LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of Vehicles

@article{Yang2022LCCDEAD,
  title={LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of Vehicles},
  author={Li Yang and Abdallah Shami and Gary Stevens and Stephen De Rusett},
  journal={GLOBECOM 2022 - 2022 IEEE Global Communications Conference},
  year={2022},
  pages={3545-3550}
}
Modern vehicles, including autonomous vehicles and connected vehicles, have adopted an increasing variety of functionalities through connections and communications with other vehicles, smart devices, and infrastructures. However, the growing connectivity of the Internet of Vehicles (IoV) also increases the vulnerabilities to network attacks. To protect IoV systems against cyber threats, Intrusion Detection Systems (IDSs) that can identify malicious cyber-attacks have been developed using… 
[PDF] Semantic Reader
1 Citations
Methods Citations
1

Figures and Tables from this paper

One Citation

A Novel Malware Traffic Classification Method Based on Differentiable Architecture Search

This paper introduces a model which performs DARTS in the field of malicious traffic classification and search for optimal architecture based on network traffic datasets and compares the method with several common models.

References

SHOWING 1-10 OF 24 REFERENCES

MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles

A multitiered hybrid IDS that incorporates a signature- based IDS and an anomaly-based IDS is proposed to detect both known and unknown attacks on vehicular networks, and experimental results illustrate the feasibility of implementing the proposed system in real-time vehicle systems.

A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles

  • Li YangA. Shami
  • Computer Science
    ICC 2022 - IEEE International Conference on Communications
  • 2022
A transfer learning and ensemble learning-based IDS is proposed for IoV systems using convolutional neural networks (CNNs) and hyper-parameter optimization techniques and shows the effectiveness of the proposed IDS for cyber-attack detection in both intra-vehicle and external vehicular networks.

Intrusion Detection Systems for Intra-Vehicle Networks: A Review

This paper provides a structured and comprehensive review of the state of the art of the intra-vehicle intrusion detection systems (IDSs) for passenger vehicles and presents outstanding research challenges and gaps in intra-Vehicle IDS research.

All Predict Wisest Decides: A Novel Ensemble Method to Detect Intrusive Traffic in IoT Networks

This work proposes an innovative ensemble learning framework, namely All Predict Wisest Decides (APWD), that builds on training of multiple ML models and testing them independently so to obtain prediction performance for all classes.

Classification Approach for Intrusion Detection in Vehicle Systems

This paper presents machine learning techniques to cluster and classify the intrusions in VANET by KNN and SVM algorithms and presents intrusion detection technique which relies on the analysis of the offset ratio and time interval between the messages request and the response in the CAN.

GIDS: GAN based Intrusion Detection System for In-Vehicle Network

A novel IDS model for in-vehicle networks, GIDS (GAN based Intrusion Detection System) is proposed using deep-learning model, Generative Adversarial Nets, which can learn to detect unknown attacks using only normal data.

Novel Deep Learning-Enabled LSTM Autoencoder Architecture for Discovering Anomalous Events From Intelligent Transportation Systems

A deep learning-based Intrusion Detection System (IDS) for ITS, in particular, to discover suspicious network activity of In-Vehicles Networks (IVN), vehicles to vehicles communications and vehicles to infrastructure (V2I) networks.

In-vehicle network intrusion detection using deep convolutional neural network

Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection

A novel multi-stage optimized ML-based NIDS framework that reduces computational complexity while maintaining its detection performance and hyper-parameter optimization techniques are investigated to enhance the NIDS’s performance.

A Novel Intrusion Detection Method Based on Lightweight Neural Network for Internet of Things

A novel NID method for IoT based on the lightweight deep neural network (LNN) that has excellent classification performance with low model complexity and small model size, and it is suitable for classifying the IoT traffic of normal and attack scenarios.