# Problem statement
MISP being a P2P system, various users and organisations are sharing data, sometimes without even knowing each others. While having access to a lot of information is extremelly benificial for all parties, it, however, also induces challenges to deal with.
In this blogpost, we will mainly discuss about information **quality** and **freshness**, other issues like **trust**, **use-cases**, **interests**, etc. are partially taken into account but will not be presented. Nevertheless, these concepts are examined in this [paper](https://arxiv.org/abs/1902.03914) along with a detailed explanation of the solution we've choosen to tackle these issues.
Our main objective is to provide users a **simple yet customizable system** to automatically (or manually) mark an *Indicator Of Compromise* (or more generic, an *Attribute*) as **expired**.
Before getting started to show how the model presented in the paper is implemented in MISP, we first need to have a look at some concepts needed to better understand how components are working and tied together.
# The (potentially) annoying bits of theory
The solution currently supported in MISP is based on two components: ``base_score`` and ``score``. The idea is to have an initial fixed value called ``base_score`` taking into account the **quality** of an indicator; and a time-dependant ``score``, which decreases the more time passes.
A simplified version would be something like this:
```
score = base_score * P
```
Where ``P`` is composed of ``parameters``:
- ``lifetime``: The lifetime of the IOC or the time at which the score of the *Attribute*'s score will be 0
- ``decay_speed``: The speed at which the decay happens or the speed at which an *Attribute* will loose score
**⚠** **It should be noted that everytime a [*Sightings*](https://www.circl.lu/doc/misp/sightings/) is added to an *Attribute*, the ``score`` is refresh to the ``base_score`` and a new decay is initiated from that point.**
# Polynomial Decaying Model built-in in MISP
We still have to see how the ``base_score`` is actually computed. In the built-in version of the *Decaying Model* in MISP, the ``base_score`` is computed from the *Taxonomies* and some weigths. Weights are a mean to prioritize extracted ``numerical_values`` from *Taxonomies* over others.
To give the intuition of how the ``base_score`` computation works, let's look at two examples. In these examples, the two *Taxonomies* used are
[*phishing*](https://github.com/MISP/misp-taxonomies/blob/master/phishing/machinetag.json) and [*admiralty-scale*](https://github.com/MISP/misp-taxonomies/blob/master/admiralty-scale/machinetag.json). Both of them contain *Tags* that have a ``numerical_value`` associated to them:
- 
, ``numerical_value = 100``
- 
, ``numerical_value = 25``
- 
, ``numerical_value = 75``
So, if an *Attribute* only have one *Tag* attached, let's say ``admiralty-scale:source-reliability="Completely reliable"``, the ``base_score`` would be:
```
base_score = 100
```
Weights come into action when multiple *Tags* are attached to an *Attribute*. To make things a bit easier, let's suppose that both *Taxonomies* should have the same importance in regards to the *Attribute*'s score. Thus, the total weigth (100) will be shared, assigning both *Taxonomy* a weight of 50.
```
admiralty-scale = 50
phishing = 50
---------------------
sum 100
```
If an *Attribute* has the *Tags* 
and attached, the computation steps would look like this:
Thus, the ``base_score`` of this *Attribute* will be ``87.50``.
# Short tutorial
Now that we've seen the basic concepts, let's have a look at how MISP implents these components. For these examples, we are using the default [phishing model](https://github.com/MISP/misp-decaying-models/blob/master/models/phishing-model.json) model on a **test** *Event*.
## Practical integration in MISP
### Endpoint: ``events/view``
At the *Event* level, a new filtering button has been added to attach the real-time computed ``score`` of any *Attributes* that has been mapped to a *Model*.
### Endpoint: ``attribute/restSearch``
The ``attribute/restSearch`` endpoint has been updated and now supports four new parameters to filter out expired *Attributes* or play with the different available models.
- ``includeDecayScore`` **[bool]**: Attach the real-time computed ``score`` of the *Attribute* along with *Model(s)* informations
- ``excludeDecayed`` **[bool]**: Filter out all expired IOC
- ``decayingModel`` **[list]**: List of *Model(s)*, which will be attached to the *Attribute*
- ``modelOverrides`` **[dict]**: JSON that can be used to on-the-fly modify *Model(s)* parameters
Example
```
// attribute/restSearch query that gets every `ip-src` attributes being tagged with tlp or phishing,
// not being expired,
// with a overriden model threshold of 30 for the two models with id 84 and 12.
{
"type": "ip-src",
"tags": ["tlp:%","phishing:%"],
"includeDecayScore": 1,
"excludeDecayed": 1,
"modelOverrides": {
"threshold": 30
}
"decayingModel": [84, 12],
}
```
## Default and Custom Models
In MISP, Some *Decaying Models* called **Default Models** will be supplied by default. Similarly to *Taxonomies*, *Galaxies* or *misp-objects*, *Decaying Models* will have their [own repository](https://github.com/MISP/misp-decaying-models) and will have the possibility to be updated directly from the UI via a single click. **Default Models** are available to everyone, meaning that they can been viewed and customized by any users having a presence on the MISP instance.
**Custom Models** are user-defined models that are shared to other users. However, if desired, they can be hidden by turning off the sharing flag, similarly to the *Tag Collection* feature.
## Decaying Fine Tuning Tool: Setting parameters and mapping model to *Attribute* types
When creating a new *Decaying Model*, setting a parameters and viewing its impact should be as easy and straighforward as possible. To do so, few widgets are shipped with the latest version of MISP.
### Customizing lifetime and decay speed parameters
### Setting the ``base_score``: Customizing Taxonomies' weigth
### Viewing scores and Simulating the model
# Developer perspective: Creating a model using a different algorithm
The Built-in Polynomial *Decaying Model* implemented in MISP allows any user to customize various components to achieve fine-grained decay behaviors. Still, it is possible that our model doesn't encompass your specific use-case. Thanks to the implemented architecture, any other formulas or algorithms can be added and used in a straightforward way.
Steps to create a new decay algorithm:
- Create a new file ``$filename`` in ``app/Model/DecayingModelsFormulas/``
- Extend the **Base** class ``DecayingModelBase``
- Implement the two functions ``computeScore`` and ``isDecayed`` with you own formula/algorithm
- Create a *Model* and set the ``formula`` field to ``$filename``
```
```
# Outcomes
Evaluating **quality** and **freshness** of IOCs is a problem commonly found in Threat Intelligence Platforms. We tried to solve it using a simple yet customizable system.
Upon release, MISP will be shipped with few models that could fit most use-cases. Still, we are eagerly waiting for contributions, fine-tunings or feedbacks from users. This would opens up plenty of opportunities includings improved *Models*' precision, parameters tweaking or even integration of machine learning as a new *Model* algorithm.
Furthermore, we are not done yet! There are already improvements cooking in the MISP-Project oven,
- Integration of ``False Positive`` and ``Expiration`` *Sightings*
- Formula tweakings to provide better control on how to reset the ``base_score`` once a *Sighting* is created
- Per-user Taxonomies' ``numerical_value`` overrides
- Weights on *Tag*'s predicate level