Blockchain-Based SQKD and IDS in Edge Enabled Smart Grid Network

Alkhiari, Abdullah Musaed; Mishra, Shailendra; Alshehri, Mohammed

doi:10.32604/cmc.2022.019562

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…Actuators and sensors are two other IoT-based devices that use Camellia. In an Edge Enabled Smart Grid Network, semi-quantum key distribution (SQKD) and an intrusion detection system (IDS) use actuators and sensors to communicate [30]. During communication, SQKD distributes the encryption key generated by the Camellia algorithm [30].…”

Section: Relationship Between Iot Devices and Camellia Algorithmmentioning

confidence: 99%

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An Application of the Khumbelo Function on the Camellia Algorithm to Prevent Attacks in IoT Devices

Muthavhine,

Sumbwanyambe

2023

IEEE Access

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Camellia is an encryption algorithm implemented in many Internet of Things (IoT) devices. However, intruders attack the Camellia cipher using Substitution Box (S-Box) distinguishers. The distinguishers are tables that provide probabilities for guessing the secret keys of the algorithms. Distinguishers are used in the majority of attacks. The most wellknown distinguishers are Linear Approximation Table (LAT), Dierence-Distribution Table (DDT), and Dierential-Linear Connectivity Table (DLCT). This research focuses on preventing these attacks using a novel function called the Khumbelo function to distract the construction of S-Box distinguishers. The Khumbelo function distracted the distinguishers' constructions by reducing the probability of building the table. The Khumbelo function successfully decreased the attack probability of LAT (from 54.6875 percent to 0 percent), DDT (from 1.5625 percent to 0 percent), and DLCT (from 50.0000 percent to 0 percent). The Khumbelo function is generated using a 4-Byte output S-Box instead of the original 1-Byte output S-Box in Camellia. INDEX TERMS Camellia, Cryptographic Attacks, Internet of Things (IoT), IoT devices, K_Camellia, S-Box, The Khumbelo Function. I. INTRODUCTION T HE Internet of Things (IoT) is a modern critical system that connects devices, things, individuals, objects, digital machines, computers, and objects for the transmission of information [1]. IoT is emerging as an eective technique with devices in signicant elds to create unique networks [1] [2]. IoT customers use these networks depending on the task needed at that time, [3]. Companies use IoT devices and networks to exchange top secret and condential information, [4]. Some of this information is sent unencrypted or encrypted by algorithms that are vulnerable to attacks [4].Cryptographic algorithms are primarily used to ensure security and privacy [4]. One of the cryptographic algorithms used on IoT devices is Camellia. This study shows that dierent types of IoT devices use Camellia. Some S-Box-based cryptographic algorithms are compromised on IoT devices, including Camellia and Advanced Encryption Standard (AES). An S-Box, for example, could resist or allow attacks. Assume an S-Box with less than 32 output bits, such as in Camellia and AES, which have eight output bits [5]. In that case, intruders could easily construct dierent distinguishers to get the probability of guessing the encryption key. Camellia and AES share some similarities in that all S-Boxes in these two algorithms are built using similar transformations that take the multiplicative inverse over a nite eld, and they are all 8 x 8 in size [5]. Therefore, if an intruder could attack one S-Box, all S-Boxes would be vulnerable. According to the ndings of this study, these S-Boxes are vulnerable to various attacks. Cryptographic algorithms are unavoidable, but they must be designed in advance to withstand attacks [4].The existing studies show that intruders attack the Camellia algorithm with various attack methods. For instance:i. Camellia is vu...

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Section: Relationship Between Iot Devices and Camellia Algorithmmentioning

confidence: 99%

“…According to the preceding statements, there is a connection between IoT devices and the Camellia algorithm. The relationship is based on condentiality, security during communication, and data transmission [27]- [30].…”

Section: Relationship Between Iot Devices and Camellia Algorithmmentioning

confidence: 99%

An Application of the Khumbelo Function on the Camellia Algorithm to Prevent Attacks in IoT Devices

Muthavhine,

Sumbwanyambe

2023

IEEE Access

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“…As a result, it has received much attention. It has been successfully used in diverse fields, including resource-constrained scheduling problems 41 , solar cell models 42 , framework hyperparameter optimization 43 , optimal parameters for fuel cell MPPT 44 , and lateral vibration of ground-based systems 45 , and edge smart grids 46 . In this study, an FBI-based algorithm will be used for FS.…”

Section: Introductionmentioning

confidence: 99%

Advancing forensic-based investigation incorporating slime mould search for gene selection of high-dimensional genetic data

Qiu,

Heidari,

Chen

et al. 2024

Sci Rep

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Modern medicine has produced large genetic datasets of high dimensions through advanced gene sequencing technology, and processing these data is of great significance for clinical decision-making. Gene selection (GS) is an important data preprocessing technique that aims to select a subset of feature information to improve performance and reduce data dimensionality. This study proposes an improved wrapper GS method based on forensic-based investigation (FBI). The method introduces the search mechanism of the slime mould algorithm in the FBI to improve the original FBI; the newly proposed algorithm is named SMA_FBI; then GS is performed by converting the continuous optimizer to a binary version of the optimizer through a transfer function. In order to verify the superiority of SMA_FBI, experiments are first executed on the 30-function test set of CEC2017 and compared with 10 original algorithms and 10 state-of-the-art algorithms. The experimental results show that SMA_FBI is better than other algorithms in terms of finding the optimal solution, convergence speed, and robustness. In addition, BSMA_FBI (binary version of SMA_FBI) is compared with 8 binary algorithms on 18 high-dimensional genetic data from the UCI repository. The results indicate that BSMA_FBI is able to obtain high classification accuracy with fewer features selected in GS applications. Therefore, SMA_FBI is considered an optimization tool with great potential for dealing with global optimization problems, and its binary version, BSMA_FBI, can be used for GS tasks.

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