back

Mastering the Maze

Unleashing Reinforcement Learning in Penetration Testing for Lateral Network Movement

If you suspend your transcription on amara.org, please add a timestamp below to indicate how far you progressed! This will help others to resume your work!

Please do not press “publish” on amara.org to save your progress, use “save draft” instead. Only press “publish” when you're done with quality control.

Video duration
00:20:23
Language
English
Abstract
## How can artificial intelligence support penetration testing?

Most processes in for the penetration-testing cycle require detailed knowledge, time and human resources.
While the are sophisticated scripts for the reconnaissance and various exploits, creating a detailed plan of the attack path can be complicated and laborious. The use of an enforcement learning algorithm can help penetration-testing identify the various attack vectors and provide a detailed overview of the system landscape. This can automate important aspects of the process and make it more efficient.

We like show an overview, on how reinforcement learning can be integrated into the penetration testing process to gain automated access to a system landscape.
To achieve this, we show approaches how an AI can be used for lateral movement within the system landscape to subject an entire landscape to the penetration-testing process.

We like show an overview, on how reinforcement learning can be integrated into the penetration testing process to gain automated access to a system landscape.

Talk ID
camp2023-57188
Event:
camp2023
Day
4
Room
Milliways
Start
10 a.m.
Duration
00:20:00
Track
Milliways
Type of
Short Talk
Speaker
Jakob
Richard Sprenger
Jakob
Talk Slug & media link
camp2023-57188-mastering_the_maze
English
0.0% Checking done0.0%
0.0% Syncing done0.0%
0.0% Transcribing done0.0%
100.0% Nothing done yet100.0%
  

Work on this video on Amara!

English: Transcribed until

Last revision: 8 months, 3 weeks ago