{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "YbyU8YKP5KOh"
      },
      "outputs": [],
      "source": [
        "# Capture to supress the download ouput\n",
        "%%capture\n",
        "!pip install datasets evaluate transformers;\n",
        "!pip install huggingface_hub;\n",
        "!pip install pandas;"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hzlMD2hyVrtD",
        "outputId": "53ad9ba2-a64b-4bd8-eeca-4449035b0595"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# Load dataset with google drive\n",
        "# We downloaded the dataset from kaggle and uploaded it to google drive, then used google colab to load\n",
        "# It is possible to download it directly using the kaggle api\n",
        "\n",
        "# Link to dataset: https://www.kaggle.com/datasets/engraqeel/iot23preprocesseddata?resource=download\n",
        "# Link to kaggle api docs: https://www.kaggle.com/docs/api#interacting-with-datasets\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "reduced_iot_path =  \"/content/drive/MyDrive/PATH/TO/FILE/iot23_combined_new.csv\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "9fLFeygkITnn"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Define Features\n",
        "# ts\tuid\tid.orig_h\tid.orig_p\tid.resp_h\tid.resp_p\tproto\tservice\tduration\torig_bytes\tresp_bytes\tconn_state\tlocal_orig\tlocal_resp\tmissed_bytes\thistory\torig_pkts\torig_ip_bytes\tresp_pkts\tresp_ip_bytes\tlabel\n",
        "# https://docs.zeek.org/en/master/scripts/base/protocols/conn/main.zeek.html#type-Conn::Info\n",
        "\n",
        "pandas_features = {\n",
        "    'id.orig_p': int,\n",
        "    'id.resp_p': int,\n",
        "    'proto': str,\n",
        "    'service': str,\n",
        "    'duration': float,\n",
        "    'orig_bytes': pd.Int64Dtype(),\n",
        "    'resp_bytes': pd.Int64Dtype(),\n",
        "    'conn_state': str,\n",
        "    'missed_bytes': pd.Int64Dtype(),\n",
        "    'history': str,\n",
        "    'orig_pkts': pd.Int64Dtype(),\n",
        "    'orig_ip_bytes': pd.Int64Dtype(),\n",
        "    'resp_pkts': pd.Int64Dtype(),\n",
        "    'resp_ip_bytes': pd.Int64Dtype(),\n",
        "    'label': str\n",
        "}\n",
        "\n",
        "all_column_names = ['ts', 'uid', 'id.orig_h', 'id.orig_p', 'id.resp_h', 'id.resp_p', 'proto', 'service', 'duration', 'orig_bytes', 'resp_bytes', 'conn_state', 'local_orig', 'local_resp', 'missed_bytes', 'history', 'orig_pkts', 'orig_ip_bytes', 'resp_pkts', 'resp_ip_bytes', 'label'];\n",
        "important_column_names = ['id.resp_p', 'proto', 'conn_state', 'orig_pkts', 'orig_ip_bytes', 'resp_ip_bytes', 'label'];\n",
        "exclude_column_names = ['ts','uid','id.orig_h', 'id.resp_h', 'local_orig', 'local_resp']\n",
        "\n",
        "column_names = [column for column in all_column_names if column not in exclude_column_names]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "Zll2DOeT9yv2"
      },
      "outputs": [],
      "source": [
        "# Load dataset with Pandas\n",
        "from datasets import Dataset\n",
        "import pandas as pd\n",
        "reduced_iot_dataset_pandas = pd.read_csv(reduced_iot_path, usecols=column_names, na_values=['-'], dtype=pandas_features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "SUb01Eg7I7wS"
      },
      "outputs": [],
      "source": [
        "# Remove Duplicates\n",
        "reduced_iot_dataset_pandas = reduced_iot_dataset_pandas.drop_duplicates()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make label Benign / Malicious\n",
        "reduced_iot_dataset_pandas['label'] = reduced_iot_dataset_pandas['label'].apply(lambda x: \"Benign\" if x == \"Benign\" else \"Malicious\")"
      ],
      "metadata": {
        "id": "M06Rb8fj2fzk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xamjYrWbgxkf",
        "outputId": "1ddb22b6-98ab-44b4-83e1-b995e8c1ea4a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "orig_bytes\n",
              "0       564771\n",
              "<NA>    241092\n",
              "48        2121\n",
              "29        1463\n",
              "45        1348\n",
              "         ...  \n",
              "1088         1\n",
              "1093         1\n",
              "1094         1\n",
              "1104         1\n",
              "770          1\n",
              "Length: 431, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Test distribution of data\n",
        "reduced_iot_dataset_pandas.value_counts('orig_bytes', dropna=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Js3KG0xNvwpf"
      },
      "outputs": [],
      "source": [
        "# Final step: convert to hugging face dataset\n",
        "reduced_iot_dataset = Dataset.from_pandas(reduced_iot_dataset_pandas).remove_columns(\"__index_level_0__\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test distribution of data again\n",
        "reduced_iot_dataset.to_pandas().value_counts('orig_bytes', dropna=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pOxVs7H-0-3k",
        "outputId": "dcd084dd-3369-4d8e-91ca-c9fbfc1636f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "orig_bytes\n",
              "0.0       564771\n",
              "NaN       241092\n",
              "48.0        2121\n",
              "29.0        1463\n",
              "45.0        1348\n",
              "           ...  \n",
              "1088.0         1\n",
              "1093.0         1\n",
              "1094.0         1\n",
              "1104.0         1\n",
              "770.0          1\n",
              "Length: 431, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Authenticate hugging face\n",
        "!huggingface-cli login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pq6DF3Z8wdmF",
        "outputId": "aaf5e476-96ce-4edc-d65c-858a7e4e52ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "    \n",
            "    A token is already saved on your machine. Run `huggingface-cli whoami` to get more information or `huggingface-cli logout` if you want to log out.\n",
            "    Setting a new token will erase the existing one.\n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Token: \n",
            "Add token as git credential? (Y/n) Y\n",
            "Token is valid (permission: write).\n",
            "Your token has been saved in your configured git credential helpers (store).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Push to the hugging face hub\n",
        "reduced_iot_dataset.push_to_hub(\"19kmunz/iot-23-preprocessed-allcolumns\")"
      ],
      "metadata": {
        "id": "Nz5RwnjxwnwY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Test loading the data set\n",
        "from datasets import load_dataset\n",
        "pulledDataSet= load_dataset(\"19kmunz/iot-23-preprocessed\", download_mode=\"force_redownload\")"
      ],
      "metadata": {
        "id": "6rMEA58Pzlyx"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}