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      "source": [
        "# Capture to supress the download ouput\n",
        "%%capture\n",
        "!pip install gradio\n",
        "!pip install pandas\n",
        "!pip install transformers\n",
        "!pip install parsezeeklogs\n",
        "!pip install elasticsearch"
      ]
    },
    {
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        }
      ],
      "source": [
        "# Define imports for model use\n",
        "import torch\n",
        "from transformers import pipeline\n",
        "from parsezeeklogs import ParseZeekLogs\n",
        "from transformers import BertTokenizer\n",
        "import gradio as gr\n",
        "import pandas as pd\n",
        "\n",
        "# Define model\n",
        "pipe = pipeline(model=\"yashika0998/IoT-23-BERT-Network-Logs-Classification\", tokenizer=BertTokenizer.from_pretrained(\"bert-base-cased\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bSHZQ2C3MURT"
      },
      "outputs": [],
      "source": [
        "# Define string constants\n",
        "LOG = \"conn.log Output\"\n",
        "HEADER_TABLE = \"Headers Table\"\n",
        "SENTENCES = \"Sentences\"\n",
        "OUT = \"out\"\n",
        "INPUT_TYPES = [LOG, HEADER_TABLE, SENTENCES]\n",
        "STEPS = [HEADER_TABLE, SENTENCES]\n",
        "HEADERS=['id.resp_p', 'proto', 'conn_state', 'orig_pkts', 'orig_ip_bytes', 'resp_ip_bytes']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A8tRc3JlIeVF"
      },
      "outputs": [],
      "source": [
        "# Define sentence-ization functions\n",
        "# Dictionary of feature names to use in the make sentence function\n",
        "feature_names = {'id.resp_p':'response port',\n",
        "                 'proto':'transport protocol',\n",
        "                 'orig_pkts':'number of packets sent by the origin',\n",
        "                 'conn_state':'connection state',\n",
        "                 'orig_ip_bytes':'number of IP level bytes sent by the originator',\n",
        "                 'resp_ip_bytes':'number of IP level bytes sent by the responder'}\n",
        "\n",
        "# Function to make sentences out of the data\n",
        "def make_sentence(row):\n",
        "  sentences = {}\n",
        "  for feature in row.keys():\n",
        "    if feature == 'label' or feature == \"#\":\n",
        "      sentences[feature] = row[feature]\n",
        "    else:\n",
        "      sentences[feature] = feature_names[feature] + \" is \" + str(row[feature]) + \".\"\n",
        "  return sentences\n",
        "\n",
        "# Take all sentence observations and make them into paragraph inputs\n",
        "def make_paragraphs(ser):\n",
        "  paragraphs_list = []\n",
        "  for index,obs in ser.items():\n",
        "    new_para = obs['id.resp_p'] + \" \" + obs['proto'] + \" \" + obs['conn_state'] + \" \" + obs['orig_pkts'] + \" \" + obs['orig_ip_bytes'] + \" \" + obs['resp_ip_bytes']\n",
        "    paragraphs_list.append(new_para)\n",
        "  return pd.Series(paragraphs_list, name=\"Sentences\", index=ser.index).to_frame()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AkqQD0b4SqNG"
      },
      "outputs": [],
      "source": [
        "# Define prediction Functions For Different Settings\n",
        "def predictFromSentences(sentenceTable):\n",
        "  output = pipe(sentenceTable[SENTENCES].tolist()) # This does the prediction!\n",
        "  return { OUT: pd.DataFrame({\"Output\": [\"Malicious\" if pred['label'] == \"LABEL_0\" else \"Benign\" for pred in output] }) }\n",
        "\n",
        "def predictFromHeaderTable(headerTable):\n",
        "  sentences = headerTable.apply(make_sentence, axis=1);\n",
        "  paragraphs = make_paragraphs(sentences)\n",
        "  return {\n",
        "      SENTENCES: paragraphs,\n",
        "      OUT: predictFromSentences(paragraphs)[OUT]\n",
        "  }\n",
        "\n",
        "def predictFromFileUpload(fileUpload):\n",
        "  if(fileUpload is None):\n",
        "      raise gr.Error(\"No file uploaded\")\n",
        "  fileType = fileUpload.split('.')[-1]\n",
        "  if(fileType == 'csv'):\n",
        "    dataFrame = pd.read_csv(fileUpload, usecols=HEADERS)\n",
        "  elif(fileType == 'log' or fileType == 'labeled'):\n",
        "    with open('out.csv',\"w\") as outfile:\n",
        "      for log_record in ParseZeekLogs(fileUpload, output_format=\"csv\", safe_headers=False, fields=HEADERS):\n",
        "          if log_record is not None:\n",
        "              outfile.write(log_record + \"\\n\")\n",
        "    dataFrame = pd.read_csv('out.csv', names=HEADERS)\n",
        "  result = predictFromHeaderTable(dataFrame)\n",
        "  toReturn =  {\n",
        "      HEADER_TABLE: dataFrame,\n",
        "      SENTENCES: result[SENTENCES],\n",
        "      OUT: result[OUT]\n",
        "  }\n",
        "  return toReturn"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def makeIndexColumn(allInputs):\n",
        "  def _makeIndexColumnFor(column):\n",
        "    theseHeaders = allInputs[column].columns\n",
        "    newHeaders = ['#', *theseHeaders]\n",
        "    allInputs[column]['#'] = allInputs[column].index\n",
        "    allInputs[column] = allInputs[column][newHeaders]\n",
        "\n",
        "  if(SENTENCES in allInputs):\n",
        "    _makeIndexColumnFor(SENTENCES)\n",
        "  if(HEADER_TABLE in allInputs):\n",
        "    _makeIndexColumnFor(HEADER_TABLE)\n",
        "  if(OUT in allInputs):\n",
        "    _makeIndexColumnFor(OUT)\n",
        "  return allInputs\n",
        "\n",
        "def predict(inputType, fileUpload, headerTable, sentenceTable, out):\n",
        "  output = {};\n",
        "  if(inputType == LOG):\n",
        "    # Process File Upload\n",
        "    output = makeIndexColumn(predictFromFileUpload(fileUpload))\n",
        "    return [output[HEADER_TABLE], output[SENTENCES], output[OUT]]\n",
        "  elif(inputType == HEADER_TABLE):\n",
        "    # Process Header Table\n",
        "    output = makeIndexColumn(predictFromHeaderTable(headerTable))\n",
        "    return [headerTable, output[SENTENCES], output[OUT]]\n",
        "  elif(inputType == SENTENCES):\n",
        "    # Process From Sentences\n",
        "    output = makeIndexColumn(predictFromSentences(sentenceTable))\n",
        "    return [headerTable, sentenceTable, output[OUT]]\n",
        "\n",
        "# Update UI\n",
        "def updateInputOutputBlocks(inputType, steps):\n",
        "  # Update visibility and Interactivity of Gradio Blocks based on Settings\n",
        "  fileUpload = gr.File(\n",
        "      visible=(True if inputType == LOG else False),\n",
        "      interactive=(1 if inputType == LOG else 0)\n",
        "  )\n",
        "  headerTable = gr.Dataframe(\n",
        "      visible=(True if (inputType == HEADER_TABLE or HEADER_TABLE in steps) else False),\n",
        "      interactive=(1 if inputType == HEADER_TABLE else 0)\n",
        "    )\n",
        "  sentenceTable = gr.Dataframe(\n",
        "      interactive=(1 if inputType == SENTENCES else 0),\n",
        "      visible=(True if (inputType == SENTENCES or SENTENCES in steps) else False)\n",
        "    )\n",
        "  return fileUpload, headerTable, sentenceTable"
      ],
      "metadata": {
        "id": "kHzuc3_sbTLa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        },
        "id": "KFoJQQScGYjh",
        "outputId": "c7160221-6116-478e-b4ee-b1e5fad514c8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://275d064dabebea6639.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://275d064dabebea6639.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# Create Gradio UI\n",
        "with gr.Blocks() as app:\n",
        "    gr.Markdown(\"\"\"\n",
        "    # Network Log Predictions\n",
        "    Input log information below and click 'Run' to get predictions from our model!\n",
        "    Access the settings at the bottom for different types of input and to see inbetween steps.\n",
        "    \"\"\")\n",
        "    # Inputs / Outputs\n",
        "    fileUpload = gr.File(file_types=[\".log\", \".log.labeled\", \".csv\"], label=\"Zeek Log File\", visible=False, file_count='single')\n",
        "    headerTable = gr.Dataframe(row_count = (2, \"dynamic\"), col_count=(7,\"fixed\"), headers=['#', *HEADERS], label=\"Header Inputs\", interactive=1)\n",
        "    sentenceTable = gr.Dataframe(row_count = (2, \"dynamic\"), col_count=(2, \"fixed\"), headers=[\"#\", \"Sentence\"], label=\"Sentences\", interactive=0, visible=False)\n",
        "    out = gr.Dataframe(row_count = (2, \"dynamic\"), col_count=(2, \"fixed\"), headers=['#', \"Output\"], label=\"Predictions\", column_widths=[\"60px\", \"100%\"])\n",
        "    btn = gr.Button(\"Run\")\n",
        "\n",
        "    # Settings\n",
        "    with gr.Accordion(\"Settings\", open=False):\n",
        "      inputType = gr.Radio(INPUT_TYPES, value=\"Headers Table\", label=\"Input\")\n",
        "      steps = gr.CheckboxGroup(STEPS, label=\"Display Intermediary Steps\")\n",
        "      inputType.change(\n",
        "        fn=updateInputOutputBlocks,\n",
        "        inputs=[inputType, steps],\n",
        "        outputs=[fileUpload, headerTable, sentenceTable]\n",
        "      )\n",
        "      steps.change(\n",
        "        fn=updateInputOutputBlocks,\n",
        "        inputs=[inputType, steps],\n",
        "        outputs=[fileUpload, headerTable, sentenceTable]\n",
        "      )\n",
        "    # Assign Callback\n",
        "    btn.click(\n",
        "        fn=predict,\n",
        "        inputs=[inputType, fileUpload, headerTable, sentenceTable, out],\n",
        "        outputs=[headerTable, sentenceTable, out]\n",
        "        )\n",
        "\n",
        "    # Add tokenization and prediction functions\n",
        "    #btn.click(fn=get_predictions, inputs=tokenize_inputs(sentence_input), outputs=out)\n",
        "\n",
        "\n",
        "app.launch()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Export the model in ONNX format\n"
      ],
      "metadata": {
        "id": "j-rm_JVGJyNa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers[onnx]\n",
        "!pip install optimum\n",
        "!pip install onnxruntime"
      ],
      "metadata": {
        "id": "7B2dgJ3QJw4Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch"
      ],
      "metadata": {
        "id": "KWYKCewEaFTj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "from transformers import BertTokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\"19kmunz/IoT-23-BERT-Network-Logs-Classification\")"
      ],
      "metadata": {
        "id": "CGRovdz3Vtm-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dummy_input = [\"response port is 8081. transport protocol is tcp. connection state is S0. number of packets sent by the origin is 2. number of IP level bytes sent by the originator is 80. number of IP level bytes sent by the responder is 0\"] * model.config.max_position_embeddings\n",
        "dummy_input = tokenizer(dummy_input, return_tensors=\"pt\").input_ids"
      ],
      "metadata": {
        "id": "WCE1457dV7NH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "torch.onnx.export(model, dummy_input, \"IoT-23-BERT-Network-Logs-Classification.onnx\", export_params=True)"
      ],
      "metadata": {
        "id": "4ZvVODyvWCAM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "cBS82h8xWC-7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "export_dir = \"/content/drive/MyDrive/CS513 Final Project/ONNX_model\"\n",
        "torch.onnx.export(model, dummy_input, export_dir + \"/IoT-23-BERT-Network-Logs-Classification.onnx\", export_params=True)"
      ],
      "metadata": {
        "id": "DSOYqFdkWDbp"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
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