Browse Source

a little cleaning

Nikolaos Alexopoulos 7 years ago
parent
commit
c4ca937a1c

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Figs/chromium-browser_all.png


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Figs/firefox_all.png


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Figs/iceape_all.png


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Figs/icedove_all.png


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Figs/linux_all.png


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Figs/mysql_all.png


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Figs/openjdk.png


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Figs/openssl_all.png


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Figs/php5_all.png


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Figs/power_law.png


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Figs/wireshark_all.png


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Figs/wordpress_all.png


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Figs/xen_all.png


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Figs/xulrunner_all.png


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all_packages_test.h5


+ 1 - 2
apt-sec.py

@@ -887,14 +887,13 @@ state['vendor'] = 'debian'
 if action == 'update':
     (dsatable, src2dsa, dsa2cve, cvetable, src2month, src2sloccount, src2pop, src2deps) = load_DBs()
 #    loadsha1lists()
-    aptsec_update(state,config, dsatable, client, src2dsa, dsa2cve, src2month, cvetable, pkg_with_cvss)
+#    aptsec_update(state,config, dsatable, client, src2dsa, dsa2cve, src2month, cvetable, pkg_with_cvss)
 #    save_sha1lists()
 #    getslocs(src2dsa, src2sloccount)
 #   getpop(src2dsa, src2pop)
 #    getdeps(src2dsa, src2deps)
     save_DBs(dsatable, src2dsa, dsa2cve, cvetable, src2month, src2sloccount, src2pop, src2deps)
     save_state(state)
-#    ml.predict(src2month)
     lstm.predict(src2month)
 #    print(pkg_with_cvss['linux'])
     

+ 0 - 0
log.file


+ 78 - 48
lstm_reg.py

@@ -25,10 +25,15 @@ def predict(src2month):
     pkg_num = len(src2month)
     training_num = len(src2month['linux'])
 
+    trainXdict = dict()
+    trainYdict = dict()
+    testXdict = dict()
+    testYdict = dict()
+
     look_back = 4
     # create the LSTM network
     model = Sequential()
-    model.add(LSTM(64, input_dim=look_back, activation ='relu', dropout_W =0.1, dropout_U =0.1))
+    model.add(LSTM(32, input_dim=look_back, activation ='relu', dropout_W =0.1, dropout_U =0.1))
 #    model.add(Dense(12, init='uniform', activation='relu'))
 #    model.add(Dense(8, init='uniform', activation='relu'))
 #    model.add(Dense(1, init='uniform', activation='sigmoid'))
@@ -40,9 +45,52 @@ def predict(src2month):
 
     flag = True
 ###################################################################################################    
-    for pkg_name in ['icedove', 'linux', 'mysql', 'xulrunner', 'wireshark', 'firefox', 'openjdk', 'php5', 'iceape', 'wordpress', 'xen', 'openssl', 'chromium-browser']:
-#    for pkg_name in ['chromium-browser']:
+    for pkg_name in src2month:
+#    for pkg_name in ['icedove', 'mysql', 'xulrunner', 'wireshark', 'firefox', 'openjdk', 'php5', 'iceape', 'wordpress', 'xen', 'openssl', 'chromium-browser', 'linux']:
+#    for pkg_name in ['linux']:
         pkg_num = len(src2month)
+        dataset = src2month[pkg_name]
+
+        if sum(dataset)>20:
+
+            dataset = pandas.rolling_mean(dataset, window=12)
+            dataset = dataset[12:]
+
+            # normalize the dataset
+            dataset = scaler.fit_transform(dataset)
+
+            train_size = int(len(dataset) * 0.80)
+            test_size = len(dataset) - train_size
+            train, test = dataset[0:train_size], dataset[train_size:len(dataset)]
+            print(len(train), len(test))
+
+            # reshape into X=t and Y=t+1
+            trainX, trainY = create_dataset(train, look_back)
+            testX, testY = create_dataset(test, look_back)
+
+            # reshape input to be [samples, time steps, features]
+            trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
+            testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))
+
+            # save to dict for later
+            trainXdict[pkg_name], trainYdict[pkg_name] = trainX, trainY
+            testXdict[pkg_name], testYdict[pkg_name] = testX, testY
+    
+            # fit the LSTM network
+            model.fit(trainX, trainY, nb_epoch=10, batch_size=1, verbose=2)
+        
+    
+###################################################################################################
+
+
+    model.save('all_packages_test.h5')
+
+
+    for pkg_name in ['icedove', 'mysql', 'xulrunner', 'wireshark', 'firefox', 'openjdk', 'php5', 'iceape', 'wordpress', 'xen', 'openssl', 'chromium-browser', 'linux']:
+    
+        trainX, trainY = trainXdict[pkg_name], trainYdict[pkg_name]
+        testX, testY = testXdict[pkg_name], testYdict[pkg_name]
+
         dataset = src2month[pkg_name]
         dataset = pandas.rolling_mean(dataset, window=12)
         dataset = dataset[12:]
@@ -50,52 +98,34 @@ def predict(src2month):
         # normalize the dataset
         dataset = scaler.fit_transform(dataset)
 
-        train_size = int(len(dataset) * 0.80)
-        test_size = len(dataset) - train_size
-        train, test = dataset[0:train_size], dataset[train_size:len(dataset)]
-        print(len(train), len(test))
 
-        # reshape into X=t and Y=t+1
-        trainX, trainY = create_dataset(train, look_back)
-        testX, testY = create_dataset(test, look_back)
-    
-        # reshape input to be [samples, time steps, features]
-        trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
-        testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))
 
-        # fit the LSTM network
-        model.fit(trainX, trainY, nb_epoch=5, batch_size=1, verbose=2)
-        
-    
-###################################################################################################
+        # make predictions
+        trainPredict = model.predict(trainX)
+        testPredict = model.predict(testX)
+        # invert predictions
+        trainPredict = scaler.inverse_transform(trainPredict)
+        trainY = scaler.inverse_transform([trainY])
+        testPredict = scaler.inverse_transform(testPredict)
+        testY = scaler.inverse_transform([testY])
+        # calculate root mean squared error
+        print('Package: ' + pkg_name)
+        trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
+        print('Train Score: %.2f RMSE' % (trainScore))
+        testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
+        print('Test Score: %.2f RMSE' % (testScore))
 
 
-    # make predictions
-    trainPredict = model.predict(trainX)
-    testPredict = model.predict(testX)
-    print(type(testPredict))
-    # invert predictions
-    trainPredict = scaler.inverse_transform(trainPredict)
-    trainY = scaler.inverse_transform([trainY])
-    testPredict = scaler.inverse_transform(testPredict)
-    testY = scaler.inverse_transform([testY])
-    # calculate root mean squared error
-    trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
-    print('Train Score: %.2f RMSE' % (trainScore))
-    testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
-    print('Test Score: %.2f RMSE' % (testScore))
-
-
-    # shift train predictions for plotting
-    trainPredictPlot = numpy.empty_like(dataset)
-    trainPredictPlot[:] = numpy.nan
-    trainPredictPlot[look_back:len(trainPredict)+look_back] = trainPredict[:, 0]
-    # shift test predictions for plotting
-    testPredictPlot = numpy.empty_like(dataset)
-    testPredictPlot[:] = numpy.nan
-    testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1] = testPredict[:, 0]
-    # plot baseline and predictions
-    plt.plot(scaler.inverse_transform(dataset))
-    plt.plot(trainPredictPlot)
-    plt.plot(testPredictPlot)
-    plt.show()
+        # shift train predictions for plotting
+        trainPredictPlot = numpy.empty_like(dataset)
+        trainPredictPlot[:] = numpy.nan
+        trainPredictPlot[look_back:len(trainPredict)+look_back] = trainPredict[:, 0]
+        # shift test predictions for plotting
+        testPredictPlot = numpy.empty_like(dataset)
+        testPredictPlot[:] = numpy.nan
+        testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1] = testPredict[:, 0]
+        # plot baseline and predictions
+        plt.plot(scaler.inverse_transform(dataset))
+        plt.plot(trainPredictPlot)
+        plt.plot(testPredictPlot)
+        plt.show()

+ 0 - 0
machine_learning.py → misc/arima.py


+ 5 - 6
powelaw.py → misc/powelaw.py

@@ -28,11 +28,10 @@ print(results.truncated_power_law.xmin)
 print(results.truncated_power_law.xmax)
 print(results.power_law.discrete)
 print(results.lognormal.mu)
-results.plot_pdf()
-results.power_law.plot_pdf()
-results.truncated_power_law.plot_pdf()
-results.lognormal.plot_pdf()
-plt.plot(results.data)
+results.plot_pdf(color = 'blue')
+results.power_law.plot_pdf(color = 'green')
+results.truncated_power_law.plot_pdf(color = 'red')
+#plt.plot(results.data)
 plt.show()
-R, p=results.distribution_compare('power_law','lognormal_positive')
+R, p=results.distribution_compare('power_law','exponential')
 print(R,p)

+ 0 - 0
power_law.csv → misc/power_law.csv


+ 0 - 0
test.py → misc/test.py


+ 0 - 0
timeseries.py → misc/timeseries.py


+ 9 - 0
notes

@@ -0,0 +1,9 @@
+* Meeting with Steffen Schulz 01.02.2017
+
+- Application scenario: system administrator choosing software
+- visualization is important
+- no code access is a reasonable assumption
+- Have a Discussion section about what we actually measure (vulnerabilities reported) and how the criticallity, popularity or other factors impact the number of vulnerabilities reported.
+
+-- Steffen requested more information on how neural networks work
+--CCS deadline (mid May) seems plausible

+ 0 - 1
out

@@ -1 +0,0 @@
-<configparser.ConfigParser object at 0x7ff9fc07c4e0>

+ 0 - 1203
output.test

@@ -1,1203 +0,0 @@
-<configparser.ConfigParser object at 0x7f3820b9e4e0>
-cadaver 1
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-libmailtools-perl 2
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-mojarra 1
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-bsdgames 2
-bnc 1
-radsecproxy 1
-pidgin 21
-smartlist 1
-xpcd 3
-mumble 2
-nsd 2
-xfstt 1
-wine 2
-libcommons-fileupload-java 4
-zlib 3
-psi 2
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-wordpress 78
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