Algorithmic method of security of the Internet of Things based on steganographic coding

Kabulov, Anvar; Saymanov, Islambek; Yarashov, Inomjon; Muxammadiev, Firdavs

doi:10.1109/iemtronics52119.2021.9422588

Cited by 33 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [15], the use of Internet of Things technologies in ecology (using the example of the Aral Sea region) as a system is proposed (Figure 1). The system was developed as a website.…”

Section: Resultsmentioning

confidence: 99%

“…The purpose of the research in this article is to develop effective object recognition methods to solve the identification problem in the IoT ecosystem of the Aral region. The objective of the research is to develop correct recognition algorithms based on an algebraic approach in solving the problem of identifying [11][12][13][14][15] the flow of information in the IoT ecosystem of the Aral region, which is described in detail in work [15] of the authors of this paper.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Algebraic Recognition Approach in IoT Ecosystem

Kabulov,

Saymanov,

Babadjanov

et al. 2024

Mathematics

Self Cite

View full text Add to dashboard Cite

The solution to the problem of identifying objects in the IoT ecosystem of the Aral region is analyzed. The problem of constructing a correct algorithm with linear closure operators of a model for calculating estimates for identifying objects in the IoT ecosystem of the Aral region is considered. An algorithm operator is developed, which is considered correct for the problem Z, is the sum of q operators of the assessment calculation model, and is described by a set of numerical parameters 3·n·m·q, where n is the number of specified features, m is the number of reference objects, and q is the set of recognized objects. Within the framework of the algebraic approach, several variants of linear combinations of recognition operators are constructed, the use of which gives the correct answer on the control material, and this is proven in the form of theorems. The constructed correct recognition algorithms, which are the easiest to use, where there is no optimization procedure, make it possible to quickly solve the issue of identifying incoming information flows in the IoT ecosystem of the Aral region.

show abstract

“…In [15], the use of Internet of Things technologies in ecology (using the example of the Aral Sea region) as a system is proposed (Figure 1). The system was developed as a website.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Algebraic Recognition Approach in IoT Ecosystem

Kabulov,

Saymanov,

Babadjanov

et al. 2024

Mathematics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Two strategies have been devised to quantitatively model humpback whale bubble-net behavior: 1) Shrinking Encircling Mechanism: To emulate the bubble-net behavior, the parameter a Tis systematically decreased from 2 to 0 over the course of iterations, mimicking the characteristic behavior. The search agent's position is updated within a range defined by the agent's starting position and the position of the current best agent [35]. This update is accomplished by incorporating random values within the range of [-1, 1] for R effectively determining the new position of the search agent, a critical aspect in capturing the bubblenet behavior.…”

Section: ) Surrounding Preymentioning

confidence: 99%

A Hybrid Framework Leveraging Whale Optimization and Deep Learning With Trust-Index for Attack Identification in IoT Networks

Gotarane,

Abimannan,

Hussain

et al. 2024

IEEE Access

View full text Add to dashboard Cite

The rise of smart cities, smart homes, and smart health powered by the Internet of Things (IoT) presents significant challenges in design, deployment, and security. The seamless data processing across a complex network of interconnected devices in unprotected conditions makes it vulnerable to potential breaches, underscoring the need for robust security at various levels of the network. Traditional security methods based on statistics often struggle to comprehend data patterns and provide the desired level of security. This work proposes a novel hybrid framework that combines Whale Optimization and Deep Learning with a trust-index to identify malicious nodes engaging in various attacks such as DoS, DDoS, Drop attack, and Tamper Attacks, thus enhancing IoT node security. The developed framework first calculates a trust-index score for IoT nodes based on drop attack, tamper attack, replay attack, and multiple-max attack. Subsequently, it utilizes the trust index score in the Optimized Neural Network (ONN) model to effectively identify the malicious IoT node. The neural network optimization is achieved through a fitness function that determines optimal weights using the Whale Optimization Algorithm. The proposed framework has been tested across varying network sizes, comprising 100, 500, and 1000 nodes. The resulting outcomes were evaluated against benchmark security methods such as Logical regression (LR),Random Forest (RF), Support Vector Machine (SVM), Bayesian models, ANN, Elephant herding optimization (EHO),and Lion algorithm (LA) using metrics like specificity, sensitivity, accuracy, precision, False Positive Rate (FPR), False Negative Rate (FNR), False Discovery Rate (FDR), Error, F1 score, Matthews Correlation Coefficient (MCC), and Negative Predictive Value (NPV). The results reveal a notable enhancement in accuracy (26.63%, 13.04%, 17.78%, 30.52%, 22.45%, 4.26%, and 2.24%) for a 100node network when compared to the benchmark security methods. Furthermore, the proposed framework consistently demonstrates strong performance even when applied to larger IoT networks with a higher node count.

show abstract

“…include smartphones, coffee makers, washing machines, headphones, light bulbs, and more. According to the International Data Corporation (IDC), it is projected that by 2025, there will be approximately 41.6 billion IoT devices, potentially generating an enormous amount of data, estimated at 79.4 zettabytes (ZB) [61]. IoT is envisioned as a vast network that interconnects everything, including people, fostering relationships between people and devices.…”

Section: Plos Onementioning

confidence: 99%

Smart greenhouse construction and irrigation control system for optimal Brassica Juncea development

Huynh,

Tran,

Duong-Trung

2023

PLoS ONE

View full text Add to dashboard Cite

This paper contributes to smart greenhouses and IoT (Internet of Things) research. Our pioneering achievement centers on successfully designing, constructing, and testing a 30m2 smart greenhouse, explicitly focusing on the cultivation and development of Brassica Juncea, a mustard variety commonly grown in Vietnam. The construction phase entailed the meticulous integration of diverse IoT technologies and systems, culminating in the creation of a finely tuned environment to meet the unique needs of Brassica Juncea cultivation. Notably, our research team has realized the physical infrastructure and developed and implemented a robust web interface. This interface empowers users to monitor and remotely control the smart greenhouse conveniently. It provides real-time visualization of critical parameters, including temperature, humidity, soil moisture, and light intensity, enabling precise monitoring and supporting informed decision-making in crop management. In addition to the web interface, we have meticulously designed and completed an Android mobile application, further enhancing accessibility and convenience. This mobile app allows users to monitor and control the smart greenhouse while on the move. It is imperative to underscore that this work marks a significant milestone as the first complete smart greenhouse IoT solution dedicated to developing Brassica Juncea. Our pioneering accomplishments not only advance the frontiers of innovative greenhouse and IoT research but also contribute substantially to the progress of intelligent agriculture.

show abstract

Algorithmic method of security of the Internet of Things based on steganographic coding

Cited by 33 publications

References 5 publications

Algebraic Recognition Approach in IoT Ecosystem

Algebraic Recognition Approach in IoT Ecosystem

A Hybrid Framework Leveraging Whale Optimization and Deep Learning With Trust-Index for Attack Identification in IoT Networks

Smart greenhouse construction and irrigation control system for optimal Brassica Juncea development

Contact Info

Product

Resources

About