CoChat
Sign Up
Cited by 38Network Security and Intrusion DetectionAdversarial Robustness in Machine LearningArtificial Immune Systems Applications

The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.

Classification of Normal and Malicious Traffic Based on an Ensemble of Machine Learning for a Vehicle CAN-Network

Open access
Alalwany, Easa, Mahgoub, Imad·2022·Sensors
DOI 10.3390/s22239195PMC PMC9736208
Open this paper in CoChat — free accountOpen PDF

Abstract

Connectivity and automation have expanded with the development of autonomous vehicle technology. One of several automotive serial protocols that can be used in a wide range of vehicles is the controller area network (CAN). The growing functionality and connectivity of modern vehicles make them more vulnerable to cyberattacks aimed at vehicular networks. The CAN bus protocol is vulnerable to numerous attacks, as it is lacking security mechanisms by design. It is crucial to design intrusion detection systems (IDS) with high accuracy to detect attacks on the CAN bus. In this paper, we design an effective machine learning-based IDS scheme for binary classification that utilizes eight supervised ML algorithms, along with ensemble classifiers. The scheme achieved a higher effectiveness score in detecting normal and abnormal activities when trained with normal and malicious CAN traffic datasets. Random Forest, Decision Tree, and Xtreme Gradient Boosting classifiers provided the most accurate results. Then we evaluated three ensemble methods, voting, stacking, and bagging, for this classification task. The ensemble classifiers achieved better accuracy than the individual models, since ensemble learning strategies have superior performance through a combination of multiple learning mechanisms. These mechanisms have a varied range of capabilities that improve the prediction reliability while lowering the possibility of classification errors. Our model outperformed the most recent study that used the same dataset, with an accuracy of 0.984.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/s22239195

Cites

37 of 37 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 The structure of the CAN bus frame.
Figure 1 The structure of the CAN bus frame.CC BY · PubMed Central
Figure 2 The overview of the workflow with the supervised models.
Figure 2 The overview of the workflow with the supervised models.CC BY · PubMed Central
Figure 3 Our proposed IDS scheme development and evaluation steps.
Figure 3 Our proposed IDS scheme development and evaluation steps.CC BY · PubMed Central
Figure 4 The overview of workflow of ensemble classifiers.
Figure 4 The overview of workflow of ensemble classifiers.CC BY · PubMed Central
Figure 5 The ROC results for supervised models.
Figure 5 The ROC results for supervised models.CC BY · PubMed Central
Figure 6 The ROC results for the ensemble classifier models.
Figure 6 The ROC results for the ensemble classifier models.CC BY · PubMed Central
Figure 7 (a) The confusion matrix of the stacking. (b) The confusion matrix of the voting. (c) The confusion matrix of the bagging.
Figure 7 (a) The confusion matrix of the stacking. (b) The confusion matrix of the voting. (c) The confusion matrix of the bagging.CC BY · PubMed Central
Figure 8 The performance of ensemble classifiers compared to supervised models.
Figure 8 The performance of ensemble classifiers compared to supervised models.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 4

  1. Avatefipour, O., and Malik, H. (2018). State-of-the-art survey on in-vehicle network communication (CAN-Bus) security and vulnerabilities. arXiv.
  2. Kang, H., Kwak, B., Lee, Y., Lee, H., Lee, H., and Kim, H. (2021). Car Hacking: Attack and Defense Challenge 2020 Dataset. IEEE Dataport, IEEE.
  3. Dietterich, 2002, Ensemble learning, Handb. Brain Theory Neural Netw., 2, 110
  4. Quinlan, J.R. (1996, January 4–8). Bagging, boosting, and C4.5. Proceedings of the AAAI/IAAI, Portland, OR, USA.

Cited by

See all 38

Related

See all

Metadata

Authors
Alalwany, E., & Mahgoub, I.
Affiliations
  • Taibah University (SA)
  • Florida Atlantic University (US)
Corresponding author
Mahgoub, Imad
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2022-11-26
Volume
22
Issue
23
Pages
9195
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s22239195
PMC
PMC9736208
OpenAlex
W4310153907
Cited by
36
References
36
Citations by year
2026: 7 · 2025: 11 · 2024: 12 · 2023: 6
FWCI
3.22
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Florida Atlantic University
  • Universidad del Atlántico
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Immune Systems Applications
Keywords
CAN bus, VANET security, intrusion detection, malicious traffic classification, ensemble classifiers, stacking, voting, bagging, random forest, XGBoost, decision tree, autonomous vehicles
MeSH terms
Machine Learning, Algorithms, Reproducibility of Results

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review