Don't be evil.
The script is designed to simulate a cybersecurity scenario in which an agent learns to perform various actions in order to infect machines, perform self-healing, and propagate to other machines. The agent uses an advanced Q-learning algorithm enhanced with curriculum learning, multi-task learning, memory augmentation, neuro-symbolic integration, and continuous learning to improve its decision-making capabilities.
In simpler terms, the script is like a game where the agent learns to take actions to achieve a goal (in this case, infecting machines and spreading the infection). The agent uses a special kind of learning algorithm called Q-learning, enhanced with several advanced techniques, to figure out which actions are the best to take in each situation.
The script takes a list of IP addresses as input and scans them to see if they are vulnerable to a specific exploit. If a vulnerable machine is found, the agent tries to infect it by connecting to a remote server and executing a payload. The agent performs periodic self-healing actions to ensure smooth operation and propagates to other machines to spread the infection.
The Q-learning algorithm uses a Q-table to keep track of the expected rewards for each action in each state and updates the Q-table based on the rewards received for each action taken. The agent balances exploration and exploitation of the environment using a decaying exploration probability.
- Python 3.6+
- Libraries: subprocess, threading, numpy, psutil, urllib, concurrent.futures
The script can be run from the command line with various options to customize its behavior:
python script.py <ip_range> --remote-server <remote_server> --port <port> --payload-url <payload_url>- ip_range: A list of IP addresses to scan.
- --remote-server: The remote server to connect to (default: example.com).
- --port: The port to connect to on the remote server (default: 8080).
- --payload-url: The URL of the payload to download and execute.
- Curriculum Learning: The agent progresses from simpler to more complex tasks, allowing for gradual learning and adaptation.
- Multi-Task Learning: The agent is trained on various tasks simultaneously, improving its ability to generalize across different scenarios.
- Memory Augmentation: The agent maintains a long-term memory of past experiences, which it uses to make more informed decisions.
- Neuro-Symbolic Integration: Incorporating symbolic reasoning helps the agent make decisions based on logical rules and current states.
- Continuous Learning: The agent continuously updates its knowledge base, adapting to new data and changing environments.
- Environment Exploration: The
explore_environmentfunction allows the agent to gather information about the current system.
By integrating these advanced techniques, the script provides a robust framework for simulating and analyzing complex cybersecurity scenarios.
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