Users can run a "logical attack" using a sample network topology. In this mode, no actual exploits are launched. Instead, the DRL agent determines the optimal attack path based on the network's configuration, allowing researchers to study attack mechanisms without risk.
The keyword represents more than just another security tool. It embodies a shift from automated (following fixed playbooks) to autonomous (learning optimal strategies through interaction). As networks grow more fluid and attacks more AI-driven, static defenses will fail. Deep Reinforcement Learning offers a path to dynamic, adaptive, and continuously learning cyber defense. autopentest-drl
: DRL is better suited for high-dimensional network environments compared to traditional rule-based scanners. Users can run a "logical attack" using a
| Method | Success Rate (%) | Avg. Steps | Time (min) | Coverage (%) | |-------------------|-----------------|------------|------------|--------------| | Random | 12.3 | 147 | 28.4 | 34.1 | | Metasploit Autopwn| 45.6 | 62 | 12.3 | 58.7 | | Q-learning | 52.1 | 58 | 11.8 | 63.2 | | OpenVAS + Manual | 78.4 | N/A | 89.0 | 81.5 | | | 91.7 | 33 | 7.4 | 92.3 | The keyword represents more than just another security tool