A Deep Learning Framework for Securing IoT Against Malwares

Mustafa; Aaras

doi:https://doi.org/10.54216/JCIM.110104

International Journal of Neutrosophic Science | 2023

Applications in KU-algebras based on BMBJ-neutrosophic Structures

We introduce BMBJ-neutrosophic sets and subalgebras as a generalisation of neutrosophic sets, and examine their application and related features to KU-algebras in this paper. We give various BMBJ-neutrosophic subalgebra characterizations, and we suggest a new BMBJ-neutrosophic subalgebra by utilizing a BMBJneutrosophic subalgebra of aKU-algebra. We look at the homomorphic inverse image of BMBJ-neutrosophic subalgebra and BMBJ-neutrosophic subalgebra translation.

groups

S. Manivasan mail -

P. Kalidass mail

link https://doi.org/10.54216/IJNS.200420

Volume & Issue

Vol. Volume 20 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2023

Neutrosophic Fuzzy Neural Network Modelling and Current-Voltage Analysis for Forecasting Post-Surgery Risks

The electrical reaction of bioactive sites in the individual’s body can be used to diagnose various disorders. Forecasts are made by examining the electric signal of the biologically active points onto patients. Measurements of the organ’s present level and variations in the passive electrical characteristics at specific bioactive sites on the body were made to evaluate the influence on the organ. The study aims to create a Neutrosophic fuzzy neural network (NFNN) approach to forecast the probability of complications following surgery. The research investigates a neural network method for predicting hazards associated with post-surgical care. Examining the current-voltage features of the biologically active spots forms the basis for the characteristics of the risk classifiers. By looking at patients who had been given a diagnosis of a disease, the training, as well as verification samples, as well as verification samples were created. Patients with type 1 had successful operations, but type 2 patients experienced a variety of post-operative problems, and type 3 patients needed extra treatment. The created classifiers show an excellent ability to foresee severe circumstances during surgical therapy. The neutrosophic fuzzy neural network model may be more sophisticated and advanced compared to conventional fuzzy neural network models. It can help differentiate the proposed model from existing models and highlight its unique features and advantages. The results show that the proposed

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Mohammad Kanan mail -

Nadir Omer mail -

Safaa S. I. Ismail mail -

Rasha M. Abd El-Aziz mail -

Ahmed I. Taloba mail

link https://doi.org/10.54216/IJNS.200421

Volume & Issue

Vol. Volume 20 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2023

Utilizing a Neutrosophic Fuzzy Logic System with ANN for Short-Term Estimation of Solar Energy

One of the primary sources of renewable energy in the coming years is thought to be solar energy. Solar energy and other renewable energy sources do, moreover, have a disadvantage in that it is hard to forecast when they will be available. The best use of solar energy is impacted by this issue, particularly when it is combined with other sources. As a result, the organization and economy of solar energy depend on accurate solar energy forecasting techniques. Predicting solar energy shortly is the study’s major goal. This paper describes the study of Neutrosophic fuzzy logic with artificial neural networks (NFL-ANN) to anticipate solar photovoltaic (PV) plant output power with the use of specified input factors known as meteorological information, such as sunshine length, humidity levels, temperature, air pressure, and others, artificial neural networks are used to forecast the outcome. NFL represents a generalised logic, which can manage stochasticity learning mistakes and unpredictability that fuzzy logic lacks. It offers the results of the calculation section. Excellent performance computer processors and NFL provide reasonable accuracy estimates of solar plant outputs as well as system reliability to consider environmental factors. The investigation was carried out with the use of MATLAB programming. With the assistance of statistical markers like mean absolute percentage error (MAPE), mean absolute error (MAE), root means square error (RMSE), and determinant coefficient, the suggested NFL-ANN approach is evaluated and compared to other approaches that are already in use. In comparison to existing techniques, the suggested NFL-ANN provides superior accuracy and lesser prediction error, according to the study’s findings. This research will be enhanced to forecast power without any loss.

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Mohammed Alqarni mail -

Ahmed H. Samak mail -

Safaa S. I. Ismail mail -

Rasha M. Abd El-Aziz mail -

Ahmed I. Taloba mail

link https://doi.org/10.54216/IJNS.200422

Volume & Issue

Vol. Volume 20 / Iss. Issue 4

Details open_in_new

American Journal of Business and Operations Research | 2023

Ranking Sustainable Technologies in Wave Energy: Multi-Criteria Decision-Making Approach under Neutrosophic Sets

While it is still in its infancy in comparison to other forms of renewable technology, there is a growing amount of interest and backing for wave energy as a potentially useful renewable resource that could replace a portion of the existing energy supply. In the context of sustainable development, the choice of technology represents a multi-criterion decision-making (MCDM) challenge that may affect the competitive advantages enjoyed by an organization or a nation. The purpose of this study is to evaluate the many wave energy technologies that are now in use as possible choices for green and sustainable technologies that may be used in the seas and oceans. However, requirements like ecological, financial, and technological factors that are based on the fundamental idea of sustainability calls for unclear or unreliable expert assessments that can be solved using single-valued neutrosophic sets (SVNSs). Because of this, the selection of sustainable wave energy technology requires the creation of a one-of-a-kind framework that can analyze both clear and ambiguous data simultaneously without sacrificing any of the information in either category. This study developed a framework that uses measurement alternatives and ranking based on compromise solution (MARCOS) within the context of SVNSs to assist decision-makers in the process of resolving real-time energy problems. An application of the process of selecting the wave energy technology is taken into consideration here as a means of illustrating how applicable the suggested framework is.

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Ahmed M. Ali mail -

Ahmed Abdelmouty mail

link https://doi.org/10.54216/AJBOR.100202

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new

American Journal of Business and Operations Research | 2023

A Multi-Criteria Decision-Making Model Based on Bipolar Neutrosophic Sets for the Selection of Battery Electric Vehicles

In the current time, global warming has compelled the automotive vehicle tech sector to undertake a paradigm shift from internal combustion engines that are fueled by fossil fuels to electrical motors that are used for traction instead. It has become an important problem to evaluate BEV options in a thorough manner from the perspective of the consumer because of the recent fast expansion that the BEV industry has seen. This evaluation may be carried out by looking at the fundamental characteristics of every BEV. In addition, effective tools for making the correct choice on the purchase of a BEV are those that use multiple criteria decision making (MCDM). The selection process of BEVs involves vague and uncertainty problem, so that, this work aims to introduce a new multi-criteria decision-making model based on the neutrosophic sets and TOPSIS method to overcome this problem.  The results concluded that the proposed model could handle unclear information and uncertainty which exist usually in the sekection process and present an effective model to rank and select best BEVs.

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Myvizhi M. mail -

Samah I. Abdel Aal mail

link https://doi.org/10.54216/AJBOR.100203

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new

American Journal of Business and Operations Research | 2021

Modified Approach for Optimization of Real-Life Transportation Problem in Neutrosophic Environment: Suggested Modifications

Singh et al. [1] presented a modified approach for solving transportation problems in neutrosophic environments. They stated that their modified approach for solving transportation problem had addressed the mathematical problems found in Thamaraiselvi and Santhi [2] approaches. But they also stopped to handle the existing problems in Thamaraiselvi and Santhi’s approach. After a deep study of Singh et al.’s method, it is observed that Singh et al. have considered several mathematical incorrect assumptions in their proposed method, and hence it is scientifically incorrect to use Singh et al.’s method and Thamaraiselvi and Santhi’s method in their present forms. This research aims to make the researchers aware of the mathematical incorrect assumptions, considered by Singh et al. in their proposed method as well as to suggest the required modifications in Singh et al.’s method and Thamaraiselvi and Santhi’s method.

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Mai Mohamed mail

link https://doi.org/10.54216/AJBOR.040204

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2023

A Neutrosophic Proposed Model for Evaluation Blockchain Technology in Secure Enterprise Distributed Applications

Applications that are enabled by blockchain technology have been infused with a decentralized system without the need for intermediate entities. Blockchain technology indicates opportunities with various technologies and applications.  Recently, a meteoric rise in the amount of interest has been indicated by academics in blockchain technology. Nevertheless, the acceptance of this blockchain technology paradigm in corporate distributed systems is not exactly promising. Executives and technocrats in a business are required to engage in multiple-criteria decision-making (MCDM) with operating uncertainty factors for the acceptance of new technologies. The proposed model aims to develop a model to identify and keep track of major elements that contribute to the sluggish pace for blockchain technology to be adopted by the general public. The study applied the Evaluation Based on the Distance from Average Solution (EDAS) approach to its interval-valued neutrosophic variant, which has the benefit of concurrently with the consideration of a decision maker's truthiness, falsity, and indeterminacy. The EDAS considers the distances of alternatives from the actual solutions considered by each criterion. In addition, the proposed model illustrated the use of neutrosophic theory with the EDAS method to rank blockchain technology in enterprise-distributed applications in uncertain conditions to aid decision-makers in optimal solutions. A numerical case study is illustrated to show the effectiveness of the proposed model in aiding decision-makers to achieve optimal solutions in uncertain conditions.   

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Nada A. Nabeeh mail -

Alshaimaa A. Tantawy mail

link https://doi.org/10.54216/JCIM.110101

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2023

Information Security Management Framework for Cloud Computing Environments

Cloud computing has become a popular paradigm for delivering computing resources and services over the internet. However, the adoption of cloud computing also brings new security challenges and risks, including data breaches, insider attacks, and unauthorized access. Therefore, it is critical to have a comprehensive information security management framework to address these challenges and ensure the security and privacy of cloud computing environments. This paper proposes a machine learning (ML) based information security management (ISM) framework for cloud computing environments that integrates best practices and standards from various domains, including cloud computing, information security, and risk management. The proposed framework includes residual recurrent network to effectively discriminate different patterns of cloud security attacks. The proposed framework emphasizes the importance of threat detection, security controls, and continuous monitoring and improvement. The framework is designed to be flexible and scalable, allowing organizations to tailor it to their specific needs and requirements.

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Manal M. Nasir mail -

Salim M. Hebrisha mail

link https://doi.org/10.54216/JCIM.110102

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2023

Managing Information Security Risks in the Age of IoT

The advent of the Internet of Things (IoT) has led to the proliferation of connected devices, creating numerous security challenges. With billions of devices generating vast amounts of data, managing information security risks in the age of IoT has become increasingly complex. Traditional security approaches are not sufficient to mitigate the risks posed by IoT devices. Machine learning (ML) provides a promising approach to enhance the security of IoT systems. This paper proposes a machine learning approach for managing information security risks in the age of IoT. The proposed approach utilizes ML algorithms to identify and mitigate security threats in IoT systems. The approach involves collecting and analyzing data from IoT devices, and applying ML algorithms to detect patterns and anomalies that may indicate security threats. The ML algorithms are trained using both supervised and unsupervised learning techniques to enable them to identify known and unknown threats. The paper describes a case study in which the proposed approach is applied to an IoT system for home security. The results demonstrate that the ML approach can effectively detect security threats in the IoT system and mitigate them in real-time.

groups

Abedallah Z. Abualkishik mail -

Rasha Almajed mail

link https://doi.org/10.54216/JCIM.110103

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2023

A Deep Learning Framework for Securing IoT Against Malwares

The proliferation of Internet of Things (IoT) devices has led to an increase in the number of malware attacks targeting these devices. Traditional security mechanisms such as firewalls and antivirus software are often inadequate in protecting IoT devices from malware attacks due to their limited resources and the heterogeneity of IoT networks. In this paper, we propose DeepSecureIoT, a deep learning-based framework for securing IoT against malware attacks. Our proposed framework uses a deep convolutional neural network (CNN) to extract features from network traffic and classify it as normal or malicious. The CNN is trained using a large dataset of network traffic to accurately identify malware attacks and reduce false positives. We evaluate the performance of DeepSecureIoT using a benchmark dataset of real-world IoT malware attacks. The results show that our proposed framework achieves an accuracy of 0.961 in detecting and classifying malware attacks, outperforming state-of-the-art intrusion detection systems. Moreover, DeepSecureIoT has low computational overhead and can be deployed on resource-constrained IoT devices.

groups

Mustafa El-Taie mail -

Aaras Y.Kraidi mail

link https://doi.org/10.54216/JCIM.110104

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new