A secured optimised AOMDV routing protocol in MANET using lightweight continuous multimodal biometric authentication

Brindha, N.; Meenakshi, V.

doi:10.1007/s12652-022-03836-7

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…The fixed vector readies the completelyrelated layer, which is like that of artificial neural network (ANN). The readiness of vector is done using as (16).…”

Section: Sequential Feature Score Rank Fusionmentioning

confidence: 99%

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Optimizing dual modal biometric authentication: hybrid HPO-ANFIS and HPO-CNN framework

Singh,

Tiwari

2024

IJEECS

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In the realm of secure data access, biometric authentication frameworks are vital. This work proposes a hybrid model, with a 90% confidence interval, that combines "hyperparameter optimization-adaptive neuro-fuzzy inference system (HPO-ANFIS)" parallel and "hyperparameter optimization-convolutional neural network (HPO-CNN)" sequential techniques. This approach addresses challenges in feature selection, hyperparameter optimization (HPO), and classification in dual multimodal biometric authentication. HPO-ANFIS optimizes feature selection, enhancing discriminative abilities, resulting in improved accuracy and reduced false acceptance and rejection rates in the parallel modal architecture. Meanwhile, HPO-CNN focuses on optimizing network designs and parameters in the sequential modal architecture. The hybrid model's 90% confidence interval ensures accurate and statistically significant performance evaluation, enhancing overall system accuracy, precision, recall, F1 score, and specificity. Through rigorous analysis and comparison, the hybrid model surpasses existing approaches across critical criteria, providing an advanced solution for secure and accurate biometric authentication.

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“…The fixed vector readies the completelyrelated layer, which is like that of artificial neural network (ANN). The readiness of vector is done using as (16).…”

Section: Sequential Feature Score Rank Fusionmentioning

confidence: 99%

“…They introduced unique feature extraction and matching algorithms that significantly improved the performance of fingerprint-based authentication systems. Brindha and Meenakshi [16] made significant contributions to multimodal biometrics. They did ground-breaking research in multimodal recognition techniques.…”

Section: Introductionmentioning

confidence: 99%

Optimizing dual modal biometric authentication: hybrid HPO-ANFIS and HPO-CNN framework

Singh,

Tiwari

2024

IJEECS

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“…Each image is saved in the "BMP" format, which has a dimension of 320 × 240 pixels. Hong Kong Polytechnic University's database is the FKP database (PolyU FKP database) [29]. A total of 7920 photos are included in this database, consisting of 12 images taken of the index as well as middle fingers of both hands of 165 individuals.…”

Section: Databasementioning

confidence: 99%

Biometric Authentication-Based Intrusion Detection Using Artificial Intelligence Internet of Things in Smart City

Annadurai

Nelson

Devi³

et al. 2022

Energies

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Nowadays, there is a growing demand for information security and security rules all across the world. Intrusion detection (ID) is a critical technique for detecting dangers in a network during data transmission. Artificial Intelligence (AI) methods support the Internet of Things (IoT) and smart cities by creating gadgets replicating intelligent behavior and enabling decision making with little or no human intervention. This research proposes novel technique for secure data transmission and detecting an intruder in a biometric authentication system by feature extraction with classification. Here, an intruder is detected by collecting the biometric database of the smart building based on the IoT. These biometric data are processed for noise removal, smoothening, and normalization. The processed data features are extracted using the kernel-based principal component analysis (KPCA). Then, the processed features are classified using the convolutional VGG−16 Net architecture. Then, the entire network is secured using a deterministic trust transfer protocol (DTTP). The suggested technique’s performance was calculated utilizing several measures, such as the accuracy, f-score, precision, recall, and RMSE. The simulation results revealed that the proposed method provides better intrusion detection outcomes.

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“…The strong data forwarding ability of the edge server is used to avoid selecting excessively long data transmission paths when the source node and destination node are not in the same trust domain. Finally, multi-path routing between nodes is established based on the Ad-hoc On-demand Multi-path Distance Vector (AOMDV) protocol [18] [19], and the link stability attributes of paths are collected.…”

Section: Introductionmentioning

confidence: 99%

Stability-based On-demand Multi-path Distance Vector Protocol for Edge Internet of Things

2023

KSII TIIS

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In edge computing scenarios, IoT end devices play a crucial role in relaying and forwarding data to significantly improve IoT network performance. However, traditional routing mechanisms are not applicable to this scenario due to differences in network size and environment. Therefore, it becomes crucial to establish an effective and reliable data transmission path to ensure secure communication between devices. In this paper, we propose a trusted path selection strategy that comprehensively considers multiple attributes, such as link stability and edge cooperation, and selects a stable and secure data transmission path based on the link life cycle, energy level, trust level, and authentication status. In addition, we propose the Stability-based On-demand Multipath Distance Vector (STAOMDV) protocol based on the Ad hoc AOMDV protocol. The STAOMDV protocol implements the collection and updating of link stability attributes during the route discovery and maintenance process. By integrating the STAOMDV protocol with the proposed path selection strategy, a dependable and efficient routing mechanism is established for IoT networks in edge computing scenarios. Simulation results validate that the proposed STAOMDV model achieves a balance in network energy consumption and extends the overall network lifespan.

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A secured optimised AOMDV routing protocol in MANET using lightweight continuous multimodal biometric authentication

Cited by 4 publications

References 19 publications

Optimizing dual modal biometric authentication: hybrid HPO-ANFIS and HPO-CNN framework

Optimizing dual modal biometric authentication: hybrid HPO-ANFIS and HPO-CNN framework

Biometric Authentication-Based Intrusion Detection Using Artificial Intelligence Internet of Things in Smart City

Stability-based On-demand Multi-path Distance Vector Protocol for Edge Internet of Things

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