mal-toolbox 0.0.39

A collection of tools used to create MAL models and attack graphs.

Overview

A collection of python modules to help developers create and work with MAL models and attack graphs.

The Language Module

The language module provides various tools to process MAL languages.

The Language Specification Submodule

The language specification submodule provides functions to load the specification from a .mar archive(load_language_specification_from_mar) or a JSON file(load_language_specification_from_json). This specification will then be used to generate python classes representing the assets and associations of the language and to determine the attack steps for each asset when generating the attack graph.

The Language Classes Factory Submodule

The language classes factory submodule is used to generate python classes using the python_jsonschema_objects package from a language specification. The classes generated by the create_classes function can then be accessed from within that namespace(.e.g: lang_classes_factory.ns.Application(), lang_classes_factory.ns.AppExecution()). Because these classes are built using JSON Schema validators they will enforce their restrictions when using the python objects created. These classes are typically used in conjunction with model module to create instance models.

The Model Module

The model module is used to create MAL instance models. The model class will store all of the relevant information to the MAL instance model, most importantly the assets and associations that make it up. These assets and associations should be objects created using the language classes factory submodule. It also allows for Attacker objects to be created and associated with attack steps on assets in the model. The most relevant functions here are the ones used to add different elements to the model, add_asset, add_association, and add_attacker. Model objects can be used to generate attack graphs using the attack graph module.

The Attack Graph Module

The attack graph module contains tools used to generate attack graphs from existing MAL instance models and analyse MAL attack graphs. The function used to generate the attack graph is generate_graph and it requires the instance model and language specification. The resulting attack graph will contain nodes for each of the attack steps. The structure of the attack node data class can be seen in attackgraph/node.py file. Of note are the lists of children and parents which allow for easy reference to the other attack step nodes related and the asset field which will contain the object in the model instance to which this attack step belongs to, if this information is available.

If it is relevant the attach_attackers function can be called on the resulting attack graph with the instance model given as a parameter in order to create attack step nodes that represent the entry points of the attackers and attach them to the attack steps specified in the instance model.

Ingestors Module

The ingestors module contains various tools that can make use of the instance model or attack graph. Currently the Neo4J ingestor is the only one available and it can be used to visualise the instance model and the attack graph.

Command Line Client

In addition to the modules that make up the MAL-Toolbox package it also provides a simple command line client that can be used to easily generate attack graphs from a .mar language specification file and a JSON instance model file.

The usage is: maltoolbox gen_ag [--neo4j] <model_json_file> <language_mar_file>

If the --neo4j flag is specified the model and attack graph will be loaded into a local Neo4J instance.

Configuration

A default configuration file default.conf can be found in the package directory. This contains the default values to use for logging and can also be used to store the information needed to access the local Neo4J instance.