A review on big data based on deep neural network approaches

Rithani, M.; Kumar, R. Prasanna; Doss, Srinath

doi:10.1007/s10462-023-10512-5

Cited by 43 publications

(4 citation statements)

References 88 publications

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“…However, there are also potential challenges and limitations to the use of Big Data in Network Security and intelligence. One challenge is the need for sophisticated data processing and analysis tools and infrastructure, which may be expensive and require specialized training to use effectively [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Big Data based approach to Network Security and intelligence

Alquaifil,

Mishra,

AlShehri

2024

IJCDS

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Big data analysis technologies and machine learning techniques are essential for examining and forecasting the state of network security as global concerns about cyber security grow. Models for monitoring network security have a number of challenges, including resource consumption, inaccuracies, low processing efficiency, and incompatibility with real-time and large-scale scenarios. This paper proposes a novel approach to Network Security Situation Awareness (NS-SA) using Big Data (BD) analytics and machine learning. The proposed approach addresses the limitations of existing NS-SA models by leveraging data purification and simplification techniques, and by employing an updated back propagation (BP) neural network to construct an NS BD analysis model. The paper provides a comprehensive explanation of the model's structure and outlines the relevant model techniques. Extensive testing has been conducted to ensure the model's accuracy and applicability in understanding NS scenarios. This study focuses on MATLAB and Python-based implementation of a neural network for network security using a big data approach. The results demonstrate the potential and value of the proposed model in accurately assessing and forecasting NS conditions. The proposed approach has several advantages over existing NS-SA models. It is more efficient in terms of resource usage, it is more accurate in its analysis of network data, It is more applicable in real-time and large-scale scenarios and it is more robust to noise and heterogeneity in network data.

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Section: Literature Reviewmentioning

confidence: 99%

Big Data based approach to Network Security and intelligence

Alquaifil,

Mishra,

AlShehri

2024

IJCDS

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“…Based on the literature, support vector machines and neural networks are the most commonly used models. In this research work, neural networks are chosen due to their high customizability (in the form of a large number of hyperparameters) and suitability for big data [24]. Multilayer perceptrons (MLPs) were chosen for this research work since the nature of the data did not necessitate the usage of RNNs or CNNs.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Anomaly Detection in WAAM Deposition of Nickel Alloys—Single-Material and Cross-Material Analysis

Rajesh,

Ya,

Hermans

2023

Metals

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The current research work investigates the possibility of using machine learning models to deduce the relationship between WAAM (wire arc additive manufacturing) sensor responses and defect presence in the printed part. The work specifically focuses on three materials from the nickel alloy family (Inconel 718, Invar 36 and Inconel 625) and uses three sensor responses for data analysis, which are welding voltage, welding current and welding audio. Two different machine learning models are used—artificial neural networks (ANNs) and random forests (RF). The results for each of the materials, separately, indicate that the accuracies range from 60% to 90% and the correlation coefficient is less than 0.5 (indicating weak positive correlation), depending on the model and material. In addition to separate material analysis, a cross-material data analysis was formed to test the models’ general prediction capabilities. This led to predictions that are significantly worse, with accuracies ranging from 20% to 27% and very weak correlation coefficients (less than 0.1), indicating that the choice of material is still important as a boundary condition. Analysis of the results indicates that the relative importance of audio sensor response depends on the nature of defect formation. Random forests are found to perform the best for single material analysis, with the comparatively inferior performance of ANNs possibly being due to lack of sufficient datapoints.

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“…Therefore, researchers often examine the cyber security measures that banks use to protect against cyber fraud (Kuzior et al, 2022b). For example, some scholars analyze the effectiveness of encryption and multifactor authentication (Almaiah et al, 2023), artificial intelligence (Rithani et al, 2023), biometrics (Malik et al, 2023), and other technological solutions.…”

Section: Literature Reviewmentioning

confidence: 99%

The Modeling of the Probable Behaviour of Insider Cyber Fraudsters in Banks

Yarovenko,

Kuzior,

Raputa

2023

FMIR

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Insider cyber fraud in the banking sector is a serious and complex issue for financial institutions. This form of cyber fraud is particularly insidious due to insiders’ inherent access and knowledge, necessitating banks to implement comprehensive strategies for detecting, preventing, and responding to these internal threats. The aim of this study is to develop a scientific and methodological approach to model the probable behaviour of insider cyber fraudsters in banks based on a complex combination of principal component analysis, k-means clustering, and associative analysis. During the analysis of current challenges in the financial sector regarding the evolution of cyber fraud and its implications, the systematization of existing theoretical approaches concerning the examination of cyber fraud in banks was performed. Its result revealed a positive trend in the dynamics of the number of published materials in conferences and articles using keywords “cyber” and “frauds” in the Scopus database from 2000 to 2023. Additionally, utilizing the VOSviewer software facilitated the systematization of keyword combinations used in scholarly publications on the chosen topic, forming clusters to visualize and organize vectors of scientific research. Analytical data from Google Trends on critical issues related to cyber fraud were chosen as input data. Twenty variables were formed, which are the results of search queries, characterizing cyberattacks and decreased trust in financial institutions. The principal components method was used to reduce the dimensionality of the input data array, making it possible to select the nine most significant for the study. Conducting a cluster analysis using the k-means method made it possible to form 3 main groups of search queries, which included 12 of the selected variables. The results of the performed procedures contributed to the implementation of associative analysis for three sets of variables. It has been found that what intrigues potential insider cybercriminals in banks the most is the personal financial information of the client, access to the client’s profile in online banking and gaining access to his phone data. The obtained results can be utilized by commercial banks for identifying potential insider cyber fraudsters and ensuring a higher level of client protection against the actions of insider cyber fraudsters, by bank clients for analysing and mitigating potential threats from insider cyber fraudsters, and by law enforcement agencies for prompt responses to potential threats posed by insider cyber fraudsters in banks.

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A review on big data based on deep neural network approaches

Cited by 43 publications

References 88 publications

Big Data based approach to Network Security and intelligence

Big Data based approach to Network Security and intelligence

Anomaly Detection in WAAM Deposition of Nickel Alloys—Single-Material and Cross-Material Analysis

The Modeling of the Probable Behaviour of Insider Cyber Fraudsters in Banks

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