diff --git a/convert_original_datasets/Convert for Device 1.ipynb b/convert_original_datasets/Convert for Device 1.ipynb
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+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Convert for Device 1.ipynb","provenance":[],"toc_visible":true,"authorship_tag":"ABX9TyOSdPXufQh1wvu5FKw0x/RE"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vhML_9mQeosl","executionInfo":{"status":"ok","timestamp":1625205435664,"user_tz":-600,"elapsed":25317,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"4dac978d-12be-4260-abd4-7a8674016015"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"QKdnoUL3lGgk","executionInfo":{"status":"ok","timestamp":1625205436888,"user_tz":-600,"elapsed":1230,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}}},"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from collections import Counter\n","from scipy import stats"],"execution_count":2,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Caxxoby4oxbP"},"source":["# Load Datasets"]},{"cell_type":"code","metadata":{"id":"zDZ1WaK0lGZb","executionInfo":{"status":"ok","timestamp":1625205475106,"user_tz":-600,"elapsed":38223,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}}},"source":["# IoT Device #2 --> device_2_Ecobee_Thermostat\n","benign = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/1.benign.csv') \n","benign.shape\n","\n","# Botnet --> Mirai\n","# Attack --> Scan volnerable devices / Ack flooding / Syn flooding / UDP flooding / UDPplain\n","\n","m_scan = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/1.mirai.scan.csv')\n","m_ack = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/1.mirai.ack.csv')\n","m_syn = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/1.mirai.syn.csv')\n","m_udp = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/1.mirai.udp.csv')\n","m_udpplain = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/1.mirai.udpplain.csv')\n","\n","#Botnet --> Gafgyt (BASHLITE)\n","#Attack --> Scan vulnerable devices / Sending spam / UDP flooding / TCP flooding / Sending spam & opening connection\n","g_scan = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/1.gafgyt.scan.csv')\n","g_junk = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/1.gafgyt.junk.csv')\n","g_udp = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/1.gafgyt.udp.csv')\n","g_tcp = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/1.gafgyt.tcp.csv')\n","g_combo = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/1.gafgyt.combo.csv')"],"execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QxlE1MfhorlX","executionInfo":{"status":"ok","timestamp":1625205475117,"user_tz":-600,"elapsed":41,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"86e9e96c-94af-430a-9acb-3e932889c4a0"},"source":["print(m_scan.shape)\n","print(m_ack.shape)\n","print(m_syn.shape)\n","print(m_udp.shape)\n","print(m_udpplain.shape)\n","print(g_combo.shape)\n","print(g_junk.shape)\n","print(g_scan.shape)\n","print(g_tcp.shape)\n","print(g_udp.shape)"],"execution_count":4,"outputs":[{"output_type":"stream","text":["(107685, 115)\n","(102195, 115)\n","(122573, 115)\n","(237665, 115)\n","(81982, 115)\n","(59718, 115)\n","(29068, 115)\n","(29849, 115)\n","(92141, 115)\n","(105874, 115)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IL0f3rd_o-DL","executionInfo":{"status":"ok","timestamp":1625205475117,"user_tz":-600,"elapsed":31,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"9f48edf6-74f0-43b4-a550-4d5a05d650cd"},"source":["benign.info()"],"execution_count":5,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 49548 entries, 0 to 49547\n","Columns: 115 entries, MI_dir_L5_weight to HpHp_L0.01_pcc\n","dtypes: float64(115)\n","memory usage: 43.5 MB\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lh9-vCzLo510","executionInfo":{"status":"ok","timestamp":1625205475118,"user_tz":-600,"elapsed":27,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"3039ed86-b085-48f9-e5c9-7288fb6638ca"},"source":["#missing value \n","benign.iloc[:,:60].isna().any(),benign.iloc[:,60:].isna().any()"],"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(MI_dir_L5_weight         False\n"," MI_dir_L5_mean           False\n"," MI_dir_L5_variance       False\n"," MI_dir_L3_weight         False\n"," MI_dir_L3_mean           False\n"," MI_dir_L3_variance       False\n"," MI_dir_L1_weight         False\n"," MI_dir_L1_mean           False\n"," MI_dir_L1_variance       False\n"," MI_dir_L0.1_weight       False\n"," MI_dir_L0.1_mean         False\n"," MI_dir_L0.1_variance     False\n"," MI_dir_L0.01_weight      False\n"," MI_dir_L0.01_mean        False\n"," MI_dir_L0.01_variance    False\n"," H_L5_weight              False\n"," H_L5_mean                False\n"," H_L5_variance            False\n"," H_L3_weight              False\n"," H_L3_mean                False\n"," H_L3_variance            False\n"," H_L1_weight              False\n"," H_L1_mean                False\n"," H_L1_variance            False\n"," H_L0.1_weight            False\n"," H_L0.1_mean              False\n"," H_L0.1_variance          False\n"," H_L0.01_weight           False\n"," H_L0.01_mean             False\n"," H_L0.01_variance         False\n"," HH_L5_weight             False\n"," HH_L5_mean               False\n"," HH_L5_std                False\n"," HH_L5_magnitude          False\n"," HH_L5_radius             False\n"," HH_L5_covariance         False\n"," HH_L5_pcc                False\n"," HH_L3_weight             False\n"," HH_L3_mean               False\n"," HH_L3_std                False\n"," HH_L3_magnitude          False\n"," HH_L3_radius             False\n"," HH_L3_covariance         False\n"," HH_L3_pcc                False\n"," HH_L1_weight             False\n"," HH_L1_mean               False\n"," HH_L1_std                False\n"," HH_L1_magnitude          False\n"," HH_L1_radius             False\n"," HH_L1_covariance         False\n"," HH_L1_pcc                False\n"," HH_L0.1_weight           False\n"," HH_L0.1_mean             False\n"," HH_L0.1_std              False\n"," HH_L0.1_magnitude        False\n"," HH_L0.1_radius           False\n"," HH_L0.1_covariance       False\n"," HH_L0.1_pcc              False\n"," HH_L0.01_weight          False\n"," HH_L0.01_mean            False\n"," dtype: bool, HH_L0.01_std             False\n"," HH_L0.01_magnitude       False\n"," HH_L0.01_radius          False\n"," HH_L0.01_covariance      False\n"," HH_L0.01_pcc             False\n"," HH_jit_L5_weight         False\n"," HH_jit_L5_mean           False\n"," HH_jit_L5_variance       False\n"," HH_jit_L3_weight         False\n"," HH_jit_L3_mean           False\n"," HH_jit_L3_variance       False\n"," HH_jit_L1_weight         False\n"," HH_jit_L1_mean           False\n"," HH_jit_L1_variance       False\n"," HH_jit_L0.1_weight       False\n"," HH_jit_L0.1_mean         False\n"," HH_jit_L0.1_variance     False\n"," HH_jit_L0.01_weight      False\n"," HH_jit_L0.01_mean        False\n"," HH_jit_L0.01_variance    False\n"," HpHp_L5_weight           False\n"," HpHp_L5_mean             False\n"," HpHp_L5_std              False\n"," HpHp_L5_magnitude        False\n"," HpHp_L5_radius           False\n"," HpHp_L5_covariance       False\n"," HpHp_L5_pcc              False\n"," HpHp_L3_weight           False\n"," HpHp_L3_mean             False\n"," HpHp_L3_std              False\n"," HpHp_L3_magnitude        False\n"," HpHp_L3_radius           False\n"," HpHp_L3_covariance       False\n"," HpHp_L3_pcc              False\n"," HpHp_L1_weight           False\n"," HpHp_L1_mean             False\n"," HpHp_L1_std              False\n"," HpHp_L1_magnitude        False\n"," HpHp_L1_radius           False\n"," HpHp_L1_covariance       False\n"," HpHp_L1_pcc              False\n"," HpHp_L0.1_weight         False\n"," HpHp_L0.1_mean           False\n"," HpHp_L0.1_std            False\n"," HpHp_L0.1_magnitude      False\n"," HpHp_L0.1_radius         False\n"," HpHp_L0.1_covariance     False\n"," HpHp_L0.1_pcc            False\n"," HpHp_L0.01_weight        False\n"," HpHp_L0.01_mean          False\n"," HpHp_L0.01_std           False\n"," HpHp_L0.01_magnitude     False\n"," HpHp_L0.01_radius        False\n"," HpHp_L0.01_covariance    False\n"," HpHp_L0.01_pcc           False\n"," dtype: bool)"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9cm3cXb1o_Tr","executionInfo":{"status":"ok","timestamp":1625205475119,"user_tz":-600,"elapsed":25,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"7f0025e4-6e5c-4d62-e7bf-1c7d97d1f30c"},"source":["#checking if all the files have the same features \n","print(g_combo.columns == benign.columns)\n","print(g_combo.columns == m_ack.columns)"],"execution_count":7,"outputs":[{"output_type":"stream","text":["[ True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True]\n","[ True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"IeRbXZ4SpeY2"},"source":["# Convert into one dataset"]},{"cell_type":"code","metadata":{"id":"xyhQoJRJpIvV","executionInfo":{"status":"ok","timestamp":1625205475119,"user_tz":-600,"elapsed":19,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}}},"source":["# add new column\n","benign['type'] = 'benign'\n","m_scan['type'] = 'm_scan'\n","m_ack['type'] = 'm_ack'\n","m_syn['type']  = 'm_syn'\n","m_udp['type'] = 'm_udp'\n","m_udpplain['type'] = 'm_udpplain'\n","g_combo['type'] = 'g_combo'\n","g_junk['type'] = 'g_junk'\n","g_scan['type'] = 'g_scan'\n","g_tcp['type'] = 'g_tcp'\n","g_udp['type'] = 'g_udp'"],"execution_count":8,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vDIuhHj3pS7f","executionInfo":{"status":"ok","timestamp":1625205475544,"user_tz":-600,"elapsed":444,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"607dcc7b-7caa-4e0c-b195-7284d0ce7875"},"source":["d = pd.concat([benign,m_scan,m_ack,m_syn,m_udp,m_udpplain,g_combo,g_junk,g_scan,g_tcp,g_udp], axis=0, sort=False, ignore_index=True)\n","d.shape"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1018298, 116)"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":385},"id":"8hYBsMGvpZl4","executionInfo":{"status":"ok","timestamp":1625205475561,"user_tz":-600,"elapsed":26,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"d72f95e1-7f1c-4d19-e70f-d0fb75811541"},"source":["#value counts\n","labels = d['type'].value_counts()\n","labels\n","label_count = pd.DataFrame({'labels': labels.index,'counts': labels.values})\n","label_count"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>labels</th>\n","      <th>counts</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>m_udp</td>\n","      <td>237665</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>m_syn</td>\n","      <td>122573</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>m_scan</td>\n","      <td>107685</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>g_udp</td>\n","      <td>105874</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>m_ack</td>\n","      <td>102195</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>g_tcp</td>\n","      <td>92141</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>m_udpplain</td>\n","      <td>81982</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>g_combo</td>\n","      <td>59718</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>benign</td>\n","      <td>49548</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>g_scan</td>\n","      <td>29849</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>g_junk</td>\n","      <td>29068</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        labels  counts\n","0        m_udp  237665\n","1        m_syn  122573\n","2       m_scan  107685\n","3        g_udp  105874\n","4        m_ack  102195\n","5        g_tcp   92141\n","6   m_udpplain   81982\n","7      g_combo   59718\n","8       benign   49548\n","9       g_scan   29849\n","10      g_junk   29068"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":441},"id":"kecoeAtgppQl","executionInfo":{"status":"ok","timestamp":1625205476165,"user_tz":-600,"elapsed":626,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"73579aa8-f279-4b0d-8ddc-88029d81ce71"},"source":["labels = label_count['labels']\n","sizes = label_count['counts']\n","plt.figure(figsize=(8,6))\n","colors = ['red', 'orange', 'yellow', 'green', 'blue', 'lightskyblue', 'pink', 'purple', 'rosybrown', 'olive', 'darkgrey']\n","# colors = [1,2,3,4,5,6,7,8,9,10,11]\n","explode = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1)\n","patches, texts = plt.pie(sizes, explode = explode, colors=colors, shadow=True, startangle=100, pctdistance=100, labeldistance=10)\n","plt.legend(patches, labels, loc=\"best\")\n","plt.axis('equal')\n","plt.tight_layout()\n","plt.show()"],"execution_count":11,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x432 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"qcuz3bkap0-r"},"source":["# Save dataset"]},{"cell_type":"code","metadata":{"id":"JkF4Keo5pwsz","executionInfo":{"status":"ok","timestamp":1625205621037,"user_tz":-600,"elapsed":144880,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}}},"source":["d.to_csv('/content/drive/MyDrive/5703 CP/Device #1.csv')"],"execution_count":12,"outputs":[]}]}
\ No newline at end of file
diff --git a/convert_original_datasets/Convert for Device 2.ipynb b/convert_original_datasets/Convert for Device 2.ipynb
new file mode 100644
index 0000000..ffbd228
--- /dev/null
+++ b/convert_original_datasets/Convert for Device 2.ipynb	
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Convert for Device 2.ipynb","provenance":[],"toc_visible":true,"authorship_tag":"ABX9TyOrEgKXTD2sq+KiSY2Kt+rm"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vhML_9mQeosl","executionInfo":{"status":"ok","timestamp":1625205112351,"user_tz":-600,"elapsed":23825,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"247a7ee6-2004-439d-f68b-9afc1400a53d"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"QKdnoUL3lGgk","executionInfo":{"status":"ok","timestamp":1625205112769,"user_tz":-600,"elapsed":453,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}}},"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from collections import Counter\n","from scipy import stats"],"execution_count":2,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Caxxoby4oxbP"},"source":["# Load Datasets"]},{"cell_type":"code","metadata":{"id":"zDZ1WaK0lGZb","executionInfo":{"status":"ok","timestamp":1625205146065,"user_tz":-600,"elapsed":33299,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}}},"source":["# IoT Device #2 --> device_2_Ecobee_Thermostat\n","benign = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/2.benign.csv') \n","benign.shape\n","\n","# Botnet --> Mirai\n","# Attack --> Scan volnerable devices / Ack flooding / Syn flooding / UDP flooding / UDPplain\n","\n","m_scan = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/2.mirai.scan.csv')\n","m_ack = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/2.mirai.ack.csv')\n","m_syn = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/2.mirai.syn.csv')\n","m_udp = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/2.mirai.udp.csv')\n","m_udpplain = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/2.mirai.udpplain.csv')\n","\n","#Botnet --> Gafgyt (BASHLITE)\n","#Attack --> Scan vulnerable devices / Sending spam / UDP flooding / TCP flooding / Sending spam & opening connection\n","g_scan = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/2.gafgyt.scan.csv')\n","g_junk = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/2.gafgyt.junk.csv')\n","g_udp = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/2.gafgyt.udp.csv')\n","g_tcp = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/2.gafgyt.tcp.csv')\n","g_combo = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/2.gafgyt.combo.csv')"],"execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QxlE1MfhorlX","executionInfo":{"status":"ok","timestamp":1625205146066,"user_tz":-600,"elapsed":47,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"27119914-398f-4ee4-83c0-c0a3f3420dca"},"source":["print(m_scan.shape)\n","print(m_ack.shape)\n","print(m_syn.shape)\n","print(m_udp.shape)\n","print(m_udpplain.shape)\n","print(g_combo.shape)\n","print(g_junk.shape)\n","print(g_scan.shape)\n","print(g_tcp.shape)\n","print(g_udp.shape)"],"execution_count":4,"outputs":[{"output_type":"stream","text":["(43192, 115)\n","(113285, 115)\n","(116807, 115)\n","(151481, 115)\n","(87368, 115)\n","(53012, 115)\n","(30312, 115)\n","(27494, 115)\n","(95021, 115)\n","(104791, 115)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IL0f3rd_o-DL","executionInfo":{"status":"ok","timestamp":1625205146067,"user_tz":-600,"elapsed":41,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"fd2c0135-6ec8-45a5-e69d-96ef186ea153"},"source":["benign.info()"],"execution_count":5,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 13113 entries, 0 to 13112\n","Columns: 115 entries, MI_dir_L5_weight to HpHp_L0.01_pcc\n","dtypes: float64(115)\n","memory usage: 11.5 MB\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lh9-vCzLo510","executionInfo":{"status":"ok","timestamp":1625205146068,"user_tz":-600,"elapsed":34,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"934e0b7f-24dc-4c8c-bda0-798a9d567c99"},"source":["#missing value \n","benign.iloc[:,:60].isna().any(),benign.iloc[:,60:].isna().any()"],"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(MI_dir_L5_weight         False\n"," MI_dir_L5_mean           False\n"," MI_dir_L5_variance       False\n"," MI_dir_L3_weight         False\n"," MI_dir_L3_mean           False\n"," MI_dir_L3_variance       False\n"," MI_dir_L1_weight         False\n"," MI_dir_L1_mean           False\n"," MI_dir_L1_variance       False\n"," MI_dir_L0.1_weight       False\n"," MI_dir_L0.1_mean         False\n"," MI_dir_L0.1_variance     False\n"," MI_dir_L0.01_weight      False\n"," MI_dir_L0.01_mean        False\n"," MI_dir_L0.01_variance    False\n"," H_L5_weight              False\n"," H_L5_mean                False\n"," H_L5_variance            False\n"," H_L3_weight              False\n"," H_L3_mean                False\n"," H_L3_variance            False\n"," H_L1_weight              False\n"," H_L1_mean                False\n"," H_L1_variance            False\n"," H_L0.1_weight            False\n"," H_L0.1_mean              False\n"," H_L0.1_variance          False\n"," H_L0.01_weight           False\n"," H_L0.01_mean             False\n"," H_L0.01_variance         False\n"," HH_L5_weight             False\n"," HH_L5_mean               False\n"," HH_L5_std                False\n"," HH_L5_magnitude          False\n"," HH_L5_radius             False\n"," HH_L5_covariance         False\n"," HH_L5_pcc                False\n"," HH_L3_weight             False\n"," HH_L3_mean               False\n"," HH_L3_std                False\n"," HH_L3_magnitude          False\n"," HH_L3_radius             False\n"," HH_L3_covariance         False\n"," HH_L3_pcc                False\n"," HH_L1_weight             False\n"," HH_L1_mean               False\n"," HH_L1_std                False\n"," HH_L1_magnitude          False\n"," HH_L1_radius             False\n"," HH_L1_covariance         False\n"," HH_L1_pcc                False\n"," HH_L0.1_weight           False\n"," HH_L0.1_mean             False\n"," HH_L0.1_std              False\n"," HH_L0.1_magnitude        False\n"," HH_L0.1_radius           False\n"," HH_L0.1_covariance       False\n"," HH_L0.1_pcc              False\n"," HH_L0.01_weight          False\n"," HH_L0.01_mean            False\n"," dtype: bool, HH_L0.01_std             False\n"," HH_L0.01_magnitude       False\n"," HH_L0.01_radius          False\n"," HH_L0.01_covariance      False\n"," HH_L0.01_pcc             False\n"," HH_jit_L5_weight         False\n"," HH_jit_L5_mean           False\n"," HH_jit_L5_variance       False\n"," HH_jit_L3_weight         False\n"," HH_jit_L3_mean           False\n"," HH_jit_L3_variance       False\n"," HH_jit_L1_weight         False\n"," HH_jit_L1_mean           False\n"," HH_jit_L1_variance       False\n"," HH_jit_L0.1_weight       False\n"," HH_jit_L0.1_mean         False\n"," HH_jit_L0.1_variance     False\n"," HH_jit_L0.01_weight      False\n"," HH_jit_L0.01_mean        False\n"," HH_jit_L0.01_variance    False\n"," HpHp_L5_weight           False\n"," HpHp_L5_mean             False\n"," HpHp_L5_std              False\n"," HpHp_L5_magnitude        False\n"," HpHp_L5_radius           False\n"," HpHp_L5_covariance       False\n"," HpHp_L5_pcc              False\n"," HpHp_L3_weight           False\n"," HpHp_L3_mean             False\n"," HpHp_L3_std              False\n"," HpHp_L3_magnitude        False\n"," HpHp_L3_radius           False\n"," HpHp_L3_covariance       False\n"," HpHp_L3_pcc              False\n"," HpHp_L1_weight           False\n"," HpHp_L1_mean             False\n"," HpHp_L1_std              False\n"," HpHp_L1_magnitude        False\n"," HpHp_L1_radius           False\n"," HpHp_L1_covariance       False\n"," HpHp_L1_pcc              False\n"," HpHp_L0.1_weight         False\n"," HpHp_L0.1_mean           False\n"," HpHp_L0.1_std            False\n"," HpHp_L0.1_magnitude      False\n"," HpHp_L0.1_radius         False\n"," HpHp_L0.1_covariance     False\n"," HpHp_L0.1_pcc            False\n"," HpHp_L0.01_weight        False\n"," HpHp_L0.01_mean          False\n"," HpHp_L0.01_std           False\n"," HpHp_L0.01_magnitude     False\n"," HpHp_L0.01_radius        False\n"," HpHp_L0.01_covariance    False\n"," HpHp_L0.01_pcc           False\n"," dtype: bool)"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9cm3cXb1o_Tr","executionInfo":{"status":"ok","timestamp":1625205146069,"user_tz":-600,"elapsed":16,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"679a8f22-16c5-402f-a6dc-972e539343d0"},"source":["#checking if all the files have the same features \n","print(g_combo.columns == benign.columns)\n","print(g_combo.columns == m_ack.columns)"],"execution_count":7,"outputs":[{"output_type":"stream","text":["[ True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True]\n","[ True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"IeRbXZ4SpeY2"},"source":["# Convert into one dataset"]},{"cell_type":"code","metadata":{"id":"xyhQoJRJpIvV","executionInfo":{"status":"ok","timestamp":1625205146069,"user_tz":-600,"elapsed":10,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}}},"source":["# add new column\n","benign['type'] = 'benign'\n","m_scan['type'] = 'm_scan'\n","m_ack['type'] = 'm_ack'\n","m_syn['type']  = 'm_syn'\n","m_udp['type'] = 'm_udp'\n","m_udpplain['type'] = 'm_udpplain'\n","g_combo['type'] = 'g_combo'\n","g_junk['type'] = 'g_junk'\n","g_scan['type'] = 'g_scan'\n","g_tcp['type'] = 'g_tcp'\n","g_udp['type'] = 'g_udp'"],"execution_count":8,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vDIuhHj3pS7f","executionInfo":{"status":"ok","timestamp":1625205146611,"user_tz":-600,"elapsed":552,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"f3a74160-f10e-480e-8201-29fb1f484295"},"source":["d = pd.concat([benign,m_scan,m_ack,m_syn,m_udp,m_udpplain,g_combo,g_junk,g_scan,g_tcp,g_udp], axis=0, sort=False, ignore_index=True)\n","d.shape"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(835876, 116)"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":385},"id":"8hYBsMGvpZl4","executionInfo":{"status":"ok","timestamp":1625205146635,"user_tz":-600,"elapsed":30,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"0cf5c38a-09a5-46df-b4c3-3f66c2db88e0"},"source":["#value counts\n","labels = d['type'].value_counts()\n","labels\n","label_count = pd.DataFrame({'labels': labels.index,'counts': labels.values})\n","label_count"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>labels</th>\n","      <th>counts</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>m_udp</td>\n","      <td>151481</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>m_syn</td>\n","      <td>116807</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>m_ack</td>\n","      <td>113285</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>g_udp</td>\n","      <td>104791</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>g_tcp</td>\n","      <td>95021</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>m_udpplain</td>\n","      <td>87368</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>g_combo</td>\n","      <td>53012</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>m_scan</td>\n","      <td>43192</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>g_junk</td>\n","      <td>30312</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>g_scan</td>\n","      <td>27494</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>benign</td>\n","      <td>13113</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        labels  counts\n","0        m_udp  151481\n","1        m_syn  116807\n","2        m_ack  113285\n","3        g_udp  104791\n","4        g_tcp   95021\n","5   m_udpplain   87368\n","6      g_combo   53012\n","7       m_scan   43192\n","8       g_junk   30312\n","9       g_scan   27494\n","10      benign   13113"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":441},"id":"kecoeAtgppQl","executionInfo":{"status":"ok","timestamp":1625205147438,"user_tz":-600,"elapsed":831,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"ae5ff5f1-77ea-4834-981c-0c33571007fb"},"source":["labels = label_count['labels']\n","sizes = label_count['counts']\n","plt.figure(figsize=(8,6))\n","colors = ['red', 'orange', 'yellow', 'green', 'blue', 'lightskyblue', 'pink', 'purple', 'rosybrown', 'olive', 'darkgrey']\n","# colors = [1,2,3,4,5,6,7,8,9,10,11]\n","explode = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1)\n","patches, texts = plt.pie(sizes, explode = explode, colors=colors, shadow=True, startangle=100, pctdistance=100, labeldistance=10)\n","plt.legend(patches, labels, loc=\"best\")\n","plt.axis('equal')\n","plt.tight_layout()\n","plt.show()"],"execution_count":11,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x432 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"qcuz3bkap0-r"},"source":["# Save dataset"]},{"cell_type":"code","metadata":{"id":"JkF4Keo5pwsz","executionInfo":{"status":"ok","timestamp":1625205274590,"user_tz":-600,"elapsed":127160,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}}},"source":["d.to_csv('/content/drive/MyDrive/5703 CP/Device #2.csv')"],"execution_count":12,"outputs":[]}]}
\ No newline at end of file
diff --git a/convert_original_datasets/Convert for Device 3.ipynb b/convert_original_datasets/Convert for Device 3.ipynb
new file mode 100644
index 0000000..54f65cc
--- /dev/null
+++ b/convert_original_datasets/Convert for Device 3.ipynb	
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Convert for Device 3.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true,"authorship_tag":"ABX9TyMEbJLnAwoy8K+WCXiIkIvi"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vhML_9mQeosl","executionInfo":{"status":"ok","timestamp":1625205017822,"user_tz":-600,"elapsed":20099,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"84ab00e9-39c5-4404-9379-d026314f82d9"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"QKdnoUL3lGgk","executionInfo":{"status":"ok","timestamp":1625205018336,"user_tz":-600,"elapsed":525,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}}},"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from collections import Counter\n","from scipy import stats"],"execution_count":2,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Caxxoby4oxbP"},"source":["# Load Datasets"]},{"cell_type":"code","metadata":{"id":"zDZ1WaK0lGZb","executionInfo":{"status":"ok","timestamp":1625205033381,"user_tz":-600,"elapsed":15048,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}}},"source":["# IoT Device #2 --> device_2_Ecobee_Thermostat\n","benign = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/3.benign.csv') \n","benign.shape\n","\n","# Botnet --> Mirai\n","# Attack --> Scan volnerable devices / Ack flooding / Syn flooding / UDP flooding / UDPplain\n","\n","# m_scan = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/8.mirai.scan.csv')\n","# m_ack = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/8.mirai.ack.csv')\n","# m_syn = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/8.mirai.syn.csv')\n","# m_udp = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/8.mirai.udp.csv')\n","# m_udpplain = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/8.mirai.udpplain.csv')\n","\n","#Botnet --> Gafgyt (BASHLITE)\n","#Attack --> Scan vulnerable devices / Sending spam / UDP flooding / TCP flooding / Sending spam & opening connection\n","g_scan = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/3.gafgyt.scan.csv')\n","g_junk = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/3.gafgyt.junk.csv')\n","g_udp = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/3.gafgyt.udp.csv')\n","g_tcp = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/3.gafgyt.tcp.csv')\n","g_combo = pd.read_csv('/content/drive/MyDrive/5703 CP/Datasets/3.gafgyt.combo.csv')"],"execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"id":"QxlE1MfhorlX","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625205033388,"user_tz":-600,"elapsed":28,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"0670465a-d0cd-44ec-ea04-acfb5f43030d"},"source":["# print(m_scan.shape)\n","# print(m_ack.shape)\n","# print(m_syn.shape)\n","# print(m_udp.shape)\n","# print(m_udpplain.shape)\n","print(g_combo.shape)\n","print(g_junk.shape)\n","print(g_scan.shape)\n","print(g_tcp.shape)\n","print(g_udp.shape)"],"execution_count":4,"outputs":[{"output_type":"stream","text":["(53014, 115)\n","(29797, 115)\n","(28120, 115)\n","(101536, 115)\n","(103933, 115)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"IL0f3rd_o-DL","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625205033388,"user_tz":-600,"elapsed":22,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"67093ee1-4755-4bf3-8262-264631bc6eb6"},"source":["benign.info()"],"execution_count":5,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 39100 entries, 0 to 39099\n","Columns: 115 entries, MI_dir_L5_weight to HpHp_L0.01_pcc\n","dtypes: float64(115)\n","memory usage: 34.3 MB\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"lh9-vCzLo510","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625205033389,"user_tz":-600,"elapsed":19,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"a302e481-e33f-43e1-c079-90cc568dc149"},"source":["#missing value \n","benign.iloc[:,:60].isna().any(),benign.iloc[:,60:].isna().any()"],"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(MI_dir_L5_weight         False\n"," MI_dir_L5_mean           False\n"," MI_dir_L5_variance       False\n"," MI_dir_L3_weight         False\n"," MI_dir_L3_mean           False\n"," MI_dir_L3_variance       False\n"," MI_dir_L1_weight         False\n"," MI_dir_L1_mean           False\n"," MI_dir_L1_variance       False\n"," MI_dir_L0.1_weight       False\n"," MI_dir_L0.1_mean         False\n"," MI_dir_L0.1_variance     False\n"," MI_dir_L0.01_weight      False\n"," MI_dir_L0.01_mean        False\n"," MI_dir_L0.01_variance    False\n"," H_L5_weight              False\n"," H_L5_mean                False\n"," H_L5_variance            False\n"," H_L3_weight              False\n"," H_L3_mean                False\n"," H_L3_variance            False\n"," H_L1_weight              False\n"," H_L1_mean                False\n"," H_L1_variance            False\n"," H_L0.1_weight            False\n"," H_L0.1_mean              False\n"," H_L0.1_variance          False\n"," H_L0.01_weight           False\n"," H_L0.01_mean             False\n"," H_L0.01_variance         False\n"," HH_L5_weight             False\n"," HH_L5_mean               False\n"," HH_L5_std                False\n"," HH_L5_magnitude          False\n"," HH_L5_radius             False\n"," HH_L5_covariance         False\n"," HH_L5_pcc                False\n"," HH_L3_weight             False\n"," HH_L3_mean               False\n"," HH_L3_std                False\n"," HH_L3_magnitude          False\n"," HH_L3_radius             False\n"," HH_L3_covariance         False\n"," HH_L3_pcc                False\n"," HH_L1_weight             False\n"," HH_L1_mean               False\n"," HH_L1_std                False\n"," HH_L1_magnitude          False\n"," HH_L1_radius             False\n"," HH_L1_covariance         False\n"," HH_L1_pcc                False\n"," HH_L0.1_weight           False\n"," HH_L0.1_mean             False\n"," HH_L0.1_std              False\n"," HH_L0.1_magnitude        False\n"," HH_L0.1_radius           False\n"," HH_L0.1_covariance       False\n"," HH_L0.1_pcc              False\n"," HH_L0.01_weight          False\n"," HH_L0.01_mean            False\n"," dtype: bool, HH_L0.01_std             False\n"," HH_L0.01_magnitude       False\n"," HH_L0.01_radius          False\n"," HH_L0.01_covariance      False\n"," HH_L0.01_pcc             False\n"," HH_jit_L5_weight         False\n"," HH_jit_L5_mean           False\n"," HH_jit_L5_variance       False\n"," HH_jit_L3_weight         False\n"," HH_jit_L3_mean           False\n"," HH_jit_L3_variance       False\n"," HH_jit_L1_weight         False\n"," HH_jit_L1_mean           False\n"," HH_jit_L1_variance       False\n"," HH_jit_L0.1_weight       False\n"," HH_jit_L0.1_mean         False\n"," HH_jit_L0.1_variance     False\n"," HH_jit_L0.01_weight      False\n"," HH_jit_L0.01_mean        False\n"," HH_jit_L0.01_variance    False\n"," HpHp_L5_weight           False\n"," HpHp_L5_mean             False\n"," HpHp_L5_std              False\n"," HpHp_L5_magnitude        False\n"," HpHp_L5_radius           False\n"," HpHp_L5_covariance       False\n"," HpHp_L5_pcc              False\n"," HpHp_L3_weight           False\n"," HpHp_L3_mean             False\n"," HpHp_L3_std              False\n"," HpHp_L3_magnitude        False\n"," HpHp_L3_radius           False\n"," HpHp_L3_covariance       False\n"," HpHp_L3_pcc              False\n"," HpHp_L1_weight           False\n"," HpHp_L1_mean             False\n"," HpHp_L1_std              False\n"," HpHp_L1_magnitude        False\n"," HpHp_L1_radius           False\n"," HpHp_L1_covariance       False\n"," HpHp_L1_pcc              False\n"," HpHp_L0.1_weight         False\n"," HpHp_L0.1_mean           False\n"," HpHp_L0.1_std            False\n"," HpHp_L0.1_magnitude      False\n"," HpHp_L0.1_radius         False\n"," HpHp_L0.1_covariance     False\n"," HpHp_L0.1_pcc            False\n"," HpHp_L0.01_weight        False\n"," HpHp_L0.01_mean          False\n"," HpHp_L0.01_std           False\n"," HpHp_L0.01_magnitude     False\n"," HpHp_L0.01_radius        False\n"," HpHp_L0.01_covariance    False\n"," HpHp_L0.01_pcc           False\n"," dtype: bool)"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"code","metadata":{"id":"9cm3cXb1o_Tr","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625205033390,"user_tz":-600,"elapsed":16,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"6d42b217-4820-4531-8662-6804eea9d66f"},"source":["#checking if all the files have the same features \n","print(g_combo.columns == benign.columns)\n","# print(g_combo.columns == m_ack.columns)"],"execution_count":7,"outputs":[{"output_type":"stream","text":["[ True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True  True  True  True  True  True\n","  True  True  True  True  True  True  True]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"IeRbXZ4SpeY2"},"source":["# Convert into one dataset"]},{"cell_type":"code","metadata":{"id":"xyhQoJRJpIvV","executionInfo":{"status":"ok","timestamp":1625205033390,"user_tz":-600,"elapsed":11,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}}},"source":["# add new column\n","benign['type'] = 'benign'\n","# m_scan['type'] = 'm_scan'\n","# m_ack['type'] = 'm_ack'\n","# m_syn['type']  = 'm_syn'\n","# m_udp['type'] = 'm_udp'\n","# m_udpplain['type'] = 'm_udpplain'\n","g_combo['type'] = 'g_combo'\n","g_junk['type'] = 'g_junk'\n","g_scan['type'] = 'g_scan'\n","g_tcp['type'] = 'g_tcp'\n","g_udp['type'] = 'g_udp'"],"execution_count":8,"outputs":[]},{"cell_type":"code","metadata":{"id":"vDIuhHj3pS7f","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625205034099,"user_tz":-600,"elapsed":719,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"efb44c19-d8ff-42a7-b0fb-0a573b14e1f9"},"source":["d = pd.concat([benign,g_combo,g_junk,g_scan,g_tcp,g_udp], axis=0, sort=False, ignore_index=True)\n","d.shape"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(355500, 116)"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"id":"8hYBsMGvpZl4","colab":{"base_uri":"https://localhost:8080/","height":232},"executionInfo":{"status":"ok","timestamp":1625205034101,"user_tz":-600,"elapsed":15,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"37b23a1e-771e-45d6-888b-81bbd221df00"},"source":["#value counts\n","labels = d['type'].value_counts()\n","labels\n","label_count = pd.DataFrame({'labels': labels.index,'counts': labels.values})\n","label_count"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>labels</th>\n","      <th>counts</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>g_udp</td>\n","      <td>103933</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>g_tcp</td>\n","      <td>101536</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>g_combo</td>\n","      <td>53014</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>benign</td>\n","      <td>39100</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>g_junk</td>\n","      <td>29797</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>g_scan</td>\n","      <td>28120</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    labels  counts\n","0    g_udp  103933\n","1    g_tcp  101536\n","2  g_combo   53014\n","3   benign   39100\n","4   g_junk   29797\n","5   g_scan   28120"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"id":"kecoeAtgppQl","colab":{"base_uri":"https://localhost:8080/","height":441},"executionInfo":{"status":"ok","timestamp":1625205206931,"user_tz":-600,"elapsed":439,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}},"outputId":"e9861be2-12b8-42ea-85fc-05b10719d58f"},"source":["labels = label_count['labels']\n","sizes = label_count['counts']\n","plt.figure(figsize=(8,6))\n","colors = ['red', 'orange', 'yellow', 'green', 'olive', 'darkgrey']\n","# colors = [1,2,3,4,5,6,7,8,9,10,11]\n","explode = (0, 0, 0, 0.1, 0, 0)\n","patches, texts = plt.pie(sizes, explode = explode, colors=colors, shadow=True, startangle=100, pctdistance=100, labeldistance=10)\n","plt.legend(patches, labels, loc=\"best\")\n","plt.axis('equal')\n","plt.tight_layout()\n","plt.show()"],"execution_count":16,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x432 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"qcuz3bkap0-r"},"source":["# Save dataset"]},{"cell_type":"code","metadata":{"id":"JkF4Keo5pwsz","executionInfo":{"status":"ok","timestamp":1625205080872,"user_tz":-600,"elapsed":46781,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}}},"source":["d.to_csv('/content/drive/MyDrive/5703 CP/Device #3.csv')"],"execution_count":12,"outputs":[]},{"cell_type":"code","metadata":{"id":"rnrNSzdrsFH8","executionInfo":{"status":"ok","timestamp":1625205080873,"user_tz":-600,"elapsed":31,"user":{"displayName":"Jane ZOU","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhovXuga2ucGkqSP_krLzx1k4yHH4hCC5bJTF0L=s64","userId":"02928433389786515403"}}},"source":[""],"execution_count":12,"outputs":[]}]}
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