def check(url, clf): # url=sys.argv[1] fT = time.time() features_test = features_extraction.main(url) feT = time.time() features_test = np.array(features_test).reshape((1, -1)) prT = time.time() pred = clf.predict(features_test) # prob=clf.predict_proba(features_test) preT = time.time() # pred=[1] print("\n") print("Feature extraction Time = " + str(feT - fT)) print("Prediction Time = " + str(preT - prT)) print("Total Time = " + str(preT - fT)) print("\n") if int(pred[0]) == 1: print(url) print("This is a safe website.") return False # return str(url)+" is a safe website." elif int(pred[0]) == -1: print(url) print("This is a phishing website..!") return True
def check_website(request): """ """ # import ipdb; ipdb.set_trace() url = request.GET.get('url') #url='https://www.google.com/' features_test = features_extraction.main(url) # Due to updates to scikit-learn, we now need a 2D array as a parameter to the predict function. features_test = np.array(features_test).reshape((1, -1)) # import ipdb; ipdb.set_trace() clf = joblib.load(os.getcwd() + '/backend/classifier/random_forest.pkl') pred = clf.predict(features_test) # Print the probability of prediction (if needed) # prob = clf.predict_proba(features_test) # print 'Features=', features_test, 'The predicted probability is - ', prob, 'The predicted label is - ', pred # print "The probability of this site being a phishing website is ", features_test[0]*100, "%" if int(pred[0]) == 1: # print "The website is safe to browse" print("SAFE") return HttpResponse("SAFE") elif int(pred[0]) == -1: print(" PHISING") # print "The website has phishing features. DO NOT VISIT!" return HttpResponse('Phising')
def main(url): #url = sys.argv[1] features_test = features_extraction.main(url) # Due to updates to scikit-learn, we now need a 2D array as a parameter to the predict function. features_test = np.array(features_test).reshape((1, -1)) clf = joblib.load('classifier/random_forest.pkl') pred = clf.predict(features_test) # Print the probability of prediction (if needed) # prob = clf.predict_proba(features_test) # print 'Features=', features_test, 'The predicted probability is - ', prob, 'The predicted label is - ', pred # print "The probability of this site being a phishing website is ", features_test[0]*100, "%" if int(pred[0]) == 1: # print "The website is safe to browse" print("SAFE") return "SAFE" elif int(pred[0]) == -1: # print "The website has phishing features. DO NOT VISIT!" print("PHISHING") return "MALICIOUS"
def get_prediction_from_url(test_url): features_test = features_extraction.main(test_url) # Due to updates to scikit-learn, we now need a 2D array as a parameter to the predict function. features_test = np.array(features_test).reshape((1, -1)) clf = joblib.load(LOCALHOST_PATH + DIRECTORY_NAME + '/classifier/random_forest.pkl') print(clf)
def get_prediction_from_url(test_url,html): features_test = features_extraction.main(test_url,html) features_test = np.array(features_test).reshape((1, -1)) #localPath = os.getcwd() clf = joblib.load(WORKING_PATH + '/classifier/random_forest1.pkl') pred = clf.predict(features_test) return int(pred[0])
def get_prediction_from_url(test_url): features_test = features_extraction.main(test_url) features_test = np.array(features_test).reshape((1, -1)) clf = joblib.load(LOCALHOST_PATH + DIRECTORY_NAME + '/classifier/random_forest.pkl') pred = clf.predict(features_test) return int(pred[0])
def get_prediction_from_url(test_url): features_test = features_extraction.main(test_url) # we now need a 2D array as a parameter to the predict function. features_test = np.array(features_test).reshape((1, -1)) clf = joblib.load(LOCALHOST_PATH + '/classifier/finalized_model.sav') pred = clf.predict(features_test) return int(pred[0])
def predict(test_url): features_test = features_extraction.main(test_url) features_test = np.array(features_test).reshape((1, -1)) clf = joblib.load(MODEL_PATH) pred = clf.predict(features_test) print(test_url) print(pred) return int(pred[0])
def main(): url = "http://www.spit.ac.in" features_test = features_extraction.main(url) clf = joblib.load('classifier/extratree.pkl') features_test = [features_test] pred = clf.predict(features_test) prob = clf.predict_proba(features_test) # print 'Features=', features_test, 'The predicted probability is - ', prob, 'The predicted label is - ', pred # print "The probability of this site being a phishing website is ", features_test[0]*100, "%" if int(pred[0]) == 1: # print "The website is safe to browse" print("SAFE") elif int(pred[0]) == -1: # print "The website has phishing features. DO NOT VISIT!" print("PHISHING")
def main(): url = sys.argv[1] features_test = features_extraction.main(url) clf = joblib.load('classifier/random_forest.pkl') pred = clf.predict(features_test) prob = clf.predict_proba(features_test) # print 'Features=', features_test, 'The predicted probability is - ', prob, 'The predicted label is - ', pred # print "The probability of this site being a phishing website is ", features_test[0]*100, "%" if int(pred[0]) == 1: # print "The website is safe to browse" print "SAFE" elif int(pred[0]) == -1: # print "The website has phishing features. DO NOT VISIT!" print "PHISHING"
def main(): url = sys.argv[1] features_test = features_extraction.main(url) # 2d array per scikit-learn features_test = np.array(features_test).reshape((1, -1)) clf = joblib.load(LOCALHOST_PATH + DIRECTORY_NAME + '/classifier/random_forest_new_4.pkl') prediction = clf.predict(features_test) prediction_int = int(prediction[0]) probability = clf.predict_proba(features_test) respond_data = { "features": features_test.tolist(), "probability": probability.tolist(), "prediction": prediction_int, "url": url } json_respond_data = json.dumps(respond_data) print(json_respond_data)
def main(): url = sys.argv[1] #url='https://www.google.com/' features_test = features_extraction.main(url) # Due to updates to scikit-learn, we now need a 2D array as a parameter to the predict function. features_test = np.array(features_test).reshape((1, -1)) clf = joblib.load( 'C://xampp//htdocs//webc3//classifier//random_forest.pkl') pred = clf.predict(features_test) # Print the probability of prediction (if needed) # prob = clf.predict_proba(features_test) # print 'Features=', features_test, 'The predicted probability is - ', prob, 'The predicted label is - ', pred # print "The probability of this site being a phishing website is ", features_test[0]*100, "%" if int(pred[0]) == 1: # print "The website is safe to browse" print("SAFE") elif int(pred[0]) == -1: # print "The website has phishing features. DO NOT VISIT!" print("PHishing")