add 9-2.py

code/9-2.py

@@ -0,0 +1,94 @@
+import re
+import numpy as np
+from sklearn import cross_validation
+from sklearn import datasets
+from sklearn import svm
+from sklearn.externals import  joblib
+from sklearn.metrics import classification_report
+from sklearn import metrics
+
+
+
+x = []
+y = []
+
+
+def get_len(url):
+    return len(url)
+
+def get_url_count(url):
+    if re.search('(http://)|(https://)', url, re.IGNORECASE) :
+        return 1
+    else:
+        return 0
+
+def get_evil_char(url):
+    return len(re.findall("[<>,\'\"/]", url, re.IGNORECASE))
+
+def get_evil_word(url):
+    return len(re.findall("(alert)|(script=)(%3c)|(%3e)|(%20)|(onerror)|(onload)|(eval)|(src=)|(prompt)",url,re.IGNORECASE))
+
+def get_last_char(url):
+    if re.search('/$', url, re.IGNORECASE) :
+        return 1
+    else:
+        return 0
+
+def get_feature(url):
+    return [get_len(url),get_url_count(url),get_evil_char(url),get_evil_word(url),get_last_char(url)]
+
+def do_metrics(y_test,y_pred):
+    print "metrics.accuracy_score:"
+    print metrics.accuracy_score(y_test, y_pred)
+    print "metrics.confusion_matrix:"
+    print metrics.confusion_matrix(y_test, y_pred)
+    print "metrics.precision_score:"
+    print metrics.precision_score(y_test, y_pred)
+    print "metrics.recall_score:"
+    print metrics.recall_score(y_test, y_pred)
+    print "metrics.f1_score:"
+    print metrics.f1_score(y_test,y_pred)
+
+def etl(filename,data,isxss):
+        with open(filename) as f:
+            for line in f:
+                f1=get_len(line)
+                f2=get_url_count(line)
+                f3=get_evil_char(line)
+                f4=get_evil_word(line)
+                data.append([f1,f2,f3,f4])
+                if isxss:
+                    y.append(1)
+                else:
+                    y.append(0)
+        return data
+
+etl('../data/xss-200000.txt',x,1)
+etl('../data/good-xss-200000.txt',x,0)
+#etl('xss-200000.txt',x,1)
+#etl('good-xss-200000.txt',x,0)
+
+
+
+x_train, x_test, y_train, y_test = cross_validation.train_test_split(x,y, test_size=0.4, random_state=0)
+
+clf = svm.SVC(kernel='linear', C=1).fit(x_train, y_train)
+
+y_pred = clf.predict(x_test)
+#print y_train
+#print y_pred
+#print y_test
+do_metrics(y_test, y_pred)
+
+#print clf.score(x_test, y_test)
+
+#joblib.dump(clf,"xss-svm-200000-module.m")
+
+'''
+with open("good-xss-200000.txt") as f:
+    for line in f:
+#clf.predict([[2., 2.]])
+        predict=clf.predict(get_feature(line))
+        if predict == 1:
+            print("maybe guest error xss %s") % (line)
+'''