Awesome R cybersecurity collection

A collection of awesome R-language libraries, documents, books, resources and cool stuff.

Libraries

Threat Intelligence

  • VirusTotal - R client for the Virustotal Public API. Virustotal is a Google service that analyzes files and URLs for viruses etc.

  • ThreatCrowd - threatcrowd an R pacakge to work with the ThreatCrowd API

  • IBM X-Force - Tools to Gather Threat Intelligence from ‘IBM’ ‘X-Force’

  • SecurityTrails - Tools to Query the ‘SecurityTrails’ ‘API’

Network Security

  • anomalyDetection - Implements procedures to aid in detecting network log anomalies. By combining various multivariate analytic approaches relevant to network anomaly detection, it provides cyber analysts efficient means to detect suspected anomalies requiring further evaluation.

Main themes

  • net.security - This package provides functions for security standards data management. It comes with data frames of 1000 observations for each security standard and updates are possible from official sources to build updated data sets.

  • httr - The aim of httr is to provide a wrapper for the curl package, customised to the demands of modern web APIs.

  • rgeolocate - IP geolocation is a powerful tool to have if you're dealing with web data, and there are a couple of R packages that provide access to specific services, such as the legacy rgeoip package or Bob Rudis's ipapi. They're all spread about and have diffing interfaces, styles and requirements. (MaxMind)

Articles

Visualization

  • Network-visualization - This is a comprehensive tutorial on network visualization with R. It covers data input and formats, visualization basics, parameters and layouts for one-mode and bipartite graphs; dealing with multiplex links, interactive and animated visualization for longitudinal networks; and visualizing networks on geographic maps.

  • ggnet - The ggnet2 function is a visualization function to plot network objects as ggplot2 objects. It accepts any object that can be coerced to the network class, including adjacency or incidence matrices, edge lists, or one-mode igraph network objects.

Your contributions are always welcome!