Skip to main content
SimilarExport ZIP
100%

Cyber Attacks

✓ Published0🌍 Public
Ccoreyfnolan@gmail.com
Last edited Feb 1, 2024
Created on Jan 29, 2024
Forked from World Population

This example displays the first record from a CSV dataset of cyber security attacks as a raw JSON object in a preformatted text block. It loads the data with a JSON import, parses the CSV, converts the `Packet Length` and `Anomaly Scores` fields to numbers, and then renders the parsed object’s fields. The visualization relies on the native Web Platform APIs for parsing and DOM manipulation, without external charting libraries. The source data is Inscribo's Cyber Security Attacks dataset on Kaggle.

AI-generated description

[Data from Inscribo's Cyber Security Attacks dataset on kaggle.com] [https://www.kaggle.com/datasets/teamincribo/cyber-security-attacks/data] cleaned/normalized using Python, exported to csv, loaded and parsed as csv

Tasks:

I'd like to be able use this data to identify common geolocations of cyber attacks.

MIT Licensed

Similar vizzes

Loading thumbnail…

Cyber_Attacks_V2

This example displays the first record from a dataset of cyber attacks, showing parsed fields such as the event date, actor, organization, industry, and motive. The code imports data from a CSV file and uses D3’s `utcParse` to convert the date strings into JavaScript `Date` objects, extracting the year for each entry. It also coerces the `industry_code` column into a numeric value with the unary plus operator. The visualization renders the resulting JSON representation of a single data point in a styled `<pre>` element, demonstrating the initial data processing and output structure.

Ccoreyfnolan@gmail.com
84% match
Loading thumbnail…

Cyber Attacks by Year/Industry

This example displays the first ten rows of a dataset on cyber attacks, showing the number of attacks per industry and year. The parsed quantitative values are formatted as JSON in a pre tag, using the d3 library for data parsing. The data comes from a CSV file detailing industry codes, years, and attack counts across various sectors. The visualization demonstrates a simple data inspection approach, converting raw CSV data into viewable structured text.

Ccoreyfnolan@gmail.com
79% match
Loading thumbnail…

Yearly Cyber Attack Data

This visualization presents yearly cyber attack counts from the CISSM Cyber Attacks dataset, showing fluctuations from 2014 to 2023 with a notable spike in 2022. The code parses the CSV data, converting year strings to Date objects and attack counts to numbers using `Date.UTC` before displaying the first ten records as JSON. It relies on the d3 library version 7.8.5 for data handling and rendering, though the current implementation only outputs the parsed data structure rather than a graphical chart.

Ccoreyfnolan@gmail.com
76% match
Loading thumbnail…

Full Cyber Attack Data

This visualization presents processed cyber attack data, showing the number of events per year for each industry sector. The code filters out entries with undetermined actor types or event types, then aggregates counts by industry code and year, producing a structured dataset. It uses JavaScript array methods like `filter` and `push` to compile the data, and exports the resulting `newData` and `yearlyData` arrays for use in other modules. The current display renders sample JSON records in a preformatted HTML element.

Ccoreyfnolan@gmail.com
75% match
Loading thumbnail…

Cyber Attacks by Year v2 w/ Responsive Axes

This line chart visualizes annual cyber attack counts from the CISSM Cyber Attacks dataset, with hover guides that track the mouse position. The visualization uses D3 scales and axes rendered via custom `xAxis` and `yAxis` functions from the `corey-responsive-axis` package, which adapt to container resizing through `observeResize`. The line is drawn using D3’s `line` generator with a time scale for years and a linear scale for attack counts. Data was pre-processed in Python before being imported into the VizHub environment.

Ccoreyfnolan@gmail.com
72% match
Loading thumbnail…

Fork of Cyber Attacks by Year v3

This fork of the Cyber Attacks by Year visualization displays annual cyber attack counts from 2014 to 2023, with multiple colored lines representing different industry codes. The chart uses D3.js with a time-scaled x-axis, linear y-axis, and an ordinal color scale based on the category10 scheme. Responding to mouse position, it draws red crosshair lines and uses axis labeling methods from the `@python-monty/corey-responsive-axis` library. The data originates from the CISSM Cyber Attacks database, and the visualization is implemented using `svg`, `scaleTime`, `scaleLinear`, `line`, and `flatGroup` APIs.

Ccoreyfnolan@gmail.com
71% match
Loading thumbnail…

CyberAttacks LineChart - Final Project Draft v2

This interactive line chart displays annual cyber attack counts across industry sectors, using data from the CISSM Cyber Attacks dataset. Three drop-down menus allow viewers to select up to three industry codes, and the chart renders a multi-line visualization with a color legend that updates dynamically. Built with D3.js, the visualization employs `flatGroup`, `scaleOrdinal`, and `schemeCategory10` to group and color lines, while custom axis and color legend modules handle responsive rendering. The chart includes an inner rectangle background and labeled axes showing year and attack counts for the filtered industries.

Ccoreyfnolan@gmail.com
71% match
Loading thumbnail…

CyberAttacks LineChart - Final Project Draft v1

This interactive line chart visualizes annual cyber attack counts across selected industry sectors from 2018 onward. Users can select up to three industry codes via dropdown menus to compare attack trends over time, with each industry represented by a distinct colored line. The visualization is built with D3.js and uses the CISSM Cyber Attacks dataset, encoding years on the x-axis and attack counts on the y-axis. The chart includes a color legend and hover interactions to highlight specific data points.

Ccoreyfnolan@gmail.com
70% match