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UID:pretalx-hack-lu-2025-QV9GZF@pretalx.com
DTSTART;TZID=CET:20251024T141500
DTEND;TZID=CET:20251024T144500
DESCRIPTION:Our lives depend on automotive cybersecurity\, protecting us in
 side and near vehicles. If vehicles go rogue\, they can operate against th
 e driver’s will and potentially drive off a cliff or into a crowd. The 
 “Automotive Security Analyzer for Exploitability Risks” (AutoSAlfER) e
 valuates the exploitability risks of automotive on-board networks by attac
 k graphs. AutoSAlfER’s Multi-Path Attack Graph algorithm is 40 to 200 ti
 mes smaller in RAM and 200 to 5 000 times faster than a comparable impleme
 ntation using Bayesian networks\, and the Single-Path Attack Graph algorit
 hm constructs the most reasonable attack path per asset with a computation
 al\, asymptotic complexity of only O(n * log(n))\, instead of O(n²). Auto
 SAlfER runs on a self-written graph database\, heuristics\, pruning\, and 
 homogenized Gaussian distributions and boosts people’s productivity for 
 a more sustainable and secure automotive on-board network. Ultimately\, we
  enjoy more safety and security in and around autonomous\, connected\, ele
 ctrified\, and shared vehicles.
DTSTAMP:20260613T002120Z
LOCATION:Europe
SUMMARY:Automotive Security Analyzer for Exploitability Risks: An Automated
  and Attack Graph-Based Evaluation of On-Board Networks - Martin Salfer
URL:https://pretalx.com/hack-lu-2025/talk/QV9GZF/
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