CyberAttacks ScatterPlot - Final Project Draft v1

CyberAttacks ScatterPlot - Final Project Draft v1

This interactive scatter plot visualizes cyber attack event types against years, with points colored by attacking actor. Users can brush over the plot to highlight specific data points. The visualization uses D3 scales (scaleOrdinal, scalePoint), axes, a color legend, and brush behavior to enable selection. Data comes from the University of Maryland CISSM Cyber Attacks Database, filtered by industry code for display.

95% match

CyberAttacks Combined Vizs- Final Project Draft v4 (all data version)

This visualization uses D3.js to combine a multi-line chart and a scatter plot on a single page, presenting cyber attack data across industries and years. The line chart shows attack counts over time per industry, with a color legend that filters the scatter plot by industry. The scatter plot displays individual attack events by date and attack type, using D3’s brush behavior to highlight selected points. Both charts rely on D3 scales (scaleTime, scalePoint, scaleOrdinal) and custom axis and legend modules, rendering as SVG. Data is sourced from the `@python-monty/full_cyber_attack_data` package.

CyberAttacks Final Draft

This visualization presents cyber attack data from 2014 to 2023 through two linked panels. The left panel uses d3’s `line` generator and `scaleTime`/`scaleLinear` to show attack counts per industry over time, with a custom color legend supporting twenty categories. The right panel displays filtered scatter plot data using `scalePoint`, showing attack types by year for a selected industry, with actor-based coloring. Interaction includes hover highlighting and click-based filtering, implemented through d3 selection joins and event handlers.

CyberAttacks Combined Vizs- Final Project Draft v1

This example combines a line chart and a scatter plot to explore cyber attack data. The line chart plots yearly trends for selected industry codes, with interactive menus to filter the data. The scatter plot displays individual attack events, using a color legend to encode industry type and supporting brush selection to highlight specific data points. Both visualizations use d3 scales, axes, and color legends, with a linked hovered value from the line chart's color legend filtering the scatter plot's data. The code uses d3.flatGroup, d3.line, d3.brush, and d3.scalePoint, among other APIs, and pulls data from the full_cyber_attack_data package.

CyberAttacks Final Draft v5

Two coordinated visualizations explore cyber attack data from 2014 to 2023. A multi-line chart displays attack counts per industry over time, with a clickable color legend; selecting an industry filters the adjacent scatter plot to show only that sector's attacks by year and attack type, colored by actor. The scatter plot supports d3.brush for highlighting points. The code uses d3.scaleOrdinal, d3.line, d3.axisLeft, and d3.axisBottom, rendering as SVG with data sourced from a local dataset.

CISSM_CyberAttacks (Fork of Simple Scatter Plot)

This scatter plot visualizes CISSM cyber attack records, plotting event dates on the x-axis against attacker actors on the y-axis. Each circle is color-coded by the affected industry using D3’s `scaleOrdinal` and `schemeCategory10`. The visualization uses D3’s `scalePoint` for both axes and renders axis labels via the `@python-monty/axis_labels` library. The underlying data comes from the CISSM-EDA dataset, and the code forks an existing example to replace the sample data with cyber attack records.

87% match

CyberAttacks Combined Vizs- Final Project Draft v2

This visualization presents a combined line chart and scatter plot of cybersecurity attack data, which is filtered by three industry selectors. The line chart shows trends over time with a color legend, while the scatter plot displays the distribution of attack subtypes per year and allows users to directly brush-select data points on the plot. Built with D3.js, the code uses the `scaleTime`, `scaleLinear`, and `scaleOrdinal` APIs for scales, `line` for the line generation, and `brush` for interactive selection. The underlying datasets, `full_cyber_attack_data` and `newData`, are imported from an external package.

87% match

Fork of Scatter Plot with Color

This example presents a scatter plot of Cyber Attack data from the University of Maryland CISSM database, showing the relationship between event date and attacker actor, colored by industry type. A color legend and brush interaction allow users to highlight specific data points, which update the circle strokes dynamically. The visualization uses d3.scalePoint for both axes, d3.scaleOrdinal with schemeCategory10 for coloring, and a d3.brush to handle selection. Axes and legend are rendered through custom functions that leverage d3.axisLeft, d3.axisBottom, and d3-selection methods.