Deep Learning Algorithms for Secure Robot Face Recognition in Cloud Environments

Chiranjeevi, K.; Naidu, M.S.R.

doi:10.1109/ispa-bdcloud-socialcom-sustaincom51426.2020.00154

Cited by 10 publications

(2 citation statements)

References 35 publications

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“…The effectiveness of the network and automated intrusion detection had been either greatly improved. The main advantage of the aforementioned structure is that it reduces the level of computation without reducing cost [19].…”

Section: Abirami Et Al (2022) Demonstrated How "Deepmentioning

confidence: 99%

Ensembling of Attention-based Recurrent Units for Detection and Mitigation of Multiple Attacks in Cloud

2023

IJACSA

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In the recent years, number of threats to network security increases exponentially as the Internet users which poses serious threat in cloud storage application. Detection and defending against the multiple threats are currently a hot topic in industry and considered as one of the challenging research in academia. Many methodologies and algorithms devised to predict the different attacks. Still, most of the methods cannot simultaneously achieve high performance of prediction with a small number of false alarm rates. In this scenario, Deep Learning (DL) algorithms are appropriate and intelligent to categorize the multiple attacks. Still, most of the existing DL techniques are computationally inefficient that may degrade the performance in predicting the both normal and attack information. To overcome this aforementioned problem, this paper proposes the hybrid combination of attention maps with deep recurrent networks to mitigate the multiple attacks with low computational overhead. Initially, the pre-processing step is proposed to the inputs in a specified range. Later on, input data are fed into the Attention Enabled Gated Recurrent Networks (AEGRN) which is used to remove the redundant features and select the optimal features that aids for the better classification. Further to enhance the faster response, deep feed forward layers are proposed to replace the traditional deep neural networks. Numerous metrics for performance, including accuracy, precision, recall, specificity, and F1-score, are examined and analyzed as part of the thorough experimentation utilizing multiple datasets, including NSL-KDD-99, UNSW -2019, and CIDC-001. Comparisons of performance between the method that is suggested and existing models developed with DL are used to demonstrate the proposed algorithm's supremacy. The suggested framework surpasses the other DL models and has the best accuracy in predicting with little computational overhead, according to an investigation.

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Section: Abirami Et Al (2022) Demonstrated How "Deepmentioning

confidence: 99%

Ensembling of Attention-based Recurrent Units for Detection and Mitigation of Multiple Attacks in Cloud

2023

IJACSA

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“…In deep learning networks on a bare metal cloud-based, a system called License Plate Recognition System (LPRS) has been constructed and introduced in [339]. the purposes of this system are plate localization, character detection, and segmentation as opposed to [340] that authors have tried to improve the speed and accuracy of facial recognition without losing the privacy of robots in the encrypted domain. Similar to [341] that a model of cloud data repository has been introduced that has trained with mobile data dataset through Training dataset Filtration Key Nearest Neighbor (TsF-KNN) and public-key cryptographic algorithms.…”

Section: Ai In Secure Cloudmentioning

confidence: 99%

Privacy-Preserving Cloud Computing: Ecosystem, Life Cycle, Layered Architecture and Future Roadmap

Ahmadi¹,

Salehfar²

2022

Preprint

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Privacy-Preserving Cloud Computing is an emerging technology with many applications in various fields. Cloud computing is important because it allows for scalability, adaptability, and improved security. Likewise, privacy in cloud computing is important because it ensures that the integrity of data stored on the cloud maintains intact. This survey paper on privacy-preserving cloud computing can help pave the way for future research in related areas. This paper helps to identify existing trends by establishing a layered architecture along with a life cycle and an ecosystem for privacy-preserving cloud systems in addition to identifying the existing trends in research on this area.

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Semantic Segmentation of MRI Images for Brain Tumour Detection with ShuffleNet-Based UNet

Potnuru

Naick

2023

SN COMPUT. SCI.

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Deep Learning Algorithms for Secure Robot Face Recognition in Cloud Environments

Cited by 10 publications

References 35 publications

Ensembling of Attention-based Recurrent Units for Detection and Mitigation of Multiple Attacks in Cloud

Ensembling of Attention-based Recurrent Units for Detection and Mitigation of Multiple Attacks in Cloud

Privacy-Preserving Cloud Computing: Ecosystem, Life Cycle, Layered Architecture and Future Roadmap

Semantic Segmentation of MRI Images for Brain Tumour Detection with ShuffleNet-Based UNet

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