Abelian subgroups based on neutrosophic sets

Aiyared; C.; P. Maragatha; N.

doi:https://doi.org/10.54216/IJNS.240215

Fusion: Practice and Applications | 2024

Fusing Deep Learning Techniques for Intrusion Detection in Smart Grids

Smart grids, pivotal in modern energy distribution, confront a mounting cybersecurity threat landscape due to their increased connectivity. This study introduces a novel hybrid deep learning approach designed for robust intrusion detection, addressing the imperative to fortify the security of these critical infrastructures. Renamed as "Intrusion Detection for Smart Grid Using a Hybrid Deep Learning Approach," the study amalgamates Conv1D for spatial feature extraction, MaxPooling1D for dimensionality reduction, and GRU for modeling temporal dependencies. The research leverages the Edge-IIoTset Cyber Security Dataset, encompassing diverse layers of emerging technologies within smart grids and facilitating a nuanced understanding of intrusion patterns. Over 10 types of IoT devices and 14 attack categories contribute to the dataset's richness, enhancing the model's training and evaluation. The proposed hybrid model's architecture is detailed, emphasizing the synergy of convolutional and recurrent neural networks in addressing complex intrusion scenarios. This research not only contributes to the evolving field of intrusion detection in smart grids but also sets the stage for creating adaptive security systems. The convergence of a hybrid deep learning approach with a comprehensive cyber security dataset marks a significant stride towards fortifying smart grids against evolving cybersecurity threats. The proposed model achieves 98.20 percentage.

groups

Rahul R. mail -

Sindhu P. mail -

G. Naveen Sundar mail -

R. Venkatesan mail

link https://doi.org/10.54216/FPA.160105

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

Fusion of Preferences with Linguistic Weighted Power Mean Operator in Complex Decision-Making Environment

This article explores the application of the linguistic 2-tuple computational model in decision-making processes, focusing on its efficiency in managing ambiguous and imprecise linguistic information, which is vital in complex decision-making environments. The main objective is to demonstrate the use of the Weighted Power Mean (WPM) operator for hierarchical aggregation, highlighting its adaptability in reflecting the priority structures of specific problems and preserving the integrity of expert opinions. The model enhances user interaction by minimizing the need for complex numerical conversions, facilitating more intuitive decision-making. The study introduces the methodology of the linguistic 2-tuples, emphasizing their practical application in various decision-making contexts through detailed case studies. It elaborates on the hierarchical aggregation model, discussing the flexibility and potential of the WPM operator to adjust the influence of individual criteria based on their importance. The article also examines potential improvements in aggregation operators to increase their effectiveness and applicability across different scenarios. This comprehensive analysis not only underscores the capabilities of linguistic computational models in modern decision-making environments but also proposes future directions for advancing these techniques to handle increasingly complex information landscapes.

groups

Silva A. Guido Javier mail -

Juan G. Sailema Armijos mail -

Marco P. Villa Zura mail -

Maha Ibrahim mail

link https://doi.org/10.54216/FPA.160106

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024

Intuitionistic Possibility Fermatean Fuzzy Soft Sets

In this study, we introduce a new concept by making Possibility Fermatean fuzzy soft sets into a more general concept, namely Intuitionistic Possibility Fermatean fuzzy soft sets. We present examples of the application of this theory to a decision-making problem. From a theoretical point of view, we review the basic properties of this model and define the operations essential to its framework. Comprehensive definitions of complement, union, and intersection, as well as AND and OR operations are meticulously presented. As a transition from theory to practical application within this innovative context, we present an algorithm for solving decision-making problems, contributing to the practical implementation of this extended concept. This research aims to improve our understanding of the intuitionistic possibility of Fermatean fuzzy soft sets and to bridge the gap between theoretical advances and their real-world utility in decision-making problems.

groups

Shawkat Alkhazaleh mail -

Areen Al-khateeb mail -

Hamzeh Zureigat mail -

Belal Batiha mail -

Rawan Almarashdeh mail

link https://doi.org/10.54216/IJNS.240212

Volume & Issue

Vol. Volume 24 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Boosting Financial Fraud Detection Using Parameter Tuned Ensemble Machine Learning Model

Fraud detection in the financial industry is a challenging area as financial transactions gradually shift to digital platforms. More and more businesses such as the financial industry are operationalizing their services online as the usage of the internet is growing exponentially. Accordingly, financial fraud can increase in number and forms worldwide leading to remarkable financial losses that make financial fraud a main challenge. Threats such as irregular attacks and unauthorized access must be identified through a financial fraud detection system. Over the past few years, data mining and machine learning (ML) approaches have been widely used to address these issues. However, this technique has yet to be enhanced in terms of speed computation, identifying unknown attack patterns, and dealing with big data. This study presents Financial Fraud Detection using the Parameter Tuned Ensemble Machine Learning (FFD-PTEML) method. The FFD-PTEML incorporates multiple advanced components, such as z-score normalization for feature scaling and ensemble classification employing Artificial Neural Networks (ANN), Multilayer Perceptron (MLP), and Radial Basis Function (RBF) networks. The use of z-score normalization ensures uniformity in feature distribution, improving the effectiveness and interpretability of the fraud detection technique. Furthermore, the ensemble classification model combines the strength of different neural network architectures to enhance the detection performance and resilience to complicated fraud patterns. FFD-PTEML demonstrates better performance than the classical technique through extensive experimentation on real-time financial datasets, exhibiting high sensitivity and specificity in fraudulent activity detection.

groups

Reem Atassi mail -

Aziz Zikriyoev mail -

Nurbek Turayev mail -

Sagdullayeva Gulnora Botırovna mail

link https://doi.org/10.54216/JCIM.130205

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Discovering Unknown Non-Consecutive Double Byte Biases in RC4 Stream Cipher Algorithm

RC4 is one of the most widely used stream cipher algorithms. It is fast, easy and suitable for hardware and software. It is used in various applications, but it has a weakness in the distribution of generated key bytes. The first few bytes of Pseudo-Random Generation Algorithm (PRGA) key stream are biased or attached to some private key bytes and thus the analysis of key stream bytes makes it potential to attack RC4, and there is connection between the key stream bytes that make it weak and breakable by single- and double-byte biases attack. This work shows the analysis of RC4 key stream based on its non-consecutive double byte biases by using newly designed algorithm that calculates the bias in a standard time (seconds). The results are shown that the bias of RC4 keystream is proved and got the same results that were shown in the literature with less time and discover a set of new non-consecutive double byte biases in the positions (i) and (i+n). The analysis of 256 positions is required additional requirements such as supercomputer and the message passing interface environment that are not available in Iraq, therefore; the analysis is done for 32 positions.

groups

Sura Mahroos mail -

Rihab Hazim mail -

AbdulRahman Kareem Oliwe mail -

Nadia Mohammed mail -

Yaqeen Saad mail -

Ali Makki mail -

Ibrahiem El Emary mail

link https://doi.org/10.54216/JCIM.130206

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Development of an Approach for Image Forgery Detection Using Machine Learning Algorithms

Digital picture fraud detection is an increasing societal necessity due to the importance of verified images. The detection of picture copying, splicing, retouching, and re-sampling forgeries is included. In the absence of digital signatures or watermarks, passive picture authentication may serve as an alternative to active authentication. Passive techniques, every so often recognized as blind techniques, could take place without preceding knowledge of the picture or its reference. Identifying counterfeiting picture or tampering was a research field for long a period of time, triggered via the Internet, online platforms, social messaging platforms, and extensive digital image usage. The rate of failure could be a key factor for examining the alteration of picture or forgery, among other existing methods. The research applies almost six common algorithms related to machine learning in order to extract features from Lightweight, Spatial Exploitation, and Residual deep learning models on benchmark datasets MICC-F220, Columbia, and CoMoFoD. The models of incorporated deep learning could consist of AlexNet, GoogleNet, VGG16, VGG19, SqueezeNet, MobileNetV2, ShuffleNet, ResNet-18, ResNet-50, and ResNet-101 for spatial exploitation. Fine-tuning is applied to the top three deep learning models, optimizing hyperparameters centered on indicators of performance for every single benchmark dataset. Tweaked SqueezeNet, MobileNetV2, and ShuffleNet deep learning models with SGDM Optimizer and SVM classifier yielded the best results for MICC-F220 dataset. Fine-tuned VGG19, MobileNetV2, and ResNet-50 deep learning models with SGDM Optimizer and SVM v classifier yielded the best results for Columbia dataset. In CoMoFoD dataset, fine-tuned AlexNet, MobileNetV2, and ShuffleNet deep learning models with SGDM Optimizer and SVM classifier yielded the best results. The proposed approach, utilizing machine learning algorithms and deep learning features, enhanced forgery detection and reduced false positives. Results were validated on benchmark image forgery datasets and compared to current methods.

groups

Ahmed K. Jawad Alataby mail

link https://doi.org/10.54216/JISIoT.120212

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Advancing IoT Device Security in Smart Cities: Through Innovative Key Generation and Distribution With D_F, Gf, and Multi-Order Recursive Sequences

In today's mass communication landscape, security is a paramount concern, notably with the rapid expansion of the Internet of Things (IoT). Various methods aim to bolster IoT communication security, particularly by regulating access between IoT devices and networks. Encrypting data with a shared secret key is crucial, considering the limited capabilities of these devices, demanding a lightweight yet robust control mechanism. While traditional methods like Diffie-Hellman facilitated secure communication, vulnerabilities arose from modular and exponential equations. Our paper proposed a mathematical refinement of the Diffie Hellman (D_H) protocol. By leveraging GF finite fields and multi-order recursive sequences, this enhanced method aims to fortify confidentiality and complexity in exchanged keys, enabling secure data transmission while remaining efficient for resource restricted IoT devices. Validation using the Affine encryption method demonstrates considerable improvements in complexity, security, and speed. Incorporating Galois field (GF) and third-order sequencing enhances secrecy and complexity, ensuring swift computational processes.

groups

Sanaa Ahmed Kadhim mail -

Ruwaida Mohammed Yas mail -

Saad A. A. Abdual Rahman mail

link https://doi.org/10.54216/JCIM.130207

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2024

New approach towards (g1, g2, g3) neutrosophic normal interval valued set applied to sin trigonometric aggregating operator and its generalization.

We introduce the concept of sine trigonometric (g1, g2, g3) neutrosophic normal interval valued set. An identifying sine trigonometric (g1, g2, g3)neutrosophic normal interval valued set is a combination of (g1, g2, g3) neutrosophic interval valued set and neutrosophic interval valued set. We communicate the new aggregating operator such as sine trigonometric (g1, g2, g3) neutrosophic normal interval valued weighted averaging, sine trigonometric (g1, g2, g3) neutrosophic normal interval valued weighted geometric, sine trigonometric generalized (g1, g2, g3) neutrosophic normal interval valued weighted averaging and sine trigonometric generalized (g1, g2, g3) neutrosophic normal interval valued weighted geometric.

groups

V. Vijayalakshmi mail -

S. Sahaya Jude Dhas mail -

T. T. Raman mail -

Aiyared Iampan mail

link https://doi.org/10.54216/IJNS.240213

Volume & Issue

Vol. Volume 24 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2024

Foundations of neutrosophic convex structures

In this paper an idea of neutrosophic convex structures (briefly, NC-structures) is given and some of their properties are explored. Also, NC-sets, neutrosophic concave sets and neutrosophic convex hull are defined and their properties are investigated. Moreover, the notions of NC-derived operator and NC-base are studied and their relationship to NC-structures are established.

groups

Jos´e Sanabria mail -

Ennis Rosas mail -

Elvis Aponte mail

link https://doi.org/10.54216/IJNS.240214

Volume & Issue

Vol. Volume 24 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2024