A Machine Learning-Based Active Learning Framework to Capture Risk and Uncertainty in Transportation and Construction Scheduling

Jha, Manoj K.; Wanko, Nicodeme; Kumar, B. Anil

doi:10.1007/978-981-99-2556-8_13

Cited by 1 publication

(2 citation statements)

References 17 publications

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“…In the practical implementation of the ML model for real-time application, it is acknowledged that inaccuracies, such as FPs or FNs, may arise. Addressing these challenges could involve exploring an active learning paradigm, as suggested in previous works, to minimize label mismatches when dealing with unstructured datasets (Jha et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

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A Machine Learning Model to Predict Cyberattacks in Connected and Autonomous Vehicles

Jha,

Jaiswal

2024

JCCE

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Connected and autonomous vehicles (CAVs) are largely at the experimental stage. Their successful deployment and field implementation require a careful consideration of their vulnerabilities to cyberattacks. The primary security vulnerability is in the controller area network (CAN) protocol, which permits communication among electronic control units in CAVs. To address this vulnerability and mitigate cyberattacks, machine learning (ML) algorithms can be developed for intrusion detection in CAV's CAN protocol. In this research, the data structure of certain experimental datasets on message injection attack from the Hacking and Countermeasure Research Lab is examined. A random forest classifier-based ML model is developed owing to its efficiency in predicting cyberattacks on CAVs consisting of over 3 million datasets. A number of procedures within the Python programming environment are employed to clean the dataset before performing the prediction. The prediction for intrusion detection is performed with a 70:30 split of the training: testing data with a random state of 11 and number of estimators as 200. The accuracy is found to be over 92% for all three scenarios in performing the prediction. The model can be deployed in real-time investigation of cyberattacks in CAVs if real-time data were available. The data cleaning method developed in this study can be applied in other ML applications consisting of large datasets, such as credit card fraud and drug discovery, to name a few.

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Section: Discussionmentioning

confidence: 99%

“…They offered an algorithm that used an error-reduction sampling estimation. The first author extended the active learning concept called modular active learning (modAL) for minimizing risk and uncertainty in transportation and construction scheduling (Jha et al, 2023).…”

Section: For Intrusion Detectionmentioning

confidence: 99%

A Machine Learning Model to Predict Cyberattacks in Connected and Autonomous Vehicles

Jha,

Jaiswal

2024

JCCE

View full text Add to dashboard Cite

show abstract

A Machine Learning-Based Active Learning Framework to Capture Risk and Uncertainty in Transportation and Construction Scheduling

Cited by 1 publication

References 17 publications

A Machine Learning Model to Predict Cyberattacks in Connected and Autonomous Vehicles

A Machine Learning Model to Predict Cyberattacks in Connected and Autonomous Vehicles

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