Robust Kalman Filter for Position Estimation of Automated Guided Vehicles Under Cyberattacks

Elsisi, Mahmoud; Altius, Marnel; Su, Shun‐Feng; Su, Chun‐Lien

doi:10.1109/tim.2023.3250285

Cited by 38 publications

(3 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Successfully addressing these vulnerabilities necessitates an in-depth understanding of potential entry points for attacks and their essences on MASS operations. Effectively mitigating these vulnerabilities has become crucial for ensuring autonomous maritime systems' safety, security, and reliability [40]. MASS vessels rely on sophisticated electrical and digital technologies for control, navigation, and communication, all operating without direct human interaction.…”

Section: Cybersecurity Vulnerabilities In Massmentioning

confidence: 99%

Maritime Autonomous Surface Ships: A Review of Cybersecurity Challenges, Countermeasures, and Future Perspectives

Tabish,

Chaur-Luh

2024

IEEE Access

View full text Add to dashboard Cite

Maritime Autonomous Surface Ships (MASS) have brought a transformative wave in the marine industry, yielding unprecedented operational advancements and efficiency gains. However, this paradigm shift has concurrently raised substantial cybersecurity concerns that demand meticulous attention to mitigate the impact of attacks on MASS stability. This study presents a comprehensive examination of the cybersecurity landscape, shedding light on prevalent issues concerning the data independence, integrity, and connectivity of MASS. The effectiveness of existing defense strategies against both inbound and outbound cyberattacks, focusing on attack detection and classification, is rigorously scrutinized. This analysis encompasses regulatory frameworks, encryption methods, intrusion detection systems, and a systematic approach to identifying and categorizing cyber threats. Additionally, the study anticipates future developments in cyber threats and proposes innovative solutions to fortify MASS defenses against potential cybersecurity vulnerabilities. To align with cybersecurity standards, this paper integrates challenges, possible solutions, and future advancements in a structured and cohesive manner, ensuring a thorough understanding of the intricate cybersecurity ecosystem surrounding Maritime Autonomous Surface Ships.

show abstract

Section: Cybersecurity Vulnerabilities In Massmentioning

confidence: 99%

Maritime Autonomous Surface Ships: A Review of Cybersecurity Challenges, Countermeasures, and Future Perspectives

Tabish,

Chaur-Luh

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Tran et al [16,17] utilized DNN for fault detection in instant messaging and proposed an integrated IoT architecture based on deep neural networks, showcasing superior performance and ease of implementation compared to state-of-the-art algorithms. Additionally, statistical methods have been used to establish equivalence between Kalman flters and specifc least squares regression problems, enabling the construction of robust Kalman flters for solving state estimation problems caused by various network attacks such as pulse, ramp, and DoS attacks [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Forest-Based E-Commerce Recommendation Attack Detection Model

Mingxun,

Jiewu,

Zhigang

et al. 2023

Security and Communication Networks

View full text Add to dashboard Cite

With the advancement of Internet, recommendation systems have become an indispensable component for every e-commerce platform, playing an increasingly pivotal role in product recommendations. However, due to the recommendation mechanisms of these systems, numerous new attack patterns have emerged. A novel attack pattern targeting e-commerce recommendation systems, termed the “Ride Item’s Coattails” attack, fabricates false click information to deceitfully establish associations between popular products and low-quality products, intending to mislead the e-commerce platform’s recommendation system and promote the sales of substandard products. This article presents a recommendation system attack detection method based on the Deep Forest algorithm to address the challenges of these novel recommendation system attacks. Random forests are used for feature selection, aiming to filter crucial features and reduce feature redundancy. To tackle the issue of extreme class imbalance, a symmetric sampling technique based on k-means centroids is introduced. This approach addresses the incomplete noise filtering and sampling data comprehensiveness issues commonly found in undersampling algorithms. Considering the potential for even more imbalanced data in real-world scenarios, a combined strategy of undersampling and SMOTE resampling is used to handle imbalanced data. The proposed algorithm was trained on e-commerce “Ride Item’s Coattails” attack identification data from Alibaba Cloud’s Tianchi, which originates from genuine recommendation system attack data. The proposed method was compared with various deep learning and machine learning algorithms, such as DLMP and deep neural networks (DNN), for extensive validation. Experiments demonstrate that the proposed technique effectively meets the demands for attack detection.

show abstract

“…Therefore, the main issue to be solved in this paper is to use speed measurement to exclude the interference of fixed targets and calculate the speed and distance of moving targets at the same time. Velocity can be used to measure the motion characteristics of the target (Essa et al [4], Bergies et al [5]) and can be used as a factor of Kalman filter tracking to form a stable tracking trajectory (Elsisi et al [6], Elsisi et al [7]). Measuring the velocity of a target to the ground is an important basis for judging the existence of an underwater target.…”

Section: Introductionmentioning

confidence: 99%

VCH: A Velocity Measurement Method Combining HFM Signals

Yan,

Song,

Peng

et al. 2024

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

During sonar detection, the fuzzy function of hyperbolic frequency modulation (HFM) in the waveform of active signal is in the shape of a “oblique blade,” which leads to the coupling characteristics of its fuzzy function in the time dimension and frequency dimension. Doppler causes a frequency shift in the frequency dimension and a time delay in the time dimension, which makes it impossible for a single HFM signal to measure speed accurately. However, the time delay caused by the same moving target to HFM signals with different frequency bands and different pulse widths is also different. A velocity measurement method combining HFM signals (VCH) is proposed, which employs the time delay correlation between the combined HFM signals to achieve the target distance and speed. In the VCH method, the echo signal is no longer matched and filtered as a whole but is divided into two channels and matched and filtered separately. And then, the combined HFM signals are used to obtain the distance and speed of the target. Extensive simulation results show that the proposed method can estimate the distance and speed of moving targets accurately, and it has reference value for engineering application.

show abstract

Robust Kalman Filter for Position Estimation of Automated Guided Vehicles Under Cyberattacks

Cited by 38 publications

References 37 publications

Maritime Autonomous Surface Ships: A Review of Cybersecurity Challenges, Countermeasures, and Future Perspectives

Maritime Autonomous Surface Ships: A Review of Cybersecurity Challenges, Countermeasures, and Future Perspectives

Deep Forest-Based E-Commerce Recommendation Attack Detection Model

VCH: A Velocity Measurement Method Combining HFM Signals

Contact Info

Product

Resources

About