Hybrid GSW and DM based fully homomorphic encryption scheme for handling false data injection attacks under privacy preserving data aggregation in fog computing

Amuthan, A.; Sendhil, R

doi:10.1007/s12652-020-01849-8

Cited by 20 publications

(6 citation statements)

References 21 publications

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“…In literature [13], a Hybrid GSW (Gentry, Sahai, and Waters) and DM (Ducas and Micciancio)related Fully Homomorphic Encryption (HGSW-DM-FHE) approach was presented to control the FDIA in privacy-preserving data aggregation in the fog computing environment. The presented HGSW-DM-FHE method was found to be extremely fault-tolerant, and the data aggregation procedure in another device did not impact even in the case of the failure of fog devices.…”

Section: Existing Fdi Detection Approachesmentioning

confidence: 99%

Optimal Hybrid Deep Learning Enabled Attack Detection and Classification in IoT Environment

Alruwaili¹

2023

Computers, Materials &Amp; Continua

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The Internet of Things (IoT) paradigm enables end users to access networking services amongst diverse kinds of electronic devices. IoT security mechanism is a technology that concentrates on safeguarding the devices and networks connected in the IoT environment. In recent years, False Data Injection Attacks (FDIAs) have gained considerable interest in the IoT environment. Cybercriminals compromise the devices connected to the network and inject the data. Such attacks on the IoT environment can result in a considerable loss and interrupt normal activities among the IoT network devices. The FDI attacks have been effectively overcome so far by conventional threat detection techniques. The current research article develops a Hybrid Deep Learning to Combat Sophisticated False Data Injection Attacks detection (HDL-FDIAD) for the IoT environment. The presented HDL-FDIAD model majorly recognizes the presence of FDI attacks in the IoT environment. The HDL-FDIAD model exploits the Equilibrium Optimizer-based Feature Selection (EO-FS) technique to select the optimal subset of the features. Moreover, the Long Short Term Memory with Recurrent Neural Network (LSTM-RNN) model is also utilized for the purpose of classification. At last, the Bayesian Optimization (BO) algorithm is employed as a hyperparameter optimizer in this study. To validate the enhanced performance of the HDL-FDIAD model, a wide range of simulations was conducted, and the results were investigated in detail. A comparative study was conducted between the proposed model and the existing models. The outcomes revealed that the proposed HDL-FDIAD model is superior to other models.

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Section: Existing Fdi Detection Approachesmentioning

confidence: 99%

Optimal Hybrid Deep Learning Enabled Attack Detection and Classification in IoT Environment

Alruwaili¹

2023

Computers, Materials &Amp; Continua

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“…In this part, the performance of our scheme is compared with other three similar and popular data aggregation schemes 45,50,51 . First, the features of these schemes are compared in Table 2.…”

Section: Performance Evaluationmentioning

confidence: 99%

NLSP: A novel lattice‐based secure primitive for privacy‐preserving smart grid communications

Bao

Hong

et al. 2022

Concurrency and Computation

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Summary As the new generation of power scheme, smart grid is proposed to overcome the shortcomings of traditional systems, such as low efficiency and reliability. In this article, a novel lattice‐based secure primitive for privacy‐preserving smart grid communications is proposed, which has the remarkable characteristics, such as scalable multi‐dimensional fine‐grained power data structure and differential privacy security. First, combining with the lattice‐based data encryption technology, while effectively resisting quantum attacks, the method of simultaneous processing of multi‐dimensional data is innovated. Second, through combining the additive homomorphism of the lattice‐based cryptosystem and the Chinese remainder theorem, the data aggregation mechanism that can directly perform homomorphic operations on compressed ciphertext is constructed. Thanks to the above innovative design ideas, the proposed scheme not only significantly improves the efficiency of data communication and processing, greatly reduces the computational cost of the intermediate entity, but also realizes the data confidentiality and information privacy. Finally, observing the decentralized topology of communication nodes in the typical cyber‐physical system of smart grid, the localized differential privacy technology is leveraged to optimize and balance the utility, security, and efficiency of differential privacy. Extensive performance evaluations are conducted to illustrate that the proposed scheme outperforms the state‐of‐the‐art similar schemes in terms of computation complexity and communication cost.

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“…Sleeping mechanisms, Energy-aware routing, data aggregation, and congestion-aware routing have been presented approaches in IoT. In energy-aware routing, the present protocol contains node reliance techniques, energy-aware solar routing, data-driven approaches, multihop hierarchical clustering, duty cycling for extending lifetime of the network [7]. It can be fundamental process for saving energy and eliminating redundant data [8].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Data Aggregation with Data Offloading Scheme for Fog Enabled IoT Environment

Nalayini¹,

Prakash²

2023

Computer Systems Science and Engineering

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Fog computing is a promising technology that has been emerged to handle the growth of smart devices as well as the popularity of latency-sensitive and location-awareness Internet of Things (IoT) services. After the emergence of IoT-based services, the industry of internet-based devices has grown. The number of these devices has raised from millions to billions, and it is expected to increase further in the near future. Thus, additional challenges will be added to the traditional centralized cloud-based architecture as it will not be able to handle that growth and to support all connected devices in real-time without affecting the user experience. Conventional data aggregation models for Fog enabled IoT environments possess high computational complexity and communication cost. Therefore, in order to resolve the issues and improve the lifetime of the network, this study develops an effective hierarchical data aggregation with chaotic barnacles mating optimizer (HDAG-CBMO) technique. The HDAG-CBMO technique derives a fitness function from many relational matrices, like residual energy, average distance to neighbors, and centroid degree of target area. Besides, a chaotic theory based population initialization technique is derived for the optimal initial position of barnacles. Moreover, a learning based data offloading method has been developed for reducing the response time to IoT user requests. A wide range of simulation analyses demonstrated that the HDAG-CBMO technique has resulted in balanced energy utilization and prolonged lifetime of the Fog assisted IoT networks.

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Hybrid GSW and DM based fully homomorphic encryption scheme for handling false data injection attacks under privacy preserving data aggregation in fog computing

Cited by 20 publications

References 21 publications

Optimal Hybrid Deep Learning Enabled Attack Detection and Classification in IoT Environment

Optimal Hybrid Deep Learning Enabled Attack Detection and Classification in IoT Environment

NLSP: A novel lattice‐based secure primitive for privacy‐preserving smart grid communications

Hierarchical Data Aggregation with Data Offloading Scheme for Fog Enabled IoT Environment

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