Service Constraint NCBQ trust orient secure transmission with IoT devices for improved data security in cloud using blockchain

Premkumar, R; Priya, Sasi

doi:10.1016/j.measen.2022.100486

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…Adequate technique for identifying cyber intrusion is introduced in [52]. An efficient service constraint network condition and behavior quality (NCBQ) scheme is proposed in [53] to include the secure transmission by utilizing different factors of network, user behavior and quality of service. A block chain-based identity authentication system (IAS) protocol is performed in [54] to ensure the authenticity and security of data transmission in CC.…”

Section: Related Workmentioning

confidence: 99%

Enhancing data security in cloud computing: a blockchain-based Feistel cipher encryption and multiclass vector side-channel attack detection approach

2024

IJATEE

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Cloud computing (CC) permits more users for storing as well as distributing their applications and data. CC continuously evolving technology that can improve agility, availability, collaboration, and scalability of data. It offers on-demand solution for different applications namely data storage, servers, databases, networking, and software. One of the most challenging issues in CC is detecting attacks. Parallel sponge-based authenticated encryptionwith sidechannel protection and adversary-invisible nonces (PSASPIN) to sponge construction was designed [1]. Moreover, leveled implementation has also utilized in implementing key generation via pseudo random function (PRF). Next, data preprocessing has utilized to minimize the code size. Finally, security proof has ensured using game theory and therefore processing shorter messages significantly. The authenticated encryption (AE) schemes such as security, performance, and efficiency are enhanced.*Author for correspondence Despite improvement found in terms of communication complexity and throughput were not focused.Side channel assault also utilizes physical data which leaks as of cryptographic tool. For avoiding from these attacks, divide-and-conquer approach is employed. Here, malicious user splits key into subkeys, endeavors for recuperating sub-keys distinctly as well as integrates them to create key. Reinforcement-strategy-based side-channel attack has designed in [2] with divide-and-conquer. Initially, the power leakage models are modified, and accuracy has improved. Next, the recovered sub-keys are integrated during the attack phase through validation therefore mitigating side-channel attacks significantly and improving the successful recovery rate. Though successful recovery rate has ensured but the detection time and accuracy have not focused. The disclosure of the cloud storage in the recent years carries enormous east to person life. Moreover, business establishments as well as organizations transmitted huge data for storage also. However, the data privacy

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Section: Related Workmentioning

confidence: 99%

Enhancing data security in cloud computing: a blockchain-based Feistel cipher encryption and multiclass vector side-channel attack detection approach

2024

IJATEE

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“…The findings of the deep blockchain framework (DBF) presented in [ 25 ], which uses Bi-LSTM to categorize attack traffic in the BoT-IoT dataset, were compared with those of our own suggested method. Figure 11 gives a comparison of the recall rate of several attack sub-categories.…”

Section: Experimental Implementationmentioning

confidence: 99%

Deep Learning-Inspired IoT-IDS Mechanism for Edge Computing Environments

Aldaej,

Ahanger,

Ullah

2023

Sensors

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The Internet of Things (IoT) technology has seen substantial research in Deep Learning (DL) techniques to detect cyberattacks. Critical Infrastructures (CIs) must be able to quickly detect cyberattacks close to edge devices in order to prevent service interruptions. DL approaches outperform shallow machine learning techniques in attack detection, giving them a viable alternative for use in intrusion detection. However, because of the massive amount of IoT data and the computational requirements for DL models, transmission overheads prevent the successful implementation of DL models closer to the devices. As they were not trained on pertinent IoT, current Intrusion Detection Systems (IDS) either use conventional techniques or are not intended for scattered edge–cloud deployment. A new edge–cloud-based IoT IDS is suggested to address these issues. It uses distributed processing to separate the dataset into subsets appropriate to different attack classes and performs attribute selection on time-series IoT data. Next, DL is used to train an attack detection Recurrent Neural Network, which consists of a Recurrent Neural Network (RNN) and Bidirectional Long Short-Term Memory (LSTM). The high-dimensional BoT-IoT dataset, which replicates massive amounts of genuine IoT attack traffic, is used to test the proposed model. Despite an 85 percent reduction in dataset size made achievable by attribute selection approaches, the attack detection capability was kept intact. The models built utilizing the smaller dataset demonstrated a higher recall rate (98.25%), F1-measure (99.12%), accuracy (99.56%), and precision (99.45%) with no loss in class discrimination performance compared to models trained on the entire attribute set. With the smaller attribute space, neither the RNN nor the Bi-LSTM models experienced underfitting or overfitting. The proposed DL-based IoT intrusion detection solution has the capability to scale efficiently in the face of large volumes of IoT data, thus making it an ideal candidate for edge–cloud deployment.

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“…CC performs data sharing that allows the user to access the data, services, links, storage, and applications through the internet (Joshi et al, 2021). The data is uploaded into the cloud by the data owner for future access and the user downloads it in the process of CC (Premkumar & Priya, 2022). However, there exist some threats, including data loss, accidental exposure, and leakage, thus resulting in security issues (Ali et al, 2023).…”

mentioning

confidence: 99%

Watermarking-Based Enhanced Data Security Among Shadow Users in Cloud Computing Using ESRCC and OMESD-LWT

TIWARI,

Jha

2024

Preprint

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For the distribution of computing services via servers, databases, data storage, and networking over the Internet, Cloud Computing (CC) is significant. Therefore, for protecting the data from security threats, data security is required for the cloud network. Yet, enhancing the data security of CC among shadow users has not been concentrated on by any work. Hence, in this article, data security is improved by preserving data from the shadow users utilizing Exponential Square Root Curve Cryptography (ESRCC) and Odd Mean Even Standard Deviation-Lifting Wavelet Transform (OMESD-LWT) techniques. Primarily, the Data Owner (DO) registers and logs into the system. Thereafter, by utilizing ESRCC, Linear Permutation-Skein Hashing Algorithm (LP-SHA), and Mid Square-based Merkle Tree (MS-MT), the data is securely shared on the InterPlanetary File System (IPFS). Afterward, the user logs in and selects the required file for retrieving it. Then, to forward requests to IPFS, the key and digital signatures are generated. Meanwhile, the DO enhances security by ciphering the MAC address utilizing Fishers Yates Transposition Cipher (FYTC) and sending it to IPFS. Then, from the IPFS, the data is decrypted utilizing ESRCC and watermarked the Media Access Control (MAC) address by employing the OMESD-LWT method. Subsequently, the user verifies the key and MAC address to download the data. The data will be accessible to the user if it is verified; otherwise, the IPFS denies the user's request. When compared with the conventional approaches, the proposed technique has enhanced performance. The proposed mechanism enhanced the Security Level (SL) to 98.92%. Also, the proposed system encrypts and decrypts the data within 1032ms and 1273ms, respectively.

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Service Constraint NCBQ trust orient secure transmission with IoT devices for improved data security in cloud using blockchain

Cited by 7 publications

References 11 publications

Enhancing data security in cloud computing: a blockchain-based Feistel cipher encryption and multiclass vector side-channel attack detection approach

Enhancing data security in cloud computing: a blockchain-based Feistel cipher encryption and multiclass vector side-channel attack detection approach

Deep Learning-Inspired IoT-IDS Mechanism for Edge Computing Environments

Watermarking-Based Enhanced Data Security Among Shadow Users in Cloud Computing Using ESRCC and OMESD-LWT

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