Cluster-based attacks prevention algorithm for autonomous vehicles using machine learning algorithms

Sankaranarayanan, R.; Umadevi, K. S.; Bhavani, NPG; Jos, Bos Mathew; Haldorai, Anandakumar; Babu, D. Vijendra

doi:10.1016/j.compeleceng.2022.108088

Cited by 20 publications

(7 citation statements)

References 12 publications

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“…Likewise, a native VMM [23,24] can be considered as a unique OS [9] since it enables multi-programming across varying sorts of hybrid cloud VMs. The system calls, made by the VMM, are considered as hardware instructions from the VMM.…”

Section: Service Instance In Amazon's Ec2 Vmmentioning

confidence: 99%

Dynamic Load Predicted Failure Resource Allocation In Virtual Machines Using Hybrid cloud

Sudhakar

ANBARASU²,

Singh

et al. 2023

Preprint

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Hybrid cloud service is a novel phenomenon that is strategized to estimate the failures in network and configure the Virtual Machines (VMs) in a cost-effective and timely manner. Amazon hybrid cloud offers Infrastructure as a Service (IaaS) in which the infrastructure such as servers, storage network, VMs etc can be dynamically accessed by global users either through manual methods or through automated web-based monitoring and management applications or else through console. Hybrid cloud computing model has been developed with an aim to handle big data since the existing algorithms find it challenging to compute the tasks. The novel hybrid cloud computing model is a result of need for an efficient computational asset that can handle huge scale sequencing experiments. In general, failure is an unavoidable phenomenon in large-scale computing systems. However, when hybrid cloud computing servers continuously fail in executing the tasks, it becomes complicated to handle fault tolerance, availability and reliability. This scenario demands the development of a novel approach that can compensate data loss and ensure automated recovery of the lost data. Further, it should also ensure the non-occurrence of such instances in future. The approach proposed, must enhance the availability of the system and performance of the overall network using VMs. This fault tolerance mechanism was tested under different scenarios involving data, application, selection and matching with faults, as per the requirements of the user. The failure detection model should have the capability to reconfigure the VMs in case of a failure. In this study that dealt with hybrid cloud infrastructure, host hypervisor was used to compare and contrast the failed clusters. The proposed prediction-recovery algorithm was able to identify superior components to achieve file delivery. Further, the AWS failure detection method used an estimation algorithm to achieve dynamic allocation of VMs. The occurrence of failure was hypothesized on the basis of failure dynamics. Further, failure prediction outcomes of the proposed model were also investigated.

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Section: Service Instance In Amazon's Ec2 Vmmentioning

confidence: 99%

Dynamic Load Predicted Failure Resource Allocation In Virtual Machines Using Hybrid cloud

Sudhakar

ANBARASU²,

Singh

et al. 2023

Preprint

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“…He is known for his completion analyses and theorems of consistency continuum hypothesis, as documented in [40]. This study employed an alternative methodology as opposed to the research conducted by [41], who posited that mathematical logic may show mathematical completeness. In the subsequent decades, [42] offered unequivocal verification of Godel's discoveries.…”

Section: Artificial Intelligence-brain Heuristic Comparisonmentioning

confidence: 99%

Theoretical Analysis of the Brain and Artificial Intelligence

Pedro

2023

JRS

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Many articles have expounded on and defended the potential advantages of co-robotics (cobots), robotics, AI, and quantum computers in the domains of research and development, clinics, community health and virology. Numerous trailblazers in the domains of artificial intelligence, robotics, and quantum computing have been recognised for their groundbreaking concepts and principles. Among these luminaries are Richard Feynman, Kurt Godel, John Nash, Norbert Wiener, Alan Turing, John von Neumann, Vannevar Bush, and John McCarthy. Theorems formulated by Kurt Godel were misinterpreted by researchers who erroneously equated computer and brain paradigms. Godel himself had recognised this misinterpretation. The individual's commendation of the brain's supremacy over computational systems was met with disapprobation. This article delineates the diverse array of artificial intelligence techniques, frameworks, and programming languages that are developed by humans and can be employed in tandem with contemporary computational systems. These advancements facilitate advancements in the realm of electrons and quantum mechanics. The process of evolution has resulted in the development of neurons in various animal species, which rely on the flow of electrons to carry out their biological functions. The identification of mirror neurons represented a significant shift in the paradigm of neuroscience. The proposed paradigm shift towards the 'hall of mirror neurons' represents a potentially effective approach to studying, warranting further investigation. The aforementioned concepts are instrumental in advancing the field of artificial intelligence and in furthering research on the intricacies of the human brain.

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“…The primary aim of this data processing task is to identify and choose the most effective characteristics that result in the highest level of differentiation among the various flights within the dataset. Various approaches, including Branch and Bound, Floating Sequential search, and Backward & Forward selection, are used to exhaustively explore all possible combinations of characteristics [31]. An exhaustive examination of these methodologies beyond the boundaries of this research article [32].…”

Section: Feature Selectionmentioning

confidence: 99%

Deep Learning and Machine Learning Algorithms for Enhanced Aircraft Maintenance and Flight Data Analysis

Helgo

2023

JRS

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This paper examines the use of machine learning and deep learning algorithms in the aviation industry, with a specific emphasis on aircraft diagnosis/prognosis, predictive maintenance, feature selection, and flight data monitoring (FDM). This study highlights the potential use of these algorithms in enhancing the efficacy and effectiveness of various aircraft operations. In the field of aviation prognosis and diagnosis, many designs have been acknowledged as efficient for defect identification, calculation of remaining usable life, and prediction of excessive vibration in aero-engines. The architectural models discussed in this paper include deep autoencoders, deep belief networks, long short-term memory networks, and convolutional neural networks. The use of feature selection and scalar feature selection methodologies has been seen to augment the efficacy of FDM (Feature Detection and Matching) algorithms by means of identifying noteworthy features and detecting highly linked features. The application of machine learning algorithms in the domain of predictive maintenance enables real-time assessment of equipment health, hence reducing possible hazards and improving overall equipment performance. The research results emphasize the importance of flight data monitoring in improving safety and operational efficiency in the field of civil aviation. The application of machine learning approaches, namely classification algorithms, facilitates the analysis of flight data for the aim of identifying unsafe behaviors or violations from established operational standards.

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Cluster-based attacks prevention algorithm for autonomous vehicles using machine learning algorithms

Cited by 20 publications

References 12 publications

Dynamic Load Predicted Failure Resource Allocation In Virtual Machines Using Hybrid cloud

Dynamic Load Predicted Failure Resource Allocation In Virtual Machines Using Hybrid cloud

Theoretical Analysis of the Brain and Artificial Intelligence

Deep Learning and Machine Learning Algorithms for Enhanced Aircraft Maintenance and Flight Data Analysis

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