Gregor Hohpe

Gregor Hohpe

I help architects connect the IT engine room with the executive penthouse through model thinking, decision discipline, and metaphors. I have experienced IT from virtually every angle: startups, Silicon Valley engineering, corporate IT, and consulting.

Autopentest-drl - !!exclusive!!

: Once a path is chosen, the framework can interface with tools like Metasploit to execute attacks on a real network. Key Features Adaptability

Use pre-trained models from similar network topologies to reduce the "cold start" time required for the agent to learn an optimal path in a new network. Interactive Attack Replay autopentest-drl

The agent’s view of the world. Often a combination of: : Once a path is chosen, the framework

| Feature | Traditional Scanner (e.g., Nessus) | Attack Graph (e.g., MulVAL) | AutoPentest-DRL | | :--- | :--- | :--- | :--- | | | High | Medium (depends on data quality) | Low (learns from action outcomes) | | Sequence awareness | None (independent checks) | Yes (paths) | Yes (optimal sequences) | | Adaptability to network changes | None (re-run required) | None (new graph required) | High (re-plans online) | | Handles unknown vulnerabilities | No (signature-based) | No | Partially (transfer learning) | | Exploration vs exploitation | Only exploitation (fixed rules) | Only exploitation (static path) | Dynamic balance | | Resource efficiency | Poor (scans everything) | Good | Excellent (learns to probe likely targets first) | Often a combination of: | Feature | Traditional Scanner (e

The age of the algorithmic hacker has begun. is its first, most compelling prototype.

Automated penetration testing is not a scripting problem; it is a . That is precisely the domain where Reinforcement Learning excels.

Researchers showed that an agent trained on a simulated enterprise network could, with fine-tuning on fewer than 1000 episodes, adapt to a cloud-based environment (AWS with misconfigured S3 buckets and EC2 instances). This is a major step toward practical, deployable agents.