File size: 1,943 Bytes
ca4a0ef | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | {
"nbformat": 4,
"nbformat_minor": 5,
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.11.0"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Security Behavior Analytics \u2014 Cohort & Peer EDA\n",
"\n",
"**Aria AI Security Data Science Team**\n",
"\n",
"Exploratory analysis on synthetic enterprise IAM/access behavior events.\n",
"\n",
"> Artifacts are pre-computed \u2014 no external downloads or model training required.\n"
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"import json\n",
"from pathlib import Path\n",
"\n",
"ROOT = Path('.')\n",
"summary = json.loads((ROOT / 'assets/demo/summary.json').read_text())\n",
"cohorts = json.loads((ROOT / 'assets/demo/cohort_analysis.json').read_text())\n",
"risk = json.loads((ROOT / 'assets/demo/risk_analysis.json').read_text())\n",
"print('Events:', summary['total_events'])\n",
"print('Users:', summary['total_users'])\n",
"print('Critical risk:', risk['summary']['critical'])\n"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"metadata": {},
"source": [
"import plotly.graph_objects as go\n",
"\n",
"dept = cohorts['dimensions']['department']['cohorts'][:10]\n",
"fig = go.Figure(go.Bar(x=[c['cohort'] for c in dept], y=[c['event_count'] for c in dept]))\n",
"fig.update_layout(title='Events by Department Cohort', template='plotly_dark')\n",
"fig.show()\n"
],
"outputs": [],
"execution_count": null
}
]
} |