Security analysis of hybrid one time password generation algorithm for IoT data

Shantha, R. Mary Joshitta; Mahender, K.; A, Jenifer; Prasanth, A.

doi:10.1063/5.0081958

Cited by 10 publications

(10 citation statements)

References 20 publications

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“…Blockchain technology is used by many financial services, such as digital assets, remittance, and online payment systems. Furthermore, BC has the potential to be used in other domains, including IoT, public services, and security services [8]. Once transactions are recorded on the ledger, they are immutable due to the unalterable nature of the blockchain.…”

Section: Blockchainmentioning

confidence: 99%

An Extensive Examination of the IoT and Blockchain Technologies in Relation to their Applications in the Healthcare Industry

Vaigandla,

Vanteru,

Siluveru

2024

Mesopotamian Journal of Computer Science

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Numerous domains have been transformed by the communication technologies made possible by the Internet of Things (IoT), one of which is health monitoring systems. Patterns associated with diseases and health conditions can be identified through the utilization of machine learning and cutting-edge AI techniques. Currently, scientific endeavours are concentrated on enhancing IoT-enabled applications such as medical report administration, prescription traceability, and infectious disease surveillance through the amalgamation of blockchain technology(BCT) and machine learning(ML) models. Although recent advancements have attempted to increase the adaptability of blockchain(BC) and ML for IoT applications, there are still a number of crucial considerations that must be made for improved outcomes. This report provides a comprehensive examination of emerging technologies in the healthcare sector, encompassing the IoT, and blockchain.

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Section: Blockchainmentioning

confidence: 99%

An Extensive Examination of the IoT and Blockchain Technologies in Relation to their Applications in the Healthcare Industry

Vaigandla,

Vanteru,

Siluveru

2024

Mesopotamian Journal of Computer Science

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“…Security is considered one of the major challenging tasks since IWSN is utilized in numerous real-time applications including smart industry, surveillance, building monitoring, burglar alarms, etc. [8][9][10] To safeguard the data that is transferred between sensor nodes of the network or between sensors and base stations, confidentiality is necessary for IWSN; otherwise, the communication may be eavesdropped on. Each sensor node and the sink must be able to confirm that the data received was delivered by a trustworthy sender in IWSN and wasn't a ruse by an adversary to get legitimate nodes to accept fake data.…”

Section: Introductionmentioning

confidence: 99%

“…Security is considered one of the major challenging tasks since IWSN is utilized in numerous real‐time applications including smart industry, surveillance, building monitoring, burglar alarms, etc 8–10 . To safeguard the data that is transferred between sensor nodes of the network or between sensors and base stations, confidentiality is necessary for IWSN; otherwise, the communication may be eavesdropped on.…”

Section: Introductionmentioning

confidence: 99%

An artificial intelligence approach for energy‐aware intrusion detection and secure routing in internet of things‐enabled wireless sensor networks

Aruchamy,

Gnanaselvi,

Sowndarya

et al. 2023

Concurrency and Computation

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SummaryIn today's world, the Internet of things (IoT) and wireless sensor networks (WSNs) have been integrated into various applications to support reliable communication systems. The IoT nodes collect the sensed data with the aid of WSN and transfer them to the cloud environment. Nevertheless, these nodes become vulnerable when Denial of Service attacks occurred in the IoT‐enabled WSN (IWSN). The insecure route also affects the energy efficiency of IWSN. Therefore, robust intrusion detection along with secure routing is necessitated to protect the IWSN from various security threats. In this paper, a novel Artificial Intelligence‐based Energy‐aware Intrusion Detection and Secure Routing model has been proposed to develop a secured IWSN. Initially, the proposed model executes the intrusion detection system to identify the various attacks. Afterward, a game strategy‐based decision mechanism is integrated with the proposed intrusion detection model to decide whether protection is needed or not. In the last phase, an energy‐aware ad‐hoc on‐demand distance vector algorithm has been established to offer secured routing among multiple nodes. The performance results show that the proposed model achieves superior detection accuracy of 95% and reduced energy utilization of 38% as compared with the existing intrusion detection models.

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“…In spite of significant advances in the domain of individual activity analysis, the challenge of modeling and perceptive crowd behaviors remains undeveloped. Therefore, an effective remote monitoring system is necessitated for detecting the anomaly behavior of malicious nodes in the WSN environment 14–16 …”

Section: Introductionmentioning

confidence: 99%

“…Therefore, an effective remote monitoring system is necessitated for detecting the anomaly behavior of malicious nodes in the WSN environment. [14][15][16] In this work, a new AI-based remote monitoring system has been proposed to identify the anomaly behavior of malicious nodes in the network. The proposed work eliminates unnecessary denoising preprocess and strengthens the robustness of the detection model.…”

mentioning

confidence: 99%

Artificialintelligence‐basedremote monitoring system for automated anomaly detection in wireless sensor networks

Govardhan¹,

Surendranath

Sundararajan³

2022

Concurrency and Computation

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In recent decades, wireless sensor networks (WSNs) are employed in the remote monitoring system which experiences a huge delay due to the excessive time consumption by the image processing techniques. The existing video surveillance system is unable to detect the anomaly accurately and falls short in classifying the routine activity and the anomaly activities. Therefore, an efficient anomaly detection model is necessitated for identifying the anomaly behavior of malicious nodes in the WSN environment. This research work introduces a novel artificial intelligence (AI)-based remote monitoring system for WSN. Primarily, the online video frames are retrieved from the wireless video sensors where the frames are generated by segmenting the entire stream of video as a constant-sized structural frame. The sequence/stream of video segments is fed into the proposed AI framework to detect anomaly activities. It has multiple levels of hidden layers to compare the features of the input captured image with the preloaded reference image. Furthermore, a 5-fold cross-validation approach has been inspected to validate the efficacy of the suggested AI model. The performance results evident that the proposed AI system acquires a better prediction accuracy of 98.2% and precision of 97.3% than the existing monitoring systems.

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Security analysis of hybrid one time password generation algorithm for IoT data

Cited by 10 publications

References 20 publications

An Extensive Examination of the IoT and Blockchain Technologies in Relation to their Applications in the Healthcare Industry

An Extensive Examination of the IoT and Blockchain Technologies in Relation to their Applications in the Healthcare Industry

An artificial intelligence approach for energy‐aware intrusion detection and secure routing in internet of things‐enabled wireless sensor networks

Artificialintelligence‐basedremote monitoring system for automated anomaly detection in wireless sensor networks

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