test.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/4k5h1t/Network-Anomaly-Detection/blob/main/test.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J6LtbKD5_BzJ"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import joblib\n",
        "import sklearn\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import GridSearchCV, train_test_split\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, plot_confusion_matrix\n",
        "from sklearn import metrics\n",
        "from sklearn.utils import class_weight\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.python.keras import layers\n",
        "\n",
        "import tensorflow.python.keras as keras\n",
        "\n",
        "from tensorflow.python.keras import layers\n",
        "from tensorflow.python.keras.layers import Dense\n",
        "\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "RANDOM_STATE_SEED = 12"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "%cd /content/gdrive/My Drive/Kaggle"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "30JMwXA5V6Tz",
        "outputId": "8193d06f-449c-4853-fb56-757b0646b82b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n",
            "/content/gdrive/My Drive/Kaggle\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('02-14-2018.csv')\n",
        "df = df.drop(['Timestamp'], axis=1)\n",
        "df = df[df.Label == 'Benign']\n",
        "df = df.drop(['Label'], axis=1)\n",
        "\n",
        "df = df.fillna(value=0)\n",
        "df = df.replace([np.inf, -np.inf], 0)\n",
        "df = df.clip(lower=0, upper=1)\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "9VkDChbJ_ETu",
        "outputId": "1c7b9cf5-1b8e-4e7e-919e-ff26b04bccab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "   Dst Port  Protocol  Flow Duration  Tot Fwd Pkts  Tot Bwd Pkts  \\\n",
              "0         0         0              1             1             0   \n",
              "1         0         0              1             1             0   \n",
              "2         0         0              1             1             0   \n",
              "3         1         1              1             1             1   \n",
              "4         1         1              1             1             1   \n",
              "\n",
              "   TotLen Fwd Pkts  TotLen Bwd Pkts  Fwd Pkt Len Max  Fwd Pkt Len Min  \\\n",
              "0                0                0                0                0   \n",
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              "2                0                0                0                0   \n",
              "3                1                1                1                0   \n",
              "4                1                1                1                0   \n",
              "\n",
              "   Fwd Pkt Len Mean  ...  Fwd Act Data Pkts  Fwd Seg Size Min  Active Mean  \\\n",
              "0               0.0  ...                  0                 0          0.0   \n",
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              "0         0.0           0           0        1.0       1.0         1         1  \n",
              "1         0.0           0           0        1.0       1.0         1         1  \n",
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              "4         0.0           0           0        0.0       0.0         0         0  \n",
              "\n",
              "[5 rows x 78 columns]"
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "int_columns = df.select_dtypes(include=['int'])\n",
        "int_columns = int_columns.astype(float)\n",
        "df[int_columns.columns] = int_columns\n",
        "train, test = train_test_split(df, test_size=0.25, random_state=42)"
      ],
      "metadata": {
        "id": "C7z7TI75GPNA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n2WWYM6sR1H6",
        "outputId": "1822d873-dd3c-4967-92fd-cb0719749a01"
      },
      "execution_count": null,
      "outputs": [
        {
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 667626 entries, 0 to 1048574\n",
            "Data columns (total 78 columns):\n",
            " #   Column             Non-Null Count   Dtype  \n",
            "---  ------             --------------   -----  \n",
            " 0   Dst Port           667626 non-null  int64  \n",
            " 1   Protocol           667626 non-null  int64  \n",
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            " 3   Tot Fwd Pkts       667626 non-null  int64  \n",
            " 4   Tot Bwd Pkts       667626 non-null  int64  \n",
            " 5   TotLen Fwd Pkts    667626 non-null  int64  \n",
            " 6   TotLen Bwd Pkts    667626 non-null  int64  \n",
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            " 72  Active Max         667626 non-null  int64  \n",
            " 73  Active Min         667626 non-null  int64  \n",
            " 74  Idle Mean          667626 non-null  float64\n",
            " 75  Idle Std           667626 non-null  float64\n",
            " 76  Idle Max           667626 non-null  int64  \n",
            " 77  Idle Min           667626 non-null  int64  \n",
            "dtypes: float64(24), int64(54)\n",
            "memory usage: 402.4 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential([\n",
        "    layers.InputLayer(input_shape=(78,)),\n",
        "       \n",
        "    layers.Dense(78, activation='relu'),\n",
        "    layers.Dropout(rate = 0.3),\n",
        "    layers.Dense(64, activation='relu'),\n",
        "    layers.Dropout(rate = 0.3),\n",
        "    layers.Dense(50, activation='relu'),\n",
        "    layers.Dropout(rate = 0.3),\n",
        "    layers.Dense(36, activation='relu'),\n",
        "    layers.Dropout(rate = 0.3),\n",
        "    layers.Dense(20, activation='relu'),\n",
        "    layers.Dropout(rate = 0.3),\n",
        "    layers.Dense(36, activation='relu'),\n",
        "    layers.Dropout(rate = 0.3),\n",
        "    layers.Dense(50, activation='relu'),\n",
        "    layers.Dropout(rate = 0.3),\n",
        "    layers.Dense(64, activation='relu'),\n",
        "    layers.Dropout(rate = 0.3),\n",
        "    layers.Dense(78, activation='relu')\n",
        "])\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "T59nrOSD_NYI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfc4d3d0-ae55-4ccf-f65e-4a6266a37f69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 78)                6162      \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 78)                0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 64)                5056      \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 50)                3250      \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 50)                0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 36)                1836      \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 36)                0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 20)                740       \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 20)                0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 36)                756       \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 36)                0         \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 50)                1850      \n",
            "_________________________________________________________________\n",
            "dropout_6 (Dropout)          (None, 50)                0         \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 64)                3264      \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 78)                5070      \n",
            "=================================================================\n",
            "Total params: 27,984\n",
            "Trainable params: 27,984\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='mean_squared_error', optimizer = 'adam')\n",
        "model.fit(train, train, epochs=3, verbose=1)"
      ],
      "metadata": {
        "id": "pC1EDUCqEWtH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4af34a36-ef15-4225-a85e-c25c3ff7399b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "15648/15648 [==============================] - 78s 5ms/step - loss: 0.0374\n",
            "Epoch 2/3\n",
            "15648/15648 [==============================] - 66s 4ms/step - loss: 0.0262\n",
            "Epoch 3/3\n",
            "15648/15648 [==============================] - 64s 4ms/step - loss: 0.0258\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f7b39457640>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = model.predict(test)\n",
        "score1 = np.sqrt(metrics.mean_squared_error(pred, test))"
      ],
      "metadata": {
        "id": "CjBwekeMEb7J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YmZknC7uXlg7",
        "outputId": "b636ef6f-b129-41c5-ad98-5544f77f2a59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.2944753847054788"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    }
  ]
}