Improving Knowledge Based Detection of Soft Attacks Against Autonomous Vehicles with Reputation, Trust and Data Quality Service Models

Chuprov, Sergei; Viksnin, Ilya I.; Kim, Iuliia; Melnikov, Timofey; Reznik, Leon; Khokhlov, Igor

doi:10.1109/smds53860.2021.00025

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…Data may also contain inconsistencies and discrepancies, and noises, which may lead to unreliable output. Therefore, data cleansing is an essential aspect of database management to minimize the risks of bias resulting from the storage inconsistencies, especially for large data sets [3], [4]. In the process of data analytics by machine learning, shortage of data is the most prevalent challenge faced in developing predictive models.…”

Section:  Issn: 2089-3272mentioning

confidence: 99%

The Effect of Using Data Pre-Processing by Imputations in Handling Missing Values

Karrar

2022

IJEEI

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The evolution of big data analytics through machine learning and artificial intelligence techniques has caused organizations in a wide range of sectors including health, manufacturing, e-commerce, governance, and social welfare to realize the value of massive volumes of data accumulating on web-based repositories daily. This has led to the adoption of data-driven decision models; for example, through sentiment analysis in marketing where produces leverage customer feedback and reviews to develop customer-oriented products. However, the data generated in real-world activities is subject to errors resulting from inaccurate measurements or fault input devices, which may result in the loss of some values. Missing attribute/variable values make data unsuitable for decision analytics due to noises and inconsistencies that create bias. The objective of this paper was to explore the problem of missing data and develop an advanced imputation model based on Machine Learning and implemented on K-Nearest Neighbor (KNN) algorithm in R programming language as an approach to handle missing values. The methodology used in this paper relied on the applying advanced machine learning algorithms with high-level accuracy in pattern detection and predictive analytics on the existing imputation techniques, which handle missing values by random replacement or deletion.. According to the results, advanced imputation technique based on machine learning models replaced missing values from a dataset with 89.5% accuracy. The experimental results showed that pre-processing by imputation delivers high-level performance efficiency in handling missing data values. These findings are consistent with the key idea of paper, which is to explore alternative imputation techniques for handling missing values to improve the accuracy and reliability of decision insights extracted from datasets.

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Section:  Issn: 2089-3272mentioning

confidence: 99%

The Effect of Using Data Pre-Processing by Imputations in Handling Missing Values

Karrar

2022

IJEEI

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“…The data from multi-modality sensors, such as an on-board camera and an inertial measurement units (IMUs), might be fused in order to determine the AV's position in relation to the surrounding objects. However, these sensor devices might also fail or be maliciously attacked, which leads to data quality (DQ) degradation and jeopardizes the AV's security and safety [2,3]. Obviously, the localization accuracy in this case depends on the quality of data acquired from the sensors.…”

Section: Introductionmentioning

confidence: 99%

Robust Autonomous Vehicle Computer-Vision-Based Localization in Challenging Environmental Conditions

et al. 2023

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In this paper, we present a novel autonomous vehicle (AV) localization design and its implementation, which we recommend to employ in challenging navigation conditions with a poor quality of the satellite navigation system signals and computer vision images. In the case when the GPS signal becomes unstable, other auxiliary navigation systems, such as computer-vision-based positioning, are employed for more accurate localization and mapping. However, the quality of data obtained from AV’s sensors might be deteriorated by the extreme environmental conditions too, which infinitely leads to the decrease in navigation performance. To verify our computer-vision-based localization system design, we considered the Arctic region use case, which poses additional challenges for the AV’s navigation and might employ artificial visual landmarks for improving the localization quality, which we used for the computer vision training. We further enhanced our data by applying affine transformations to increase its diversity. We selected YOLOv4 image detection architecture for our system design, as it demonstrated the highest performance in our experiments. For the computational platform, we employed a Nvidia Jetson AGX Xavier device, as it is well known and widely used in robotic and AV computer vision, as well as deep learning applications. Our empirical study showed that the proposed computer vision system that was further trained on the dataset enhanced by affine transformations became robust regarding image quality degradation caused by extreme environmental conditions. It was effectively able to detect and recognize images of artificial visual landmarks captured in the extreme Arctic region’s conditions. The developed system can be integrated into vehicle navigation facilities to improve their effectiveness and efficiency and to prevent possible navigation performance deterioration.

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Trust-Based Anomaly Detection in Federated Edge Learning

Zatsarenko,

Chuprov,

Korobeinikov

et al. 2024

2024 IEEE World AI IoT Congress (AIIoT)

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Improving Knowledge Based Detection of Soft Attacks Against Autonomous Vehicles with Reputation, Trust and Data Quality Service Models

Cited by 5 publications

References 17 publications

The Effect of Using Data Pre-Processing by Imputations in Handling Missing Values

The Effect of Using Data Pre-Processing by Imputations in Handling Missing Values

Robust Autonomous Vehicle Computer-Vision-Based Localization in Challenging Environmental Conditions

Trust-Based Anomaly Detection in Federated Edge Learning

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