Graph Visualization of Cyber Threat Intelligence Data for Analysis of Cyber Attacks

SÜLÜ>, Mücahit; Daş, Resul

doi:10.17694/bajece.1090145

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…In most places with big data, visualization is used to understand the data [12,13]. Bachechi et al used information visualization techniques to analyze urban traffic data and the effect of traffic emissions on urban air quality.…”

Section: B Related Workmentioning

confidence: 99%

Actionable Data Visualization for Air Quality Data in the Istanbul Location

MENGÜŞ

Daş

2022

Balkan Journal of Electrical and Computer Engineering

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Air pollution is increasing day by day due to the increasing population, urbanization, and industrial development. In our country, the amounts of pollutants in the air are recorded every day at different points. These recorded data continue to be collected in an increasing amount day by day. Information overload, which renders the data meaningless, complicates the interpretation of these data. One of the ways to solve this problem is to visualize curves and trends in measured pollution concentrations over time. In this study, using the data provided by the continuous monitoring center of the Turkey Ministry of Environment, Urbanization and Climate Change, visualization of different pollutants in the air was provided. Scatter plots, line scatter plots, and bar plots were used as data visualization. Data visualization makes it easy for non-experts to estimate air quality information from the concentration profiles displayed.

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Section: B Related Workmentioning

confidence: 99%

Actionable Data Visualization for Air Quality Data in the Istanbul Location

MENGÜŞ

Daş

2022

Balkan Journal of Electrical and Computer Engineering

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“…Because attackers are getting smarter and more stubborn, standard cybersecurity methods don't always work to stop constantly changing threats. Because of this, we need new methods that can provide effective and thorough threat intelligence research right away to protect ourselves from online dangers [1]. In answer to this need, this study suggests a new approach that uses graph theory and machine learning together to improve the assessment of cyber threats.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Frameworks for Threat Intelligence Analysis: Leveraging Graph Theory and Machine Learning for Cyber Threat Assessment

Kanchan Rahul Jamnik

2024

cana

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Because there are so many complex online risks, we need new ways to look at threat data. This study suggests a complete approach that combines graph theory and machine learning methods to make figuring out cyber threats better. The basic idea behind networks is graph theory, which lets us show the complicated connections between different things in a connected world. This approach gives a full picture of the danger scene by representing cyber entities and how they interact as nodes and lines in a graph. This makes it easier to spot trends and outliers. The system includes machine learning techniques that make use of the huge amount of data that is available for analyzing cyber threats. Supervised learning methods are used for classification tasks. These let threats be put into groups based on past data and known patterns of bad behavior. Unsupervised learning methods, on the other hand, make finding anomalies easier by noticing changes in how networks normally behave. These machine learning models learn to adapt to changing threats by being trained and improved over and over again. This makes methods for finding threats and stopping them more effective. Combining graph theory and machine learning makes it possible to get useful information from a huge number of different data sources. Graph-based analytics bring together different kinds of data, like network traffic, system logs, and threat intelligence feeds, into a single view. This helps you see the connections between things that don't seem to be related. Machine learning algorithms improve this analysis by finding small patterns and trends that point to bad behavior. This gives cybersecurity professionals the power to stop new threats before they happen. Scalability and freedom are built into the suggested system so it can adapt to changing cyber dangers and network platforms. It can handle big datasets and real-time streaming data well by using distributed computer structures and flexible machine learning methods. This makes sure that threats are found and dealt with quickly. Putting graph theory and machine learning together is a good way to make threat intelligence research better in defense.

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A key review on graph data science: The power of graphs in scientific studies

Daş

SÜLÜ>

2023

Chemometrics and Intelligent Laboratory Systems

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Graph Visualization of Cyber Threat Intelligence Data for Analysis of Cyber Attacks

Cited by 4 publications

References 17 publications

Actionable Data Visualization for Air Quality Data in the Istanbul Location

Actionable Data Visualization for Air Quality Data in the Istanbul Location

Mathematical Frameworks for Threat Intelligence Analysis: Leveraging Graph Theory and Machine Learning for Cyber Threat Assessment

A key review on graph data science: The power of graphs in scientific studies

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