Survey on Visualization and Visual Analytics pipeline-based models: Conceptual aspects, comparative studies and challenges

Ltifi, Hela; Kolski, Christophe; Ayed, Mounir Ben

doi:10.1016/j.cosrev.2020.100245

Cited by 6 publications

(18 citation statements)

References 79 publications

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“…Information visualization [2] commonly involves visualization of tabular multi-variable business (or non-scientific) data such as the one generated from within financial institutions or computer networks. It is the study of transformation of non-scientific data into corresponding visual representations and the layout of visual data abstractions on views.…”

Section: Visualization and Visual Analysis Pipelinesmentioning

confidence: 99%

“…Numerous visualization pipelines and/or frameworks have been proposed in literature for guided data analysis, visualization, and knowledge extraction [2] [14] [25]. Figure 2 shows a 3-step generic information visualization framework for forensic Honeypot data analysis.…”

Section: Visualization and Visual Analysis Pipelinesmentioning

confidence: 99%

“…Classical information visualization has evolved to include visual analytics [2]. While information visualization concerns the visual representation and presentation of abstract multivariate data mostly in low-dimension space, visual analytics concerns making sense of visual data through visual inspection, and hypothesis formulation and validation for human cognition tasks [2].…”

Section: Visualization and Visual Analysis Pipelinesmentioning

confidence: 99%

“…Information visualization can be described as the study and development of interactive visual representations of complex, abstract data for the purpose of revealing patterns and gaining insights and actionable knowledge for informed human cognitive decision-making [1] [2]. As an established scientific field [2], its application in the fight against cybercrime involving the detection and identification of intrusive attacks from network traffic data is of immense benefits [3] [4] [5].…”

Section: Introductionmentioning

confidence: 99%

“…particularly in fields such as information visualization [2] [11], network anomaly detection [12], data preprocessing, and dimensionality reduction [13] [14] have not presented findings on how visualization methods are used in relation to Honeypot data analysis. Finally, there have been few Honeypot research contributions in the areas of Honeypot data analysis and visualization [10] and this paper hopes to fill this gap.…”

Section: Introductionmentioning

confidence: 99%

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Systematic Review of Graphical Visual Methods in Honeypot Attack Data Analysis

Ikuomenisan¹,

Morgan²

2022

JIS

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Mitigating increasing cyberattack incidents may require strategies such as reinforcing organizations' networks with Honeypots and effectively analyzing attack traffic for detection of zero-day attacks and vulnerabilities. To effectively detect and mitigate cyberattacks, both computerized and visual analyses are typically required. However, most security analysts are not adequately trained in visualization principles and/or methods, which is required for effective visual perception of useful attack information hidden in attack data. Additionally, Honeypot has proven useful in cyberattack research, but no studies have comprehensively investigated visualization practices in the field. In this paper, we reviewed visualization practices and methods commonly used in the discovery and communication of attack patterns based on Honeypot network traffic data. Using the PRISMA methodology, we identified and screened 218 papers and evaluated only 37 papers having a high impact. Most Honeypot papers conducted summary statistics of Honeypot data based on static data metrics such as IP address, port, and packet size. They visually analyzed Honeypot attack data using simple graphical methods (such as line, bar, and pie charts) that tend to hide useful attack information. Furthermore, only a few papers conducted extended attack analysis, and commonly visualized attack data using scatter and linear plots. Papers rarely included simple yet sophisticated graphical methods, such as box plots and histograms, which allow for critical evaluation of analysis results. While a significant number of automated visualization tools have incorporated visualization standards by default, the construction of effective and expressive graphical methods for easy pattern discovery and explainable insights still requires applied knowledge and skill of visualization principles and tools, and occasionally, an interdisciplinary collaboration with peers. We, therefore, suggest the need, going forward, for non-classical graphical methods for visualizing attack patterns and communicating analysis results. We also recommend training investigators in How to cite this paper: Ikuomenisan, G.

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Section: Visualization and Visual Analysis Pipelinesmentioning

confidence: 99%

Section: Visualization and Visual Analysis Pipelinesmentioning

confidence: 99%

Section: Visualization and Visual Analysis Pipelinesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Systematic Review of Graphical Visual Methods in Honeypot Attack Data Analysis

Ikuomenisan¹,

Morgan²

2022

JIS

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An Unsupervised Machine Learning Approach for Process Monitoring by Visual Analytics

Garcés,

Aballay,

Mohan Rao

et al. 2024

IFAC-PapersOnLine

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Exploring the Fusion Potentials of Data Visualization and Data Analytics in the Process of Mining Digitalization

et al. 2023

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Mining digitalisation have been receiving significant attention due to the utilisation of advanced technologies, such as IoT, automation, and sensing. However, maximising the potential value of collected data in the mining industry remains a challenge. Therefore, this paper aims to review timely concern topics to facilitate the fusion implementation in mining engineering. Specifically, this review covers recent popular topics, such as, data visualisation, data management, data analytics, data fusion, visual analytics, and mining digital twin construction. In this paper, we aim to draw a comprehensive picture about the fusion of data visualisation and analytics in the big data context, by examining the recent academic research related to these topics. Therefore, this paper reviews the visualisation domain by conventional classification, including scientific visualisation, information visualisation, and visual analytics, associated with the analysis of current digital twin development. Next, according to the challenges and issues related to visualisation development, this paper reviews the data management and data analytics domains as well. Incorporating with the fusion concept, machine learning-oriented fusion applications and potential scenarios in the mining industry have been discussed. In addition, based on the observation across various domains, this paper presents challenges and future potentials of data fusion in mining.INDEX TERMS mining digitalisation, data management, data analytics, data visualisation, and data fusion I. INTRODUCTION

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Survey on Visualization and Visual Analytics pipeline-based models: Conceptual aspects, comparative studies and challenges

Cited by 6 publications

References 79 publications

Systematic Review of Graphical Visual Methods in Honeypot Attack Data Analysis

Systematic Review of Graphical Visual Methods in Honeypot Attack Data Analysis

An Unsupervised Machine Learning Approach for Process Monitoring by Visual Analytics

Exploring the Fusion Potentials of Data Visualization and Data Analytics in the Process of Mining Digitalization

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