Merge branch 'github_dev' into release/2.0.0

LICENSE

@@ -1,21 +1,28 @@
-MIT License
+MIT License License
 
-Copyright (c) 2023 - 2025 Defence Science and Technology Laboratory UK (https://dstl.gov.uk)
+MIT License Conditions
 
-Permission is hereby granted, free of charge, to any person obtaining a copy
+These MIT License conditions confirm the provision of the following artefacts as MIT License by Defence Science and Technology
 of this software and associated documentation files (the "Software"), to deal
 in the Software without restriction, including without limitation the rights
+
 to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 copies of the Software, and to permit persons to whom the Software is
 furnished to do so, subject to the following conditions:
 
 The above copyright notice and this permission notice shall be included in all
 copies or substantial portions of the Software.
-
 THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+
 IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
+
+
+
+

README.md

@@ -1,12 +1,41 @@
 # PrimAITE
 
-PrimAITE (Primary-level AI Training Environment) is a simulation environment for training AI under the ARCD programme.
+![image](https://github.com/Autonomous-Resilient-Cyber-Defence/PrimAITE/assets/107395948/c59cc1c2-b5eb-4e0f-91a1-ce0036295e54)
+
+
+The ARCD Primary-level AI Training Environment (**PrimAITE**) provides an effective simulation capability for the purposes of training and evaluating AI in a cyber-defensive role. It incorporates the functionality required of a primary-level ARCD environment, which includes:
+
+- The ability to model a relevant platform / system context;
+
+- The ability to model key characteristics of a platform / system by representing connections, IP addresses, ports, traffic loading, operating systems, services and processes;
+
+- Operates at machine-speed to enable fast training cycles.
+
+PrimAITE presents the following features:
+
+- Highly configurable (via YAML files) to provide the means to model a variety of platform / system laydowns and adversarial attack scenarios;
+
+- A Reinforcement Learning (RL) reward function based on (a) the ability to counter the specific modelled adversarial cyber-attack, and (b) the ability to ensure success;
+
+- Provision of logging to support AI evaluation and metrics gathering;
+
+- Uses the concept of Information Exchange Requirements (IERs) to model background pattern of life and adversarial behaviour;
+
+- An Access Control List (ACL) function, mimicking the behaviour of a network firewall, is applied across the model, following standard ACL rule format (e.g. DENY/ALLOW, source IP address, destination IP address, protocol and port);
+
+- Application of IERs to the platform / system laydown adheres to the ACL ruleset;
+
+- Presents an OpenAI gym or RLLib interface to the environment, allowing integration with any OpenAI gym compliant defensive agents;
+
+- Full capture of discrete logs relating to agent training (full system state, agent actions taken, instantaneous and average reward for every step of every episode);
+
+- NetworkX provides laydown visualisation capability.
 
 ## Getting Started with PrimAITE
 
 ### 💫 Install & Run
 **PrimAITE** is designed to be OS-agnostic, and thus should work on most variations/distros of Linux, Windows, and MacOS.
-Currently, the PRimAITE wheel can only be installed from GitHub. This may change in the future with release to PyPi.
+Currently, the PrimAITE wheel can only be installed from GitHub. This may change in the future with release to PyPi.
 
 #### Windows (PowerShell)
 

pyproject.toml

@@ -10,7 +10,7 @@ license = {file = "LICENSE"}
 requires-python = ">=3.8, <3.11"
 dynamic = ["version", "readme"]
 classifiers = [
-    "License :: OSI Approved :: MIT License",
+    "License :: MIT License",
     "Development Status :: 5 - Production/Stable",
     "Operating System :: Microsoft :: Windows",
     "Operating System :: MacOS",