From 92671796a15801366d26d9eaf8510e2dd629b46f Mon Sep 17 00:00:00 2001 From: Chris McCarthy Date: Thu, 27 Jul 2023 11:40:29 +0100 Subject: [PATCH] Added GFX license conditions. Included LICENSE file in build. Fixed a few character issues in README.md --- LICENSE | 28 ++++++++++++++++++++++++++++ README.md | 28 ++++++++++++++-------------- pyproject.toml | 3 ++- 3 files changed, 44 insertions(+), 15 deletions(-) create mode 100644 LICENSE diff --git a/LICENSE b/LICENSE new file mode 100644 index 00000000..3f5e4bb3 --- /dev/null +++ b/LICENSE @@ -0,0 +1,28 @@ +MIT License License + +MIT License Conditions + +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. + + + + diff --git a/README.md b/README.md index 390f7f50..4baf47b9 100644 --- a/README.md +++ b/README.md @@ -1,38 +1,38 @@ # PrimAITE -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 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 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; +- 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: +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; +- 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; +- 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; +- 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; +- 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); +- 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; +- 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; +- 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)​; +- 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.  +- 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) diff --git a/pyproject.toml b/pyproject.toml index 3cd5922a..b66b0168 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ build-backend = "setuptools.build_meta" name = "primaite" description = "PrimAITE (Primary-level AI Training Environment) is a simulation environment for training AI under the ARCD programme." authors = [{name="Defence Science and Technology Laboratory UK", email="oss@dstl.gov.uk"}] -license = {text = "MIT License"} +license = {file = "LICENSE"} requires-python = ">=3.8, <3.11" dynamic = ["version", "readme"] classifiers = [ @@ -47,6 +47,7 @@ readme = {file = ["README.md"]} [tool.setuptools] package-dir = {"" = "src"} include-package-data = true +license-files = ["LICENSE"] [project.optional-dependencies]