AllDataPreprocessing.ipynb

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      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "cBT-CXtLLBza"
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      "source": [
        "import torch\n",
        "import scipy\n",
        "import sklearn\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "from sklearn import preprocessing\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('data.csv', sep=',')\n",
        "df = df.truncate(after=75000)\n",
        "df"
      ],
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          "height": 443
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        "outputId": "9a29b93a-5c82-4cea-8bc3-ae998a442640"
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      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                    hash  millisecond  \\\n",
              "0      42fb5e2ec009a05ff5143227297074f1e9c6c3ebb9c914...            0   \n",
              "1      42fb5e2ec009a05ff5143227297074f1e9c6c3ebb9c914...            1   \n",
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              "4      42fb5e2ec009a05ff5143227297074f1e9c6c3ebb9c914...            4   \n",
              "...                                                  ...          ...   \n",
              "74996  6e222eabc2ef5f8077c379f3b4da0e77bd0bca71b7e032...          996   \n",
              "74997  6e222eabc2ef5f8077c379f3b4da0e77bd0bca71b7e032...          997   \n",
              "74998  6e222eabc2ef5f8077c379f3b4da0e77bd0bca71b7e032...          998   \n",
              "74999  6e222eabc2ef5f8077c379f3b4da0e77bd0bca71b7e032...          999   \n",
              "75000  0602834d897fe3f3314586ae867aed63f3757be01b7f03...            0   \n",
              "\n",
              "      classification  state  usage_counter        prio  static_prio  \\\n",
              "0            malware      0              0  3069378560        14274   \n",
              "1            malware      0              0  3069378560        14274   \n",
              "2            malware      0              0  3069378560        14274   \n",
              "3            malware      0              0  3069378560        14274   \n",
              "4            malware      0              0  3069378560        14274   \n",
              "...              ...    ...            ...         ...          ...   \n",
              "74996        malware      0              0  3069657088        14747   \n",
              "74997        malware      0              0  3069657088        14747   \n",
              "74998        malware      0              0  3069657088        14747   \n",
              "74999        malware      0              0  3069657088        14747   \n",
              "75000        malware      0              0  3069870080        14119   \n",
              "\n",
              "       normal_prio  policy  vm_pgoff  ...  nivcsw  min_flt  maj_flt  \\\n",
              "0                0       0         0  ...       0        0      120   \n",
              "1                0       0         0  ...       0        0      120   \n",
              "2                0       0         0  ...       0        0      120   \n",
              "3                0       0         0  ...       0        0      120   \n",
              "4                0       0         0  ...       0        0      120   \n",
              "...            ...     ...       ...  ...     ...      ...      ...   \n",
              "74996            0       0         0  ...      69        0      120   \n",
              "74997            0       0         0  ...      69        0      120   \n",
              "74998            0       0         0  ...      69        2      120   \n",
              "74999            0       0         0  ...      69        0      120   \n",
              "75000            0       0         0  ...      96        1      120   \n",
              "\n",
              "       fs_excl_counter        lock   utime  stime  gtime  cgtime  signal_nvcsw  \n",
              "0                    0  3204448256  380690      4      0       0             0  \n",
              "1                    0  3204448256  380690      4      0       0             0  \n",
              "2                    0  3204448256  380690      4      0       0             0  \n",
              "3                    0  3204448256  380690      4      0       0             0  \n",
              "4                    0  3204448256  380690      4      0       0             0  \n",
              "...                ...         ...     ...    ...    ...     ...           ...  \n",
              "74996                3  3204448256  379561      4      1       0             0  \n",
              "74997                3  3204448256  379561      4      1       0             0  \n",
              "74998                3  3204448256  379561      4      1       0             0  \n",
              "74999                3  3204448256  379561      4      1       0             0  \n",
              "75000                2  3204448256  377574      4      3       0             0  \n",
              "\n",
              "[75001 rows x 35 columns]"
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              "      <td>2</td>\n",
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              "\n",
              "<div id=\"df-41ab164b-2d66-453f-b5cd-7d4e98a4598d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-41ab164b-2d66-453f-b5cd-7d4e98a4598d')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-41ab164b-2d66-453f-b5cd-7d4e98a4598d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "original_headers = list(df.columns.values)\n",
        "\n",
        "df = df._get_numeric_data()\n",
        "\n",
        "numeric_headers = list(df.columns.values)\n",
        "\n",
        "\n",
        "numpy_array = df.to_numpy()\n",
        "\n",
        "numeric_headers.reverse()\n",
        "reverse_df = df[numeric_headers]\n",
        "\n",
        "x = df.drop('utime', axis=1)\n",
        "y = df['utime']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    x, y, test_size=0.05, random_state=101)\n",
        "\n",
        "model = KNeighborsClassifier(n_neighbors=5)\n",
        "model.fit(X_train,y_train)\n",
        "\n",
        "predictions = model.predict(X_test)\n",
        "\n",
        "\n",
        "acc = accuracy_score(y_test, predictions)\n",
        "print('Accuracy: %.3f' % acc)\n"
      ],
      "metadata": {
        "id": "EYjPknLMLFsj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd81cfb2-bf87-4a10-f274-21efca21a190"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.894\n"
          ]
        }
      ]
    }
  ]
}