Dynamic IRS for connected and autonomous vehicles

Contributors

The main author of this code is Michael Kühr. This code was written to demonstrate the research paper "REACT: Dynamic Intrusion Response System for Connected and Autonomous Vehicles."

System requirements

The following software is required in advance to use this IRS:

1. Python, Version 3.8.10 or higher. Install via apt-get install python3
2. PuLP, Version 2.6.0 or higher. Install via pip install pulp
3. GLPK, Version 4.65-2 or higher. Install via apt-get install glpk-utils

Configuration

Since this software represents only an IRS, some data needs to be provided manually, which is normally provided by an IDS.

All the relevant data is stored inside system_state.xml. It can be edited using any text editor. Since this IRS can not only handle a single intrusion but also cover stepping-stone attacks, multiple states can be included, each representing one intrusion to solve. For each state, the following information is required:

• infected_asset: This represents the asset that is infected by a computer virus/hacker. It can be any asset from the enum Asset of ref_architecture.py. Please refer to the standardized syntax: Asset.<your asset>, where <your asset> can be replaced by any Asset.
• affected_asset: This represents the asset that is under attack / which is the victim of an intrusion. It can be any asset from the enum Asset of ref_architecture.py. Please refer to the standardized syntax: Asset.<your asset>, where <your asset> can be replaced by any Asset.
• intrusion_result: This represents the "detected" intrusion result. It can be any intrusion result from the enum AttackResult of ref_architecture.py. Please refer to the standardized syntax: AttackResult.<your intrusion result>, where <your intrusion result> can be replaced by any AttackResult.

Additionally, the current vehicle velocity is required. In a real-world setup, this is gathered via sensors, but in this prototype, it will be read using the dynamic_state.xml file. This contains the following elements:

• dynamic_parameters/velocity: The current velocity of the vehicle. It can be any float or integer value

Execution

After installing all system requirements and configuring your project, it can be simply executed via:

python3 main.py

Note: Since a real IDS is missing, some manual user-input is necessary in order to provide information. The tool will ask for that information in case it is necessary.

For developers only

This section briefly describes all available files in this root directory. The configuration files are separately described in their respective sub-folder.

Please note: This section is only meant to be used by developers, not users!

main.py

Main IRS program, calling call methods inside classes, to run the IRS functionality

response.py and intrusion.py

Two files to described responses and intrusions as classes

ref_architecture.py

Contains mainly Enums to provide all the data structures

ids_dummy.py

Since no real IDS is available, this script gives all the information, which is normally provided by an IRS. Overview of public methods:

• getDetectedIntrusionDummy: Return random assets and attack results (only for testing purposes)
• getDetectedIntrusionManual: The user must enter all assets and attack results via the terminal
• getDetectedIntrusionManualGUI: The user can provide all assets and attack results via a GUI
• getDetectedIntrusionXml: Uses the system_state.xml file to read out the assets and attack results. By providing and index, the n-th intrusion (= state) will be read.

risk_assessment.py

This script performs a threat analysis and risk assessment of the detected intrusion under consideration of the dynamic vehicle parameters (currently only velocity). It has only one public method, RiskEvaluation, which is used to evaluate the risk of a detected intrusion. By providing and index, the n-th intrusion (= state) will be read.

response_set_generation.py

This script identifies all possible responses that can be applied in the detected scenario. It has only one public method, createResponseSet, which returns the list of applicable responses.

optimal_response_selection.py

The actual identification of the optimal response will be handled here. Its only public method, getOptimalResponse, has the optional parameter method, which is a string identifying the method to calculate the best response. If it is not provided, the user will be asked to select the method manually.

check_precondition.py

After identifying the optimal response, its precondition will be checked in this script. It has one public method, checkPrecondition, which tries to check the preconditions automatically, but might rely on user inputs.

ids_feedback.py

Depending on the success of an applied response, the response-specific parameters need to be adapted. This will happen here. Its public method, idsFeedback, will ask the user to provide the success of the response as a yes/no question. The IDS normally senses this information, but it needs to be emulated here. This script will adapt the XML-files in the folder dynamic_updated_Parameters