Datasets, Simulator, Literature Review, & Research Sample. Let's Collaborate!

CIDS Research Project | Linktree

What is CIDS?

Read CIDS with DLT

For a more in-depth understanding of CIDS, particularly its integration with Distributed Ledger Technology (DLT), you may find the Systematization of Knowledge (SoK) study at the link below valuable. This research provides insights into the current landscape and future directions of CIDS with DLT, serving as a useful reference for further exploration in this domain.

CIDS with Distributer Ledger Technology

Read CIDS with FL

For a more in-depth understanding of CIDS, particularly its implementation with Federated Learning, you may find the Systematization of Knowledge (SoK) study at the link below valuable. This research provides insights into the current landscape and future directions of CIDS with Federated Learning, serving as a useful reference for further exploration in this domain.

CIDS with Federated Learning

Simulator and Dataset

Explore Simulator

CIDS-Sim is a powerful simulator for Collaborative Intrusion Detection Systems (CIDS) using Federated Learning. Explore and experiment with it to gain deeper insights into how CIDS operates!

CIDS-Sim

Explore Dataset

The CoAt-Set dataset is a specially curated dataset designed for collaborative anomaly detection in Collaborative Intrusion Detection Systems (CIDS). Explore its potential to enhance your research in cybersecurity!

CoAt-Set

Research Sample

Read This Research

This research proposes an Intrusion Detection System (IDS) by ensemble-ing traffic from heterogeneous IoT devices. This research proposes Deep Neural Network (DNN) to create a training model from each heterogeneous IoT device. After that, each training model from each heterogeneous IoT device is used to predict the traffic. The prediction results from each training model are averaged using the ensemble averaging method to determine the final result. 

Ensemble averaging deep neural network for botnet detection in heterogeneous Internet of Things devices

This research introduces a pioneering approach, termed Hierarchical Collaborative Botnet Detection, leveraging Federated Deep Learning to address the escalating security concerns within the Internet of Things (IoT) ecosystems characterized by heterogeneous devices.

Collaborative Botnet Detection in Heterogeneous Devices of Internet of Things using Federated Deep Learning

This research introduces a comprehensive collaborative intrusion detection system (CIDS) framework aimed at bolstering the security of Internet of Things (IoT) environments by synergistically integrating lightweight architecture, trust management, and privacy-preserving mechanisms.

Lightweight, Trust-Managing, and Privacy-Preserving Collaborative Intrusion Detection for Internet of Things

This research introduces a novel Collaborative Intrusion Detection System (CIDS) using a Weighted Ensemble Averaging Deep Neural Network (WEA-DNN) designed to detect coordinated attack in heterogeneous networks with diverse devices and systems.

Collaborative intrusion detection using weighted ensemble averaging deep neural network for coordinated attack detection in heterogeneous network

This research explores using a Federated Random Forest (FRF) model with feature selection techniques to improve collaborative intrusion detection in Internet of Things (IoT) settings.

Federated Random Forest with Feature Selection for Collaborative Intrusion Detection in Internet of Things

This research proposes a Collaborative Intrusion Detection System (CIDS) named Stacked Network Integration CIDS (SNI-CIDS), which Modified Ensemble Stacking Deep Neural Networks (MES-DNN) to enhance intrusion detection in heterogeneous networks.

SNI-CIDS: Collaborative intrusion detection using modified ensemble stacking deep neural networks for new network integration in heterogeneous networks

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CIDS Research Project

Datasets, Simulator, Literature Review, & Research Sample. Let's Collaborate!

What is CIDS?

Simulator and Dataset

Research Sample

Contact Me!

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