import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier;
from sklearn.model_selection import train_test_split;
from sklearn.svm import SVC
col_names = [“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”]
test_fields ={“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”}

trainsRows = []
testRows=[]
alpha=0.22;
t=0.00000000005;
A_index=[];
B_index=[];

reading csv file

print(“1”)
trainFilename=“KDD Train+q.csv”;
testFilename=“KDD Test+w.csv”;

trainFilename=“KDD Train+.csv”;

testFilename=“KDD Test+.csv”;

train=pd.read_csv(“F:\DDos-Detection-via-RDF-SVM-master\”+trainFilename);
test=pd.read_csv(“F:\DDos-Detection-via-RDF-SVM-master\”+testFilename);
predictor_Vars=[“duration”,“src_bytes”,“dst_bytes”,“logged_in”,“hot”,“num_access_files”,“srv_count”,“is_guest_login”]
train[“label”]=train[“label”].fillna(“1”);
test[“label”]=test[“label”].fillna(“1”);
train[“num_failed_logins”]=train[“num_failed_logins”].fillna(“0”);
test[“num_failed_logins”]=test[“num_failed_logins”].fillna(“0”);
train[“num_access_files”]=train[“num_access_files”].fillna(“0”);
test[“num_access_files”]=test[“num_access_files”].fillna(“0”);
train[“srv_count”]=train[“srv_count”].fillna(“0”);
test[“srv_count”]=test[“srv_count”].fillna(“0”);
train[“is_guest_login”]=train[“is_guest_login”].fillna(“0”);
test[“is_guest_login”]=test[“is_guest_login”].fillna(“0”);
X,Y=train[predictor_Vars],train.label;
clf = RandomForestClassifier(n_estimators=10)
clf.fit(X,Y)
predictions=clf.predict(test[predictor_Vars]);

print(X)
max_feature=0.0;
best_feature="";
for feature in zip(predictor_Vars, clf.feature_importances_):
print(feature)
if feature[1]>float(alpha):
A_index.append(feature[0]);
if max_feature<feature[1]:
best_feature=feature[0];
max_feature=feature[1];
else:
B_index.append(feature[0]);
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33)
sum=0.00;
for xi in B_index:
predict_a=[str(best_feature)]
clfA = SVC(kernel=‘linear’,C=1.0,gamma=0.2,cache_size=7000)
clfB = SVC(kernel=‘linear’,C=1.0,gamma=0.2,cache_size=7000)
print(“SVC initialization done”);

clfA.fit(X_train[predictor_Vars],y_train)
print("SVM on A is done........")
clfB.fit(X_train[predictor_Vars],y_train)
print("SVM on B is done........")

# X_test[best_feature]= np.array(X_test[best_feature]).reshape(1, -1);

print X_test[best_feature];
predictA=clfA.predict(X_test[best_feature]);

predictB=clfB.predict(X_test[best_feature]);
for feature in zip(best_feature, clfA.feature_importances_):
    print(feature)
    print("\n");
    sum=sum+feature[1];
for feature in zip(xi, clfB.feature_importances_):
    print(feature)
    sum=sum-feature[1];
mean_sum=float(sum/len(X_train));
if mean_sum>t:
    A_index.append(xi); 

print(A_index);
#predictions1=clf.predict(test[predictor_Vars]);
#print(predictions1)