Phase-1 Contributions for Analyzer

.github/FUNDING.yml

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+# These are supported funding model platforms
+
+github: hardlyhuman
+patreon: gsriharsha
+custom: ["https://www.paypal.me/SRIHARSHAGAJAVALLI"]

.github/ISSUE_TEMPLATE/bug_report.md

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+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. Go to '...'
+2. Click on '....'
+3. Scroll down to '....'
+4. See error
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**Desktop (please complete the following information):**
+ - OS: [e.g. iOS]
+ - Browser [e.g. chrome, safari]
+ - Version [e.g. 22]
+
+**Smartphone (please complete the following information):**
+ - Device: [e.g. iPhone6]
+ - OS: [e.g. iOS8.1]
+ - Browser [e.g. stock browser, safari]
+ - Version [e.g. 22]
+
+**Additional context**
+Add any other context about the problem here.

.github/ISSUE_TEMPLATE/custom.md

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+---
+name: Custom issue template
+about: Describe this issue template's purpose here.
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+

.github/ISSUE_TEMPLATE/feature_request.md

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+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

.github/workflows/django.yml

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+name: Django CI
+
+on:
+  push:
+    branches: [ master ]
+  pull_request:
+    branches: [ master ]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      max-parallel: 4
+      matrix:
+        python-version: [3.6, 3.7, 3.8]
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v1
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install Dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt
+    - name: Run Tests
+      run: |
+        python manage.py test

iids/analyzer/Feature_Extraction.py

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+#importing libraries
+import numpy as np
+import pandas as pd 
+import os
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+print(os.listdir("/"))
+
+#change it according ur directory
+df = pd.read_csv('/Intelligent-Intrusion-Detection-System/datasets/kddcup.data.gz')
+print(df.shape())
+
+df.columns =["duration","protocol_type","service","flag","src_bytes",
+    "dst_bytes","land","wrong_fragment","urgent","hot","num_failed_logins",
+    "logged_in","num_compromised","root_shell","su_attempted","num_root",
+    "num_file_creations","num_shells","num_access_files","num_outbound_cmds",
+    "is_host_login","is_guest_login","count","srv_count","serror_rate",
+    "srv_serror_rate","rerror_rate","srv_rerror_rate","same_srv_rate",
+    "diff_srv_rate","srv_diff_host_rate","dst_host_count","dst_host_srv_count",
+    "dst_host_same_srv_rate","dst_host_diff_srv_rate","dst_host_same_src_port_rate",
+    "dst_host_srv_diff_host_rate","dst_host_serror_rate","dst_host_srv_serror_rate",
+    "dst_host_rerror_rate","dst_host_srv_rerror_rate","label"]
+
+X = df.iloc[:,:41]
+y = df.iloc[:,-1]
+X.head()
+
+#Pre-processing of categorial data columns
+from sklearn import preprocessing
+le = preprocessing.LabelEncoder()
+df['protocol_type'] = le.fit_transform(df['protocol_type'])
+df['service']= le.fit_transform(df['service'])
+df['flag'] = le.fit_transform(df['flag'])
+
+#Splitting of data
+from sklearn.model_selection import train_test_split
+X_train,X_test,y_train,y_test = train_test_split(X,y,random_state = 42,test_size = 0.3) 
+
+#Scaling of data
+from sklearn.preprocessing import MinMaxScaler
+scaler = MinMaxScaler(feature_range=(0, 1))
+X_train_scaled = scaler.fit_transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+from sklearn.feature_selection import RFECV
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import StratifiedKFold
+
+#U can easily change the model by just replacing here
+from sklearn.ensemble import RandomForestClassifier
+model1 = RandomForestClassifier(n_estimators=100, max_depth=2,random_state=0,class_weight='balanced')
+
+rfecv = RFECV(estimator=model1, step=1, cv=StratifiedKFold(2), scoring='accuracy' )
+rfecv.fit(X_train_scaled,y_train)
+
+
+print('Optimal number of features: {}'.format(rfecv.n_features_))
+
+#Feature Selection plot
+plt.figure(figsize=(16, 9))
+plt.title('Recursive Feature Elimination with Cross-Validation', fontsize=18, fontweight='bold', pad=20)
+plt.xlabel('Number of features selected', fontsize=14, labelpad=20)
+plt.ylabel('% Correct Classification', fontsize=14, labelpad=20)
+plt.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_, color='#303F9F', linewidth=3)
+
+plt.show()
+
+dset = pd.DataFrame()
+dset['attr'] = X.columns
+dset['importance'] = rfecv.estimator_.feature_importances_
+dset = dset.sort_values(by='importance', ascending=False)
+
+#Feature Ranking plot
+plt.figure(figsize=(16, 14))
+plt.barh(y=dset['attr'], width=dset['importance'], color='#1976D2')
+plt.title('RFECV - Feature Importances', fontsize=20, fontweight='bold', pad=20)
+plt.xlabel('Importance', fontsize=14, labelpad=20)
+plt.show()
+

iids/analyzer/model_predictions/Adaboost.py

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+
+import numpy as np
+import pandas as pd 
+import os
+print(os.listdir("/"))
+
+df = pd.read_csv('/mnt/d/GSOC/Intelligent-Intrusion-Detection-System/datasets/kddcup.data.gz')
+##Change according to the directory of the cloned repo w.r.t dataset location.
+
+df.columns =["duration","protocol_type","service","flag","src_bytes",
+    "dst_bytes","land","wrong_fragment","urgent","hot","num_failed_logins",
+    "logged_in","num_compromised","root_shell","su_attempted","num_root",
+    "num_file_creations","num_shells","num_access_files","num_outbound_cmds",
+    "is_host_login","is_guest_login","count","srv_count","serror_rate",
+    "srv_serror_rate","rerror_rate","srv_rerror_rate","same_srv_rate",
+    "diff_srv_rate","srv_diff_host_rate","dst_host_count","dst_host_srv_count",
+    "dst_host_same_srv_rate","dst_host_diff_srv_rate","dst_host_same_src_port_rate",
+    "dst_host_srv_diff_host_rate","dst_host_serror_rate","dst_host_srv_serror_rate",
+    "dst_host_rerror_rate","dst_host_srv_rerror_rate","label"]
+
+
+from sklearn.preprocessing import LabelEncoder,OneHotEncoder
+le = LabelEncoder()
+df['protocol_type'] = le.fit_transform(df['protocol_type'])
+df['service']= le.fit_transform(df['service'])
+df['flag'] = le.fit_transform(df['flag'])
+
+X = df.iloc[:,:41]
+y = df.iloc[:,-1]
+
+from sklearn.model_selection import train_test_split
+X_train,X_test,y_train,y_test = train_test_split(X,y,random_state = 42,test_size = 0.4)
+
+from sklearn.preprocessing import MinMaxScaler
+scaler = MinMaxScaler(feature_range=(0, 1))
+X_train_scaled = scaler.fit_transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+
+
+from sklearn.ensemble import AdaBoostClassifier
+ab_model = AdaBoostClassifier()
+ab_model.fit(X_train,y_train)
+
+score = ab_model.score(X_test, y_test)
+print(f'The score by AdaBoostClassifier is {score}')
+

iids/analyzer/model_predictions/Decision_tree.py

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+
+import numpy as np
+import pandas as pd 
+import os
+print(os.listdir("/"))
+
+df = pd.read_csv('/mnt/d/GSOC/Intelligent-Intrusion-Detection-System/datasets/kddcup.data.gz')
+##Change according to the directory of the cloned repo w.r.t dataset location.
+
+df.columns =["duration","protocol_type","service","flag","src_bytes",
+    "dst_bytes","land","wrong_fragment","urgent","hot","num_failed_logins",
+    "logged_in","num_compromised","root_shell","su_attempted","num_root",
+    "num_file_creations","num_shells","num_access_files","num_outbound_cmds",
+    "is_host_login","is_guest_login","count","srv_count","serror_rate",
+    "srv_serror_rate","rerror_rate","srv_rerror_rate","same_srv_rate",
+    "diff_srv_rate","srv_diff_host_rate","dst_host_count","dst_host_srv_count",
+    "dst_host_same_srv_rate","dst_host_diff_srv_rate","dst_host_same_src_port_rate",
+    "dst_host_srv_diff_host_rate","dst_host_serror_rate","dst_host_srv_serror_rate",
+    "dst_host_rerror_rate","dst_host_srv_rerror_rate","label"]
+
+
+from sklearn.preprocessing import LabelEncoder,OneHotEncoder
+le = LabelEncoder()
+df['protocol_type'] = le.fit_transform(df['protocol_type'])
+df['service']= le.fit_transform(df['service'])
+df['flag'] = le.fit_transform(df['flag'])
+
+X = df.iloc[:,:41]
+y = df.iloc[:,-1]
+
+from sklearn.model_selection import train_test_split
+X_train,X_test,y_train,y_test = train_test_split(X,y,random_state = 42,test_size = 0.4)
+
+from sklearn.preprocessing import MinMaxScaler
+scaler = MinMaxScaler(feature_range=(0, 1))
+X_train_scaled = scaler.fit_transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+from sklearn.tree import DecisionTreeClassifier
+dt_model = DecisionTreeClassifier(max_depth=5)
+dt_model.fit(X_train,y_train)
+
+score = dt_model.score(X_test, y_test)
+print(f'The score by DecisionTreeClassifier is {score}')
+

iids/analyzer/model_predictions/knn.py

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+
+import numpy as np
+import pandas as pd 
+import os
+print(os.listdir("/"))
+
+df = pd.read_csv('/mnt/d/GSOC/Intelligent-Intrusion-Detection-System/datasets/kddcup.data.gz')
+##Change according to the directory of the cloned repo w.r.t dataset location.
+
+df.columns =["duration","protocol_type","service","flag","src_bytes",
+    "dst_bytes","land","wrong_fragment","urgent","hot","num_failed_logins",
+    "logged_in","num_compromised","root_shell","su_attempted","num_root",
+    "num_file_creations","num_shells","num_access_files","num_outbound_cmds",
+    "is_host_login","is_guest_login","count","srv_count","serror_rate",
+    "srv_serror_rate","rerror_rate","srv_rerror_rate","same_srv_rate",
+    "diff_srv_rate","srv_diff_host_rate","dst_host_count","dst_host_srv_count",
+    "dst_host_same_srv_rate","dst_host_diff_srv_rate","dst_host_same_src_port_rate",
+    "dst_host_srv_diff_host_rate","dst_host_serror_rate","dst_host_srv_serror_rate",
+    "dst_host_rerror_rate","dst_host_srv_rerror_rate","label"]
+
+
+from sklearn.preprocessing import LabelEncoder,OneHotEncoder
+le = LabelEncoder()
+df['protocol_type'] = le.fit_transform(df['protocol_type'])
+df['service']= le.fit_transform(df['service'])
+df['flag'] = le.fit_transform(df['flag'])
+
+X = df.iloc[:,:41]
+y = df.iloc[:,-1]
+
+from sklearn.model_selection import train_test_split
+X_train,X_test,y_train,y_test = train_test_split(X,y,random_state = 42,test_size = 0.4)
+
+from sklearn.preprocessing import MinMaxScaler
+scaler = MinMaxScaler(feature_range=(0, 1))
+X_train_scaled = scaler.fit_transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+# n_neighbors are set to 3, after getting max accuracy
+from sklearn.neighbors import KNeighborsClassifier
+knn_model = KNeighborsClassifier(n_neighbors=3)
+knn_model.fit(X_train,y_train)
+
+score = knn_model.score(X_test, y_test)
+print(f'The score by KNClassifier is {score}')

iids/analyzer/model_predictions/random_forest.py

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+
+
+import numpy as np
+import pandas as pd 
+import os
+print(os.listdir("/"))
+
+df = pd.read_csv('/mnt/d/GSOC/Intelligent-Intrusion-Detection-System/datasets/kddcup.data.gz')
+##Change according to the directory of the cloned repo w.r.t dataset location.
+
+df.columns =["duration","protocol_type","service","flag","src_bytes",
+    "dst_bytes","land","wrong_fragment","urgent","hot","num_failed_logins",
+    "logged_in","num_compromised","root_shell","su_attempted","num_root",
+    "num_file_creations","num_shells","num_access_files","num_outbound_cmds",
+    "is_host_login","is_guest_login","count","srv_count","serror_rate",
+    "srv_serror_rate","rerror_rate","srv_rerror_rate","same_srv_rate",
+    "diff_srv_rate","srv_diff_host_rate","dst_host_count","dst_host_srv_count",
+    "dst_host_same_srv_rate","dst_host_diff_srv_rate","dst_host_same_src_port_rate",
+    "dst_host_srv_diff_host_rate","dst_host_serror_rate","dst_host_srv_serror_rate",
+    "dst_host_rerror_rate","dst_host_srv_rerror_rate","label"]
+
+
+from sklearn.preprocessing import LabelEncoder,OneHotEncoder
+le = LabelEncoder()
+df['protocol_type'] = le.fit_transform(df['protocol_type'])
+df['service']= le.fit_transform(df['service'])
+df['flag'] = le.fit_transform(df['flag'])
+
+X = df.iloc[:,:41]
+y = df.iloc[:,-1]
+
+from sklearn.model_selection import train_test_split
+X_train,X_test,y_train,y_test = train_test_split(X,y,random_state = 42,test_size = 0.4)
+
+from sklearn.preprocessing import MinMaxScaler
+scaler = MinMaxScaler(feature_range=(0, 1))
+X_train_scaled = scaler.fit_transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+from sklearn.ensemble import RandomForestClassifier
+rf_model = RandomForestClassifier(n_estimators=100, max_depth=2,random_state=0,class_weight='balanced')
+rf_model.fit(X_train,y_train)
+
+score = rf_model.score(X_test, y_test)
+print(f'The score by Random Forest is {score}')

iids/analyzer/serializers.py

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+#Serializers for input
+from rest_framework import serializers
+from rest_framework.renderers import JSONRenderer
+
+class EndpointSerializer(serializers.ModelSerializer):
+    read_only_fields = ("model_name", "param1", "param2", "param3")
+
+
+serializer = EndpointSerializer(data)
+json = JSONRenderer().render(serializer.data)
+
+#Deserializinf of data
+from StringIO import StringIO
+from rest_framework.parsers import JSONParser
+
+stream = StringIO(json)
+data = JSONParser().parse(stream)
+