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Platform for automatic analysis of malicious applications using artificial intelligence algorithms ⚖️

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<div align="center">
    <b><i>Platform for Automatic Analysis of Malicious Applications<br>Using Artificial Intelligence Algorithms</i></b>
</div>

<br>

---

- [Description 🖼️](#description-️)
  - [Objectives 🎯](#objectives-)
  - [Features 🧰](#features-)
- [Important Observation ⚠️](#important-observation-️)
- [Setup 🛠️](#setup-️)
  - [For Private Repositories 🙊](#for-private-repositories-)
- [Typical Usage 🔎](#typical-usage-)
  - [For Clients 👨‍💼](#for-clients-)
  - [For Administrators 👩‍💻](#for-administrators-)
  - [For Other Systems 🖥️](#for-other-systems-️)
- [Resources 🥣](#resources-)

---

# Description 🖼️

***dike*** ( pronounced */ˈdaɪkiː/*) is an open-source platform combining the fields of **malware analysis** with the one of **artificial intelligence**, more precisely the machine learning subfield.

## Objectives 🎯

At the moment, ***dike*** is capable only of analyzing **Portable Executable** and **Object Linking and Embedding** formats. Besides this limitation, it has three main objectives:
1. **Regression of malice**
2. **Classification in malware families**
3. **Similarity analysis**.

## Features 🧰

The software enables the creation of **analysis pipelines** (named in the context of the platform *models*), which deals with the specific steps of the malware analysis and data engineering:
1. **Dataset management**, where it uses three main sources of labeled PE and OLE files:
    - The open-source dataset [DikeDataset](https://github.com/iosifache/DikeDataset)
    - Accurate results of analysis made by the analysts of the organization in which the platform is set up
    - Results of automatic VirusTotal scans
2. **Features extraction**, in which extractors are used to obtain relevant information such as:
    - Strings
    - Characteristics of the file format
    - Opcodes
    - Windows API calls
    - Macros
3. **Features preprocessing**, where preprocessors are used to transform the features into a more friendly format for the machine learning algorithms
    - Transformations
        - Binarization
        - Discretization
        - Counting (and in a special approach, for categories of opcodes and API calls)
        - Vectorization
        - NGrams
    - Scaling
    - Dimensionality reduction
4. **Training of machine learning models** with included cross-validation and evaluation (regression-wise and classification-wise).

# Important Observation ⚠️

***dike*** is part of my Bachelor thesis, which aims at demonstrating that the artificial intelligence techniques can improve the malware analysis. The document and the presentation (in Romanian 🇷🇴 only) can be found in a separate [repository](https://github.com/iosifache/BachelorThesis).

At the moment, this is the only place where some relevant information can be found:
- Software requirements
- Architecture (more detailed than the description above)
- Testing
- Evaluation
- Further development.

# Setup 🛠️

1. Download the script [`manage.sh`](https://github.com/iosifache/dike/blob/main/infrastructure/manage.sh) from the folder `infrastructure`.
2. Obtain a VirusTotal API key.
3. Create and host (on a server which the platform can access) a TGZ archive containing two folders, `ghidra` (with a Ghidra project) and `qiling` (with the dynamically linked libraries needed by Qiling).
4. Run the script and follow the instructions.

<details>
    <summary>Setup Example</summary>
    <a href="https://asciinema.org/a/LnsWrugTTUZDCWxdAD0kKFv8k" target="_blank">
        <img src="https://asciinema.org/a/LnsWrugTTUZDCWxdAD0kKFv8k.svg" alt="Setup Example"/>
    </a>
</details>

## For Private Repositories 🙊

If the repository hosting the platform is private, there are two steps that needs to be performed before:
1. Generate an asymmetric key pair via `ssh-keygen -t ed25519 -C "EMAIL_ADDRESS"`, where `EMAIL_ADDRESS` need to be populated with your email address.
2. Add the public one into the GitHub's deployment key section.

# Typical Usage 🔎

## For Clients 👨‍💼

<details>
    <summary>Malice Prediction</summary>
    <kbd>
        <img src="others/screenshots/malice.png" alt="Malice Prediction"/>
    </kbd>
</details>
<details>
    <summary>Similarity Analysis</summary>
    <kbd>
        <img src="others/screenshots/similarity.png" alt="Similarity Analysis"/>
    </kbd>
</details>
<details>
    <summary>Feature-wise Comparison of Samples</summary>
    <kbd>
        <img src="others/screenshots/comparison.png" alt="Feature-wise Comparison of Samples"/>
    </kbd>
</details>
<details>
    <summary>Model Evaluation</summary>
    <kbd>
        <img src="others/screenshots/evaluation.png" alt="Model Evaluation"/>
    </kbd>
</details>
<details>
    <summary>Settings</summary>
    <kbd>
        <img src="others/screenshots/settings.png" alt="Settings"/>
    </kbd>
</details>

## For Administrators 👩‍💻

A powerful command line interface can be used by the administrators, by running the `dike` command on a leader server. Some available commands are demonstrated in the recording below.

<details>
    <summary>Connections with Subordinate Servers</summary>
    <a href="https://asciinema.org/a/f6k6QUcNMqauB7fayjdIZ43X8" target="_blank">
        <img src="https://asciinema.org/a/f6k6QUcNMqauB7fayjdIZ43X8.svg" alt="Connections with Subordinate Servers"/>
    </a>
</details>
<details>
    <summary>Datasets</summary>
    <a href="https://asciinema.org/a/fMbv7d57Hg0hIbA1sK1wCAcex" target="_blank">
        <img src="https://asciinema.org/a/fMbv7d57Hg0hIbA1sK1wCAcex.svg" alt="Datasets"/>
    </a>
</details>
<details>
    <summary>Training and Management of Models</summary>
    <a href="https://asciinema.org/a/dFlup8OWwSvJVR7li70AZ0NhM" target="_blank">
        <img src="https://asciinema.org/a/dFlup8OWwSvJVR7li70AZ0NhM.svg" alt="Training and Management of Models"/>
    </a>
</details>
<details>
    <summary>Predictions with Models</summary>
    <a href="https://asciinema.org/a/puTJEIuhZOEKn7tSEh8627Zfi" target="_blank">
        <img src="https://asciinema.org/a/puTJEIuhZOEKn7tSEh8627Zfi.svg" alt="Predictions with Models"/>
    </a>
</details>

The administrators use also manual editing of YAML files, respecting a schema depending on the context in which the file is used. Some existing files (one per type, only for exampling purposes) has comments to document these schemas as follows:
- [Platform configuration](data/configuration/configuration.yaml)
- [Dataset creation](others/configuration_files/datasets/small_pe.yaml)
- [Model creation](others/configuration_files/models/small_ole.yaml).

## For Other Systems 🖥️

Other systems of the organization can use the scan services of the platform, creating HTTP or HTTPS (depending on the configuration) requests to the following **API endpoints**.

| Route                                | Action                                                        |
|--------------------------------------|---------------------------------------------------------------|
| `/get_malware_families`              | Retrieves the used malware families.                          |
| `/get_evaluation/MODEL_NAME`         | Retrieves the evaluation of a model.                          |
| `/get_configuration/MODEL_NAME`      | Retrieves the configuration.                                  |
| `/get_features/MODEL_NAME/FILE_HASH` | Retrieves the features of a file from the platform's dataset. |
| `/create_ticket/MODEL_NAME`          | Creates a prediction ticket.                                  |
| `/get_ticket/TICKET_NAME`            | Retrieves the content of a prediction ticket.                 |
| `/publish/MODEL_NAME`                | Publishes for a specific model the results of a scan.         |

# Resources 🥣

The most important used resources are listed in the table below.

| Name             | Description                                             | Link                                                           | 
|------------------|---------------------------------------------------------|----------------------------------------------------------------|
| *Ghidra*         | Software reverse engineering framework                  | [repository](https://github.com/NationalSecurityAgency/ghidra) |
| *VirusTotal API* | Scanning API that aggregates multiple antivirus engines | [website](https://developers.virustotal.com/v3.0/reference)    |
| *Qiling*         | Python 3 emulation framework                            | [repository](https://github.com/qilingframework/qiling)        |
| *Pandas*         | Python 3 data analysis and manipulation library         | [repository](https://github.com/pandas-dev/pandas)             |
| *scikit-learn*   | Python 3 machine learning library                       | [repository](https://github.com/scikit-learn/scikit-learn)     |
| *Python 3*       | General-purpose programming language                    | [website](https://www.python.org)                              |
| *Docker*         | Software product for OS-level virtualization            | [website](https://www.docker.com)                              |
| *Docker Compose* | Tool for running multi-container applications on Docker | [repository](https://github.com/docker/compose)                |
| *GitHub*         | Git repository hosting service                          | [website](https://github.com)                                  |
| *YAML*           | Data-serialization language                             | [website](https://yaml.org)                                    |

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