Transactions Dataset/Elliptic++_Transactions_Case_Analysis.ipynb

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      "cell_type": "markdown",
      "source": [
        "# **Elliptic++ Transactions Dataset**\n",
        "\n",
        "\n",
        "---\n",
        "---\n",
        "\n",
        "\n",
        "Released by: Youssef Elmougy, Ling Liu\n",
        "\n",
        "\n",
        "\n",
        "School of Computer Science, Georgia Institute of Technology\n",
        "\n",
        "Contact: yelmougy3@gatech.edu\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "Github Repository: [https://www.github.com/git-disl/EllipticPlusPlus](https://www.github.com/git-disl/EllipticPlusPlus)\n",
        "\n",
        "\n",
        "If you use our dataset in your work, please cite our paper:\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        ">> Youssef Elmougy and Ling Liu. 2023. Demystifying Fraudulent Transactions and Illicit Nodes in the Bitcoin Network for Financial Forensics.\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "O34u-DVsX4jx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## [SETUP] Import libraries and csv files "
      ],
      "metadata": {
        "id": "ReHrhaPiaiI-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Download dataset from: [https://www.github.com/git-disl/EllipticPlusPlus](https://www.github.com/git-disl/EllipticPlusPlus)"
      ],
      "metadata": {
        "id": "TLi0Zc7j6Rb6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eUbJT_J-A1Mw",
        "outputId": "6e64f92e-3bac-4dbc-b4f2-d57489140473"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "!cp drive/My\\ Drive/Elliptic++\\ Dataset/txs_features.csv ./\n",
        "!cp drive/My\\ Drive/Elliptic++\\ Dataset/txs_classes.csv ./\n",
        "!cp drive/My\\ Drive/Elliptic++\\ Dataset/txs_edgelist.csv ./"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kz7WtWhG6MtI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba02acc2-6f29-4d71-c5df-2d6bc5c61453"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import networkx as nx\n",
        "import plotly.graph_objs as go \n",
        "import plotly.offline as py \n",
        "import math\n",
        "\n",
        "!pip install -U ipython \n",
        "from IPython.core.interactiveshell import InteractiveShell\n",
        "InteractiveShell.ast_node_interactivity = 'all'"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import precision_recall_fscore_support\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.metrics import f1_score, accuracy_score, confusion_matrix\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "from sklearn.base import clone \n",
        "\n",
        "import xgboost as xgb"
      ],
      "metadata": {
        "id": "TKJFAkVLp34j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install eli5\n",
        "import eli5\n",
        "from eli5.sklearn import PermutationImportance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bRW1hh3S4pbS",
        "outputId": "af64ff37-eb12-4dd8-faba-628b9b695aec"
      },
      "execution_count": null,
      "outputs": [
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Building wheels for collected packages: eli5\n",
            "  Building wheel for eli5 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for eli5: filename=eli5-0.13.0-py2.py3-none-any.whl size=107748 sha256=138c7b7afc731dc3a39e6bd4e82b7a9fa2f965be699cbe7c19820bc09cf8bfa4\n",
            "  Stored in directory: /root/.cache/pip/wheels/85/ac/25/ffcd87ef8f9b1eec324fdf339359be71f22612459d8c75d89c\n",
            "Successfully built eli5\n",
            "Installing collected packages: jinja2, eli5\n",
            "  Attempting uninstall: jinja2\n",
            "    Found existing installation: Jinja2 2.11.3\n",
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            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "\u001b[0mSuccessfully installed eli5-0.13.0 jinja2-3.1.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Transactions Dataset Overview\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "This section loads the 3 csv files (txs_features, txs_classes, txs_edgelist) and provides a quick overview of the dataset structure and features."
      ],
      "metadata": {
        "id": "y3JLmL3SfJqP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load saved transactions dataset csv files:"
      ],
      "metadata": {
        "id": "ZcdjXmV8gr8S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nTransaction features: \\n\")\n",
        "df_txs_features = pd.read_csv(\"txs_features.csv\")\n",
        "df_txs_features\n",
        "\n",
        "print(\"\\nTransaction classes: \\n\")\n",
        "df_txs_classes = pd.read_csv(\"txs_classes.csv\")\n",
        "df_txs_classes\n",
        "\n",
        "print(\"\\nTransaction-Transaction edgelist: \\n\")\n",
        "df_txs_edgelist = pd.read_csv(\"txs_edgelist.csv\")\n",
        "df_txs_edgelist"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "dNNEwGmae2Eo",
        "outputId": "3ed76d40-095b-42dc-8362-5eb54958b88a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Transaction features: \n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             txId  Time step  Local_feature_1  Local_feature_2  \\\n",
              "0            3321          1        -0.169615        -0.184668   \n",
              "1           11108          1        -0.137586        -0.184668   \n",
              "2           51816          1        -0.170103        -0.184668   \n",
              "3           68869          1        -0.114267        -0.184668   \n",
              "4           89273          1         5.202107        -0.210553   \n",
              "...           ...        ...              ...              ...   \n",
              "203764  158304003         49        -0.165622        -0.139563   \n",
              "203765  158303998         49        -0.167040        -0.139563   \n",
              "203766  158303966         49        -0.167040        -0.139563   \n",
              "203767  161526077         49        -0.172212        -0.139573   \n",
              "203768  194103537         49        -0.172212        -0.139573   \n",
              "\n",
              "        Local_feature_3  Local_feature_4  Local_feature_5  Local_feature_6  \\\n",
              "0             -1.201369        -0.121970        -0.043875        -0.113002   \n",
              "1             -1.201369        -0.121970        -0.043875        -0.113002   \n",
              "2             -1.201369        -0.121970        -0.043875        -0.113002   \n",
              "3             -1.201369         0.028105        -0.043875        -0.113002   \n",
              "4             -1.756361        -0.121970       260.090707        -0.113002   \n",
              "...                 ...              ...              ...              ...   \n",
              "203764         1.018602        -0.121970        -0.043875        -0.113002   \n",
              "203765         1.018602        -0.121970        -0.043875        -0.113002   \n",
              "203766         1.018602        -0.121970        -0.043875        -0.113002   \n",
              "203767         1.018602        -0.121970        -0.043875        -0.113002   \n",
              "203768         1.018602        -0.121970        -0.043875        -0.113002   \n",
              "\n",
              "        Local_feature_7  Local_feature_8  ...  in_BTC_min  in_BTC_max  \\\n",
              "0             -0.061584        -0.160199  ...    0.534072    0.534072   \n",
              "1             -0.061584        -0.127429  ...    5.611878    5.611878   \n",
              "2             -0.061584        -0.160699  ...    0.456608    0.456608   \n",
              "3              0.547008        -0.161652  ...    0.308900    8.000000   \n",
              "4             -0.061584         5.335864  ...  852.164680  852.164680   \n",
              "...                 ...              ...  ...         ...         ...   \n",
              "203764        -0.061584        -0.156113  ...         NaN         NaN   \n",
              "203765        -0.061584        -0.157564  ...         NaN         NaN   \n",
              "203766        -0.061584        -0.157564  ...         NaN         NaN   \n",
              "203767        -0.061584        -0.162856  ...         NaN         NaN   \n",
              "203768        -0.061584        -0.162856  ...         NaN         NaN   \n",
              "\n",
              "        in_BTC_mean  in_BTC_median  in_BTC_total   out_BTC_min  out_BTC_max  \\\n",
              "0          0.534072       0.534072      0.534072  1.668990e-01     0.367074   \n",
              "1          5.611878       5.611878      5.611878  5.861940e-01     5.025584   \n",
              "2          0.456608       0.456608      0.456608  2.279902e-01     0.228518   \n",
              "3          3.102967       1.000000      9.308900  1.229000e+00     8.079800   \n",
              "4        852.164680     852.164680    852.164680  1.300000e-07    41.264036   \n",
              "...             ...            ...           ...           ...          ...   \n",
              "203764          NaN            NaN           NaN           NaN          NaN   \n",
              "203765          NaN            NaN           NaN           NaN          NaN   \n",
              "203766          NaN            NaN           NaN           NaN          NaN   \n",
              "203767          NaN            NaN           NaN           NaN          NaN   \n",
              "203768          NaN            NaN           NaN           NaN          NaN   \n",
              "\n",
              "        out_BTC_mean  out_BTC_median  out_BTC_total  \n",
              "0           0.266986        0.266986       0.533972  \n",
              "1           2.805889        2.805889       5.611778  \n",
              "2           0.228254        0.228254       0.456508  \n",
              "3           4.654400        4.654400       9.308800  \n",
              "4           0.065016        0.000441     852.164680  \n",
              "...              ...             ...            ...  \n",
              "203764           NaN             NaN            NaN  \n",
              "203765           NaN             NaN            NaN  \n",
              "203766           NaN             NaN            NaN  \n",
              "203767           NaN             NaN            NaN  \n",
              "203768           NaN             NaN            NaN  \n",
              "\n",
              "[203769 rows x 184 columns]"
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              "      <th></th>\n",
              "      <th>txId</th>\n",
              "      <th>Time step</th>\n",
              "      <th>Local_feature_1</th>\n",
              "      <th>Local_feature_2</th>\n",
              "      <th>Local_feature_3</th>\n",
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              "      <th>Local_feature_8</th>\n",
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              "      <th>out_BTC_max</th>\n",
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              "      <td>-0.160199</td>\n",
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              "      <td>1.668990e-01</td>\n",
              "      <td>0.367074</td>\n",
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              "      <td>11108</td>\n",
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              "      <td>5.861940e-01</td>\n",
              "      <td>5.025584</td>\n",
              "      <td>2.805889</td>\n",
              "      <td>2.805889</td>\n",
              "      <td>5.611778</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>51816</td>\n",
              "      <td>1</td>\n",
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              "      <td>-0.184668</td>\n",
              "      <td>-1.201369</td>\n",
              "      <td>-0.121970</td>\n",
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              "      <td>-0.113002</td>\n",
              "      <td>-0.061584</td>\n",
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              "      <td>...</td>\n",
              "      <td>0.456608</td>\n",
              "      <td>0.456608</td>\n",
              "      <td>0.456608</td>\n",
              "      <td>0.456608</td>\n",
              "      <td>0.456608</td>\n",
              "      <td>2.279902e-01</td>\n",
              "      <td>0.228518</td>\n",
              "      <td>0.228254</td>\n",
              "      <td>0.228254</td>\n",
              "      <td>0.456508</td>\n",
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              "      <td>-0.114267</td>\n",
              "      <td>-0.184668</td>\n",
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              "      <td>0.547008</td>\n",
              "      <td>-0.161652</td>\n",
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              "      <td>0.308900</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>3.102967</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9.308900</td>\n",
              "      <td>1.229000e+00</td>\n",
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              "      <td>4.654400</td>\n",
              "      <td>4.654400</td>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data structure for an example transaction (txId = 272145560):"
      ],
      "metadata": {
        "id": "5Qw43a6xe9rN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\ntxs_features.csv for txId = 272145560\\n\")\n",
        "df_txs_features[df_txs_features['txId']==272145560]\n",
        "\n",
        "print(\"\\ntxs_classes.csv for txId = 272145560\\n\")\n",
        "df_txs_classes[df_txs_classes['txId']==272145560]\n",
        "\n",
        "print(\"\\ntxs_edgelist.csv for txId = 272145560\\n\")\n",
        "df_txs_edgelist[(df_txs_edgelist['txId1']==272145560) | (df_txs_edgelist['txId2']==272145560)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "BHp9b7S1e1-F",
        "outputId": "757d6c2d-3c51-49f6-d0eb-2d701b52fc78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "txs_features.csv for txId=272145560\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             txId  Time step  Local_feature_1  Local_feature_2  \\\n",
              "105573  272145560         24        -0.155493        -0.107012   \n",
              "\n",
              "        Local_feature_3  Local_feature_4  Local_feature_5  Local_feature_6  \\\n",
              "105573        -1.201369         -0.12197        -0.043875        -0.113002   \n",
              "\n",
              "        Local_feature_7  Local_feature_8  ...  in_BTC_min  in_BTC_max  \\\n",
              "105573        -0.061584        -0.145749  ...      2.7732      2.7732   \n",
              "\n",
              "        in_BTC_mean  in_BTC_median  in_BTC_total  out_BTC_min  out_BTC_max  \\\n",
              "105573       2.7732         2.7732        2.7732     0.001917     2.770883   \n",
              "\n",
              "        out_BTC_mean  out_BTC_median  out_BTC_total  \n",
              "105573        1.3864          1.3864         2.7728  \n",
              "\n",
              "[1 rows x 184 columns]"
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            "\n",
            "txs_classes.csv for txId=272145560\n",
            "\n"
          ]
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              "             txId  class\n",
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              "\n",
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              "\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "txs_edgelist.csv for txId=272145560\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            txId1      txId2\n",
              "123072  272145560  296926618\n",
              "123272  272145560  272145556\n",
              "125873  299475624  272145560"
            ],
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              "\n",
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              "<style scoped>\n",
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              "        vertical-align: middle;\n",
              "    }\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>txId1</th>\n",
              "      <th>txId2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "      <th>125873</th>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Transaction features --- 94 local features, 72 aggregate features, 17 augmented features:\n"
      ],
      "metadata": {
        "id": "moS6bxoLg1Pk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "list(df_txs_features.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RpljxgT7k49T",
        "outputId": "916b4dda-11d6-4f92-f10d-3d7040f10ea8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['txId',\n",
              " 'Time step',\n",
              " 'class',\n",
              " 'Local_feature_1',\n",
              " 'Local_feature_2',\n",
              " 'Local_feature_3',\n",
              " 'Local_feature_4',\n",
              " 'Local_feature_5',\n",
              " 'Local_feature_6',\n",
              " 'Local_feature_7',\n",
              " 'Local_feature_8',\n",
              " 'Local_feature_9',\n",
              " 'Local_feature_10',\n",
              " 'Local_feature_11',\n",
              " 'Local_feature_12',\n",
              " 'Local_feature_13',\n",
              " 'Local_feature_14',\n",
              " 'Local_feature_15',\n",
              " 'Local_feature_16',\n",
              " 'Local_feature_17',\n",
              " 'Local_feature_18',\n",
              " 'Local_feature_19',\n",
              " 'Local_feature_20',\n",
              " 'Local_feature_21',\n",
              " 'Local_feature_22',\n",
              " 'Local_feature_23',\n",
              " 'Local_feature_24',\n",
              " 'Local_feature_25',\n",
              " 'Local_feature_26',\n",
              " 'Local_feature_27',\n",
              " 'Local_feature_28',\n",
              " 'Local_feature_29',\n",
              " 'Local_feature_30',\n",
              " 'Local_feature_31',\n",
              " 'Local_feature_32',\n",
              " 'Local_feature_33',\n",
              " 'Local_feature_34',\n",
              " 'Local_feature_35',\n",
              " 'Local_feature_36',\n",
              " 'Local_feature_37',\n",
              " 'Local_feature_38',\n",
              " 'Local_feature_39',\n",
              " 'Local_feature_40',\n",
              " 'Local_feature_41',\n",
              " 'Local_feature_42',\n",
              " 'Local_feature_43',\n",
              " 'Local_feature_44',\n",
              " 'Local_feature_45',\n",
              " 'Local_feature_46',\n",
              " 'Local_feature_47',\n",
              " 'Local_feature_48',\n",
              " 'Local_feature_49',\n",
              " 'Local_feature_50',\n",
              " 'Local_feature_51',\n",
              " 'Local_feature_52',\n",
              " 'Local_feature_53',\n",
              " 'Local_feature_54',\n",
              " 'Local_feature_55',\n",
              " 'Local_feature_56',\n",
              " 'Local_feature_57',\n",
              " 'Local_feature_58',\n",
              " 'Local_feature_59',\n",
              " 'Local_feature_60',\n",
              " 'Local_feature_61',\n",
              " 'Local_feature_62',\n",
              " 'Local_feature_63',\n",
              " 'Local_feature_64',\n",
              " 'Local_feature_65',\n",
              " 'Local_feature_66',\n",
              " 'Local_feature_67',\n",
              " 'Local_feature_68',\n",
              " 'Local_feature_69',\n",
              " 'Local_feature_70',\n",
              " 'Local_feature_71',\n",
              " 'Local_feature_72',\n",
              " 'Local_feature_73',\n",
              " 'Local_feature_74',\n",
              " 'Local_feature_75',\n",
              " 'Local_feature_76',\n",
              " 'Local_feature_77',\n",
              " 'Local_feature_78',\n",
              " 'Local_feature_79',\n",
              " 'Local_feature_80',\n",
              " 'Local_feature_81',\n",
              " 'Local_feature_82',\n",
              " 'Local_feature_83',\n",
              " 'Local_feature_84',\n",
              " 'Local_feature_85',\n",
              " 'Local_feature_86',\n",
              " 'Local_feature_87',\n",
              " 'Local_feature_88',\n",
              " 'Local_feature_89',\n",
              " 'Local_feature_90',\n",
              " 'Local_feature_91',\n",
              " 'Local_feature_92',\n",
              " 'Local_feature_93',\n",
              " 'Aggregate_feature_1',\n",
              " 'Aggregate_feature_2',\n",
              " 'Aggregate_feature_3',\n",
              " 'Aggregate_feature_4',\n",
              " 'Aggregate_feature_5',\n",
              " 'Aggregate_feature_6',\n",
              " 'Aggregate_feature_7',\n",
              " 'Aggregate_feature_8',\n",
              " 'Aggregate_feature_9',\n",
              " 'Aggregate_feature_10',\n",
              " 'Aggregate_feature_11',\n",
              " 'Aggregate_feature_12',\n",
              " 'Aggregate_feature_13',\n",
              " 'Aggregate_feature_14',\n",
              " 'Aggregate_feature_15',\n",
              " 'Aggregate_feature_16',\n",
              " 'Aggregate_feature_17',\n",
              " 'Aggregate_feature_18',\n",
              " 'Aggregate_feature_19',\n",
              " 'Aggregate_feature_20',\n",
              " 'Aggregate_feature_21',\n",
              " 'Aggregate_feature_22',\n",
              " 'Aggregate_feature_23',\n",
              " 'Aggregate_feature_24',\n",
              " 'Aggregate_feature_25',\n",
              " 'Aggregate_feature_26',\n",
              " 'Aggregate_feature_27',\n",
              " 'Aggregate_feature_28',\n",
              " 'Aggregate_feature_29',\n",
              " 'Aggregate_feature_30',\n",
              " 'Aggregate_feature_31',\n",
              " 'Aggregate_feature_32',\n",
              " 'Aggregate_feature_33',\n",
              " 'Aggregate_feature_34',\n",
              " 'Aggregate_feature_35',\n",
              " 'Aggregate_feature_36',\n",
              " 'Aggregate_feature_37',\n",
              " 'Aggregate_feature_38',\n",
              " 'Aggregate_feature_39',\n",
              " 'Aggregate_feature_40',\n",
              " 'Aggregate_feature_41',\n",
              " 'Aggregate_feature_42',\n",
              " 'Aggregate_feature_43',\n",
              " 'Aggregate_feature_44',\n",
              " 'Aggregate_feature_45',\n",
              " 'Aggregate_feature_46',\n",
              " 'Aggregate_feature_47',\n",
              " 'Aggregate_feature_48',\n",
              " 'Aggregate_feature_49',\n",
              " 'Aggregate_feature_50',\n",
              " 'Aggregate_feature_51',\n",
              " 'Aggregate_feature_52',\n",
              " 'Aggregate_feature_53',\n",
              " 'Aggregate_feature_54',\n",
              " 'Aggregate_feature_55',\n",
              " 'Aggregate_feature_56',\n",
              " 'Aggregate_feature_57',\n",
              " 'Aggregate_feature_58',\n",
              " 'Aggregate_feature_59',\n",
              " 'Aggregate_feature_60',\n",
              " 'Aggregate_feature_61',\n",
              " 'Aggregate_feature_62',\n",
              " 'Aggregate_feature_63',\n",
              " 'Aggregate_feature_64',\n",
              " 'Aggregate_feature_65',\n",
              " 'Aggregate_feature_66',\n",
              " 'Aggregate_feature_67',\n",
              " 'Aggregate_feature_68',\n",
              " 'Aggregate_feature_69',\n",
              " 'Aggregate_feature_70',\n",
              " 'Aggregate_feature_71',\n",
              " 'Aggregate_feature_72',\n",
              " 'in_txs_degree',\n",
              " 'out_txs_degree',\n",
              " 'total_BTC',\n",
              " 'fees',\n",
              " 'size',\n",
              " 'num_input_addresses',\n",
              " 'num_output_addresses',\n",
              " 'in_BTC_min',\n",
              " 'in_BTC_max',\n",
              " 'in_BTC_mean',\n",
              " 'in_BTC_median',\n",
              " 'in_BTC_total',\n",
              " 'out_BTC_min',\n",
              " 'out_BTC_max',\n",
              " 'out_BTC_mean',\n",
              " 'out_BTC_median',\n",
              " 'out_BTC_total']"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1-J38vk9fKfg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## EASY, HARD, and AVERAGE cases Analysis\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "This section analyzes EASY, HARD, and AVERAGE cases (only refers to illicit transactions):\n",
        "\n",
        "\n",
        "1.   **EASY** cases: all models classify an illicit transaction correctly\n",
        "2.   **HARD** cases: all models classify an illicit transaction incorrectly\n",
        "3.   **AVERAGE** cases: some models failed to classify an illicit transaction but  ≥1 models classified correctly\n"
      ],
      "metadata": {
        "id": "3aHWohviwlLm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Correct base values in the test set ('0': licit, '1': illicit):"
      ],
      "metadata": {
        "id": "C1lxkHAM0z49"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FjFx4k3bVgi9",
        "outputId": "12fae872-c9a5-487c-dae7-8cf2460aca0f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    15263\n",
              "1     1083\n",
              "Name: class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "y_test.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ba4QfxncelQr",
        "outputId": "add39cde-e691-45fb-ee67-4e7f711955ec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of EASY CASES: \n",
            "49\n"
          ]
        }
      ],
      "source": [
        "# EASY CASES: fraud transactions that all 5 models identified correctly\n",
        "indices_easy_fraud = [i for i in range(len(y_test.values)) if ((y_test.values[i] == 1) and (y_test.values[i] == y_preds_LR[i]) and (y_test.values[i] == y_preds_RF[i]) and (y_test.values[i] == y_preds_MLP[i]) and (y_test.values[i] == y_preds_LSTM[i]) and (y_test.values[i] == y_preds_XGB[i]))]\n",
        "print(\"Number of EASY CASES: \")\n",
        "print(len(indices_easy_fraud))\n",
        "wrong_predictions_easy = X_testing_timesteps.iloc[indices_easy_fraud,:]\n",
        "wrong_predictions_easy2 = wrong_predictions_easy.drop(columns=wrong_predictions_easy.columns.difference(['txId', 'class', 'Time step']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z6qXWtkTiN-2",
        "outputId": "71ffc581-b97b-4883-bd76-aa4454576587"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of EASY CASES in each time step:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "35    32\n",
              "37     2\n",
              "38     5\n",
              "39     5\n",
              "40     1\n",
              "41     1\n",
              "42     3\n",
              "Name: Time step, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "# EASY CASES: number in each time step\n",
        "print(\"Number of EASY CASES in each time step:\")\n",
        "wrong_predictions_easy2['Time step'].value_counts().sort_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nQ1MrfN5elQs",
        "outputId": "da0a5711-5b92-4c55-a22d-96d38da27134"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of HARD CASES: \n",
            "243\n"
          ]
        }
      ],
      "source": [
        "# HARD CASES: fraud transactions that all 5 models failed to identify\n",
        "indices_hard_fraud = [i for i in range(len(y_test.values)) if ((y_test.values[i] == 1) and (y_test.values[i] != y_preds_LR[i]) and (y_test.values[i] != y_preds_RF[i]) and (y_test.values[i] != y_preds_MLP[i]) and (y_test.values[i] != y_preds_LSTM[i]) and (y_test.values[i] != y_preds_XGB[i]))]\n",
        "print(\"Number of HARD CASES: \")\n",
        "print(len(indices_hard_fraud))\n",
        "wrong_predictions_hard = X_testing_timesteps.iloc[indices_hard_fraud,:]\n",
        "wrong_predictions_hard2 = wrong_predictions_hard.drop(columns=wrong_predictions_hard.columns.difference(['txId', 'class', 'Time step']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m3DTyiECi1vh",
        "outputId": "0fe3e496-4a18-4612-baec-4f1467535046"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of HARD CASES in each time step:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "35     4\n",
              "37    10\n",
              "38     7\n",
              "39     4\n",
              "40    28\n",
              "41     6\n",
              "42    36\n",
              "43    22\n",
              "44    20\n",
              "45     4\n",
              "46     1\n",
              "47    21\n",
              "48    27\n",
              "49    53\n",
              "Name: Time step, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "# HARD CASES: number in each time step\n",
        "print(\"Number of HARD CASES in each time step:\")\n",
        "wrong_predictions_hard2['Time step'].value_counts().sort_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2QplDa5-elQt",
        "outputId": "f849d8f0-2b4d-4771-a6f5-e901e8f69443"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of AVERAGE CASES: \n",
            "98\n"
          ]
        }
      ],
      "source": [
        "# AVERAGE CASES: fraud transactions that some models failed but at least 1 of the 5 models succeed\n",
        "indices_avg_fraud = [i for i in range(len(y_test.values)) if ( \n",
        "    #((y_test.values[i] == 1) and (y_test.values[i] == y_preds_LR[i]) and (y_test.values[i] != y_preds_RF[i]) and (y_test.values[i] != y_preds_MLP[i]) and (y_test.values[i] != y_preds_LSTM[i]) and (y_test.values[i] != y_preds_XGB[i]))# or\n",
        "    ((y_test.values[i] == 1) and (y_test.values[i] == y_preds_LR[i]) and (y_test.values[i] == y_preds_RF[i]) and (y_test.values[i] != y_preds_MLP[i]) and (y_test.values[i] != y_preds_LSTM[i]) and (y_test.values[i] == y_preds_XGB[i]))# or\n",
        "    #((y_test.values[i] == 1) and (y_test.values[i] == y_preds_LR[i]) and (y_test.values[i] == y_preds_RF[i]) and (y_test.values[i] != y_preds_MLP[i])) or\n",
        "    #((y_test.values[i] == 1) and (y_test.values[i] == y_preds_LR[i]) and (y_test.values[i] != y_preds_RF[i]) and (y_test.values[i] == y_preds_MLP[i])) or\n",
        "    #((y_test.values[i] == 1) and (y_test.values[i] != y_preds_LR[i]) and (y_test.values[i] == y_preds_RF[i]) and (y_test.values[i] != y_preds_MLP[i])) or\n",
        "    #((y_test.values[i] == 1) and (y_test.values[i] != y_preds_LR[i]) and (y_test.values[i] == y_preds_RF[i]) and (y_test.values[i] == y_preds_MLP[i])) or\n",
        "    #((y_test.values[i] == 1) and (y_test.values[i] != y_preds_LR[i]) and (y_test.values[i] != y_preds_RF[i]) and (y_test.values[i] == y_preds_MLP[i]))\n",
        "    )]\n",
        "print(\"Number of AVERAGE CASES: \")\n",
        "print(len(indices_avg_fraud))\n",
        "wrong_predictions_avg = X_testing_timesteps.iloc[indices_avg_fraud,:]\n",
        "wrong_predictions_avg2 = wrong_predictions_avg.drop(columns=wrong_predictions_avg.columns.difference(['txId', 'class', 'Time step']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tk20imfZjUF5",
        "outputId": "52f11839-62e8-402a-e3f5-14c3e8ede2ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of AVERAGE CASES in each time step:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "35     6\n",
              "36     1\n",
              "37    10\n",
              "38    27\n",
              "39    18\n",
              "40    10\n",
              "41     5\n",
              "42    21\n",
              "Name: Time step, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ],
      "source": [
        "# AVERAGE CASES: number in each time step\n",
        "print(\"Number of AVERAGE CASES in each time step:\")\n",
        "wrong_predictions_avg2['Time step'].value_counts().sort_index()"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bOk6Ic9Ms7vc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Acknowledgements**\n",
        "\n",
        "\n",
        "---\n",
        "---\n",
        "\n",
        "\n",
        "Released by: Youssef Elmougy, Ling Liu\n",
        "\n",
        "\n",
        "\n",
        "School of Computer Science, Georgia Institute of Technology\n",
        "\n",
        "Contact: yelmougy3@gatech.edu\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "Github Repository: [https://www.github.com/git-disl/EllipticPlusPlus](https://www.github.com/git-disl/EllipticPlusPlus)\n",
        "\n",
        "\n",
        "If you use our dataset in your work, please cite our paper:\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        ">> Youssef Elmougy and Ling Liu. 2023. Demystifying Fraudulent Transactions and Illicit Nodes in the Bitcoin Network for Financial Forensics.\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "BwrFHYfy5hrz"
      }
    }
  ]
}