats and improving their resilience and reliability.

Efficacy of Heterogeneous Ensemble Assisted Machine Learning ...

As solution data-driven machine learning methods have exhibited better by learning over network traffic information and detecting anomalies; however, its ...

Collabo: A Collaborative Machine Learning Model and its ... - Authorea

... cybersecurity engineeringmachine learning

Privacy-preserving federated machine learning modeling and ...

While many researchers focus on improving the performance of ML models, the cybersecurity of ML modeling approaches has attracted increasing attention, as the ...

Federated Learning with Heterogeneous Models for On-device ...

Federated Learning with Heterogeneous Models for On-device Malware Detection in IoT Networks ... Abstract: IoT devices have been widely deployed in many ...

Deep Learning-Enabled Heterogeneous Transfer Learning for ...

Cybersecurity faces constant challenges from increasingly sophisticated network attacks. Recent research shows machine learning can improve attack detection ...

Detection of Network Attacks in a Heterogeneous Industrial Network ...

Variants of constructing ensembles of classifiers based on machine learning models and heterogeneous neural network models are analyzed. The F1 score for test ...

Generalizing intrusion detection for heterogeneous networks - arXiv

supervised learning algorithms (classic and deep learning), [25] also reports ... Shu, Federated deep learning for cyber security in the internet of things: ...

A stacked ensemble learning model using heterogeneous base ...

In network intrusion detection, using a machine learning method alone has blind spots and low detection accuracy.

Machine Learning-Based Security Enhancement in Heterogeneous ...

The suggested approach uses Autoencoder (AE), a technique for machine learning that uses feature extraction along with principal component ...

Federated Machine Learning-Based Anomaly Detection System for ...

The proposed methodology includes training local models using heterogeneous data sets that include network and grid information and updating the global model ...

A deep residual computation model for heterogeneous data learning ...

Results show that the proposed model produces more accurate classification results than other models for heterogeneous data feature learning in cyber–physical– ...

A deep learning‐based framework to identify and characterise ...

Mainly flow-based features and packet-based features of secure network traffic flows are deployed to machine learning engines to identify the ...

Harnessing Dynamic Heterogeneous Redundancy to Empower ...

Abstract The rapid development of deep learning (DL) models has been accompanied by various safety and security challenges, such as adversarial attacks and ...

(PDF) Collabo: A Collaborative Machine Learning Model and its ...

Collabo: A Collaborative Machine Learning Model and its Application to the Security of Heterogeneous Medical Data in an IoT Network. April 2023. DOI:10.36227 ...

Evaluation of Malware Classification Models for Heterogeneous Data

Over the years, ML methods for identifying malicious software have been developed across various security domains. However, recent research has ...

How to Train Machine Learning Models with Heterogeneous Data

Choose a model that can handle heterogeneous data effectively, such as ensemble methods or neural networks with multiple input layers. Represent ...

Machine learning-based security-aware spatial modulation for ...

System model. A heterogeneous radio-optical network is considered that consists of a number of legitimate users (L) and eavesdroppers (E), as ...

Improving detection of scanning attacks on heterogeneous networks ...

Scanning attacks are the first step in the attempt to compromise the security of systems. Machine learning (ML) has been used for network ...