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International Journal of Wireless and Ad Hoc Communication | 2024

Enhancing Wireless Ad-Hoc Network Security by Mitigating Distributed Denial-of-Service (DDoS) Attacks

The increasing threat landscape of Distributed Denial-of-Service (DDoS) attacks makes network security a major concern. These attacks are a serious challenge to the stability and integrity of digital infrastructures. This research paper is an in-depth study on how to enhance network security through the detection and mitigation of DDoS attacks. The study reviews existing literature on DDoS attack mitigation strategies, emphasizing the evolving nature of these threats and the imperative for robust defense mechanisms. The research uses statistical analysis and logistic regression to provide a detailed methodology for distinguishing DDoS attacks from normal network activities. The results show that logistic regression is an effective classification model, providing insights into improved detection measures. Finally, the study concludes by recommending a multi-faceted approach that combines theoretical insights with empirical validation, highlighting the need for stronger network security measures against DDoS attacks and enhancing digital resilience.

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Mahmoud M. Ismail mail -
Ahmed A. Metwaly mail
link https://doi.org/10.54216/IJWAC.080205

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

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Journal of Cybersecurity and Information Management | 2024

Insider Threat Detection: Exploring User Event Behavior Analytics and Machine Learning in Security Reviews

With the exponential increase in technology use, insider threats are also growing in scale and importance, becoming one of the biggest challenges for government and corporate information security. Recent research shows that insider threats are more costly than external threats, making it critical for organizations to protect their information security. Effective insider threat detection requires the use of the latest models and technologies. Although a large number of insider threats have been discovered, the field is still limited by many issues, such as data imbalance, false positives, and a lack of accurate data, which require further research. This survey investigates the existing approaches and technologies for insider threat detection. It finds and summarizes relevant studies from different databases, followed by a detailed comparison. It also examines the types of data used and the machine learning models employed to detect these threats. It discusses the challenges researchers face in detecting insider threats and future trends in the field.

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Ruba Altuwaijiri mail -
Hanan AlShaher mail
link https://doi.org/10.54216/JCIM.130213

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2024

Multiple Attribute Group Decision-Making with Neutrosophic Environment for Carbon Emission Prediction on Sustainable Urban Management

Sufficient CO2 is indispensable for vegetation, but space and oceanic vehicles, industrial chimneys and land use tons of extreme CO2 and are typically accountable for global warming, climate variations and greenhouse effect. Owing to COVID19, CO2 discharge was in 2020 at its lower level than first ten years. However, the time taken is not known to decrease, increase or change carbon emissions to an endurable point. Precise predicting of carbon production has real consequences for selecting the optimal ways of decreasing carbon emissions. A pressing necessity to control these carbon emissions is needed. The preliminary step is to precisely recognize the milestones and threat levels. Specific thresholds should be mapped that formulate the maximum levels of CO2 namely – the point of no return, risk point, and so on. This article focuses on the development of Multiple Attribute Group Decision-Making with Neutrosophic Environment for Carbon Emission Prediction (MAGDM-NECEP) method on Sustainable Urban Management. The MAGDM-NECEP architecture proficiently manages the multi-criteria nature of emission calculation, while neutrosophic logic accommodates ambiguity and uncertainty in input dataset. Furthermore, GSO enhances model parameters, improving prediction performance. The MAGDM synergy and neutrosophic logic offer strong decision-making abilities, whereas GSO fine-tuned the model parameter for superior outcomes. Empirical analysis establishes the efficiency of the presented technique in precisely predicting carbon emission, providing valuable insight for the environmentalist and policymaker in developing efficient mitigation strategy

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Mesfer Al Duhayyim mail
link https://doi.org/10.54216/IJNS.240127

Volume & Issue

Vol. Volume 24 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2024

Two-Person Intuitionistic Neutrosophic Soft Games with Harris Hawks Optimizer based Tweets Classification on NLP Applications

With the widespread usage of social media in our day-to-day lives, it becomes a platform for persons to express and share their feelings, views, thoughts, and opinions. Recognizing emotions has numerous applications extending from dynamic advertisement to behavior analyses. People express their emotional state in a language that is often complemented by figures of speech and ambiguity, making it problematic even for human beings to understand. Categorizing tweets is a dynamic application of NLP, allowing the scrutiny of topical discussions, user opinions, and trends in real-time. Leveraging techniques such as word embeddings, machine learning, and text preprocessing approaches, tweet classification enables tasks like spam detection, sentiment analysis, and topic modeling. This ability assists companies in understanding client feedback and allows policymakers and researchers to track emerging issues and gauge public opinion on social networking media. This study presents a Two-Person Intuitionistic Neutrosophic Soft Games with Harris Hawks Optimizer (TINSG-HHO) based Tweets Classification on NLP Applications. The purpose of the TINSG-HHO technique is to detect the existence of different kinds of emotions or sentiments in the tweets. The TINSG-HHO technique begins with preprocessing of tweets to convert them into useful format.  Then, FastText embedding represents words as contextual similarities, dense vectors, and capturing semantic nuances. Leveraging the embedding, the Neutrosophic classification model proficiently handles vagueness and uncertainty intrinsic in deceptive content detection tasks. Moreover, the HHO technique enhances the parameter of the Neutrosophic classifier, improving its generalization capabilities and performance. Based on the hunting strategy of Harris's hawks, HHO discovers the parameter range to search for optimum configurations for the classifier. Experimental evaluations carried out on different datasets illustrate the effectiveness of the DCRM in precisely detecting the deceptive content

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Mohammad Mahzari mail -
Aisha H. Abdalla Hashim mail -
Khalid M. Osman Saeed mail -
Mohammed M. Osman Mokhtar mail
link https://doi.org/10.54216/IJNS.240128

Volume & Issue

Vol. Volume 24 / Iss. Issue 1

Details open_in_new
International Journal of Neutrosophic Science | 2024

Optimal Single-Valued Neutrosophic Sine Trigonometric Aggregation Operators for Accurate Financial Fraud Detection Model

Financial fraud may be regarded as any fraud targeting financial organisations including crypto exchanges, banks, fintech, and lending organizations, or any criminal activity associated with the payment process. Financial fraud detection cites protocol set prepared to circumvent the destruction produced by fraudulent activities happening in financial service suppliers. Ecological financial fraud detection (FD) includes the usage of ethical and sustainable performs within fraud actions recognition from the financial area. In recent times, DL and ML techniques have been used in CCF recognition owing to their ability to construct a robust mechanism to discover fraud businesses. Therefore, this study develops an Optimal Single Valued Neutrosophic Sine Trigonometric Aggregation Operator (O-SVNSTAO) for Accurate Financial Fraud Detection Model. The genetic-inspired particle swarm optimization (GIPSO) feature selection model efficiently discerns the relevant attribute from sophisticated financial databases, improving the model's discriminative power while alleviating dimensionality problems. Consequently, the SVNSTAO classifier leverages the features selected to discern complicated features inherent in fraudulent actions, which facilitates accurate diagnosis. Moreover, the COA parameter tuning mechanism enhances the SVNSTAO model's parameter, which ensures adaptability and optimum performance to varied fraud settings. Empirical analysis of real-time financial datasets demonstrates the superiority of O-SVNSTAO technique over classical methods, underlining its effectiveness in discovering financial fraud with exceptional efficiency and reliability

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Fadoua Kouki mail
link https://doi.org/10.54216/IJNS.230434

Volume & Issue

Vol. Volume 23 / Iss. Issue 4

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International Journal of Neutrosophic Science | 2024

Arithmetic Optimization Algorithm with Adaptive Neuro-Fuzzy Interference System for Predicting Financial Crisis

Financial technology (Fintech) is paramount in driving advanced technologies, economies, society, and several other sectors. Smart Fintech is the new-generation Fintech, primarily stimulated and endowed by compuational technology. Smart Fintech syndicates DSAI and renovates economies and finance for dynamic, smart, customized, automated services and systems, economies and financial companies, and the industry. The strength and development of the country’s economies are assessed by the correct forecasting. Financial crisis prediction (FCP) has the substantial consequence on the economies. Previous studies mainly emphasise statistical, DL, and ML methodologies for predicting the financial well-being of the business. Therefore, this article develops a new Arithmetic Optimization Algorithm with Adaptive Neuro-Fuzzy Interference System (AOA-ANFIS) technique for Predicting Financial Crisis. The presented AOA-ANFIS technique aims to predict the presence of financial crises or not. The model incorporates three major elements: Arithmetic Optimization Algorithm (AOA) for feature selection, Adaptive Neuro-Fuzzy Inference System (ANFIS) as the classification algorithm, and Bat Optimization Algorithm (BOA) for parameter tuning. The AOA feature selection model effectively detects the important attributes from a large proportion of financial indicators, augmenting the model's prediction capability while decreasing computational difficulty. Subsequently, the ANFIS classifier exploits the features selected for capturing the intricate non-linear relations intrinsic in financial data, permitting accurate crisis calculation. Additionally, the BOA parameter tuning model augments the ANFIS model's parameters, ensuring robustness and optimum performance. Experimental outcomes on varied financial databases validate the higher efficiency of the AOA-ANFIS technique over underlying processes, demonstrating its effectiveness in forecasting financial crises with great reliability and precision.

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Fadoua Kouki mail
link https://doi.org/10.54216/IJNS.230435

Volume & Issue

Vol. Volume 23 / Iss. Issue 4

Details open_in_new
International Journal of Neutrosophic Science | 2024

Neutrosophic Fuzzy Interval Sets and its Extension through MCDM and Applications in E-Management

we are introducing the model-type operators over Interval-Valued Fuzzy Neutrosophic Sets with time moments [IVFNS] and learn a few of their properties with numerical examples to demonstrate the defined operations and operators. Also introduce various distance measures over the extension of interval neutrosophic sets as well as apply the introduced measures in ecological management in this direct to decide the type of corrosion disturbing some towns for valuable management to be taken, using this normalized distance measures. The extensions of neutrosophic connection values and non-connection values be not used for all time probable positive to our fulfillment, but this concept IVTNFS part has more significant responsibility at this point since the time progress with IVN-fuzzy sets provide the accurate solution in factual situations similarly, conclusion making, career deciding and so on. This is the main reason for taking in the extensions of neutrosophic sets.

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A. Manshath mail -
K. Rajesh mail -
M. Logeshwari mail -
R. Saranya mail
link https://doi.org/10.54216/IJNS.240225

Volume & Issue

Vol. Volume 24 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2024

ODESMAN: Optimizing Decision-Making in Complex Environments: Integrating Neutrosophic and Fuzzy Logic for Advanced System Modeling

Within the domain of complex systems, inherent uncertainties, and ambiguities that traditional models frequently find difficult to handle pose a constant challenge to decision-making. To dramatically improve decision-making frameworks, this study presents a novel methodology called "ODESMAN," which synergistically integrates fuzzy logic with neutrosophic sets. Neutrosophic sets, on the other hand, allow one to express the degrees of truth, untruth, and indeterminacy as shifts rather than fixed points. Therefore, their use is more elegant than the existing methods offered. The implementation of fuzzy logic into such sets may provide a high level of effectiveness in managing uncertainty, which can be predicted and quantified. For example, the model allows accounting for uncertainty in the system inputs and processes up to 20%, the variability of truth values 10-50%, and the overall uncertainty 15-30%. The application of the model in practice, specifically in the emergency response, and the supply chain system permitted achieving a 40% increase in flexibility capacity and a 25% improvement in decision-making approaches compared to the traditional frameworks. Therefore, the practical strength and broad utility of the model can be proved, which validates its efficiency and allows broad implementation of this complex theoretical framework into the existing systems.

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Shaik Khaja Mohiddin mail -
Abdul Ahad mail -
N. Murugavalli mail -
V. Kavitha mail -
S. Venkata Suryanarayana mail -
M. Sundar Raj mail
link https://doi.org/10.54216/IJNS.240226

Volume & Issue

Vol. Volume 24 / Iss. Issue 2

Details open_in_new
Fusion: Practice and Applications | 2024

Personal Data Protection Model in IOMT-Blockchain on Secured Bit-Count Transmutation Data Encryption Approach

The Internet of Medical Things (IoMT) has paved the way for innovative approaches to collecting and managing medical data. With the large and sensitive medical data being processed hence, the need for a strong identity and privacy become necessary. The present paper suggests a comprehensive method of PriMedGuard which aims at protection of the personal medical information. The first stage will be data collection from devices and sensors, then data cleaning to transform the data into the required format. There is also a safety system in the system that registers and authenticates authorized entities as well as ETDO (Enhanced Tasmanian Devil Optimization algorithm) is used for generating asymmetric cryptographic keys. The data is encrypted using the Secure Bit-Count Transmutation (SBCT) Data Encryption Algorithm and then put in the locations provided by the InterPlanetary File System (IPFS), a decentralized and distributed storage system. A safe smart contract on the blockchain is created so that the data retrieval is secure and MedSecEnsemble Detection is proposed as an intrusion detection technique in the IoMT network. By using this method, data will stay available while at the same time integrity, confidentiality and protection against vulnerabilities are ensured. Hence, the Internet of Medical Things ecosystem will be secured from unauthorized access and possible security threats…

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Sultan Almotairi mail -
Santosh Reddy Addula mail -
Olayan Alharbi mail -
Zaid Alzaid mail -
Yasser M. Hausawi mail -
Jaber Almutairi mail
link https://doi.org/10.54216/FPA.160111

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new
Fusion: Practice and Applications | 2024

Efficient Intrusion Detection using OptCNN-LSTM Model based on hybrid Correlation-based Feature Selection in IoMT

Intrusion detection in the IoMT (Internet of Medical Things) represents the process of keeping track of and discovering unauthorized or malicious actions in medical devices and networks. Some of its benefits include early detection of potential threats, prevention of data breaches, and protection of patient privacy. Aside from these benefits, some difficulties are evident, like alarm fatigue due to false positives, the complexity in the standardizing detection across different devices, and resource limits that hinder qualitative implementations, thus leaving some vulnerabilities in the healthcare infrastructure. This paper proposes a new Efficient Intrusion Detection model based on the Correlation-Based Feature Selection and the OptCNN-LSTM model to address these problems. The proposed methodology comprises five key phases: (i) Data Acquisition (ii) Pre-processing (iii) Feature Extraction (iv) Feature Selection (v) OptCNN-LSTM Model-based intrusion detection. The raw data is first gathered and then preprocessed using z-score normalization and data cleaning. Then, the best features are extracted using central tendency, the degree of dispersion, and correlation. A mixed IHHO-PSO feature with the Correlation-based Feature Selection (CFS) framework is employed to choose the best features amongst the collected features. At last, the OptCNN-LSTM model is performed to detect the intrusion in the IoMT based on features-selected data. The CNN is tuned using the Levy Flight Optimization (LF) which can be further combined with the LSTM to get the expected results. The code is written in Python and the model is then run to determine its performance which is measured in terms of accuracy, precision, f-measure, and a Receiver Operating Characteristic Curve (ROC). Compared to the current models, the proposed model has the highest accuracies 97.6% and 96.5% for learning rates 70 and 80, respectively…

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Sultan Almotairi mail -
Deepak Dasaratha Rao mail -
Olayan Alharbi mail -
Zaid Alzaid mail -
Yasser M. Hausawi mail -
Jaber Almutairi mail
link https://doi.org/10.54216/FPA.160112

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new