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@@ -774,9 +774,9 @@ class Statistics:
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graphy.append(row[1])
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plt.autoscale(enable=True, axis='both')
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- plt.title("IP New Values Distribution")
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+ plt.title("IP Novelity Distribution")
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plt.xlabel('Timestamp')
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- plt.ylabel('New values count')
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+ plt.ylabel('Novel values count')
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plt.xlim([0, len(graphx)])
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plt.grid(True)
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width = 0.1
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@@ -791,7 +791,7 @@ class Statistics:
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plt.locator_params(axis='x', nbins=20)
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plt.bar(x, graphy, width, align='center', linewidth=1, color='red', edgecolor='red')
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- out = self.pcap_filepath.replace('.pcap', '_plot-interval-new-ip-dist' + file_ending)
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+ out = self.pcap_filepath.replace('.pcap', '_plot-interval-novel-ip-dist' + file_ending)
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plt.savefig(out, dpi=500)
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print("IP Standard Deviation:")
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@@ -810,9 +810,9 @@ class Statistics:
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graphy.append(row[1])
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plt.autoscale(enable=True, axis='both')
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- plt.title("TTL New Values Distribution")
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+ plt.title("TTL Novelity Distribution")
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plt.xlabel('Timestamp')
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- plt.ylabel('New values count')
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+ plt.ylabel('Novel values count')
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plt.xlim([0, len(graphx)])
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plt.grid(True)
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width = 0.1
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@@ -827,7 +827,7 @@ class Statistics:
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plt.locator_params(axis='x', nbins=20)
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plt.bar(x, graphy, width, align='center', linewidth=1, color='red', edgecolor='red')
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- out = self.pcap_filepath.replace('.pcap', '_plot-interval-new-ttl-dist' + file_ending)
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+ out = self.pcap_filepath.replace('.pcap', '_plot-interval-novel-ttl-dist' + file_ending)
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plt.savefig(out, dpi=500)
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print("TTL Standard Deviation:")
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@@ -847,9 +847,9 @@ class Statistics:
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graphy.append(row[1])
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plt.autoscale(enable=True, axis='both')
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- plt.title("ToS New Values Distribution")
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+ plt.title("ToS Novelity Distribution")
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plt.xlabel('Timestamp')
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- plt.ylabel('New values count')
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+ plt.ylabel('Novel values count')
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plt.xlim([0, len(graphx)])
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plt.grid(True)
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width = 0.1
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@@ -864,7 +864,7 @@ class Statistics:
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plt.locator_params(axis='x', nbins=20)
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plt.bar(x, graphy, width, align='center', linewidth=1, color='red', edgecolor='red')
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- out = self.pcap_filepath.replace('.pcap', '_plot-interval-new-tos-dist' + file_ending)
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+ out = self.pcap_filepath.replace('.pcap', '_plot-interval-novel-tos-dist' + file_ending)
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plt.savefig(out, dpi=500)
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print("ToS Standard Deviation:")
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@@ -884,9 +884,9 @@ class Statistics:
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graphy.append(row[1])
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plt.autoscale(enable=True, axis='both')
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- plt.title("Window Size New Values Distribution")
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+ plt.title("Window Size Novelity Distribution")
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plt.xlabel('Timestamp')
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- plt.ylabel('New values count')
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+ plt.ylabel('Novel values count')
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plt.xlim([0, len(graphx)])
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plt.grid(True)
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width = 0.1
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@@ -901,7 +901,7 @@ class Statistics:
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plt.locator_params(axis='x', nbins=20)
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plt.bar(x, graphy, width, align='center', linewidth=1, color='red', edgecolor='red')
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- out = self.pcap_filepath.replace('.pcap', '_plot-interval-new-win-size-dist' + file_ending)
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+ out = self.pcap_filepath.replace('.pcap', '_plot-interval-novel-win-size-dist' + file_ending)
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plt.savefig(out, dpi=500)
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# Calculate Standart Deviation
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@@ -924,9 +924,9 @@ class Statistics:
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graphy.append(row[1])
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plt.autoscale(enable=True, axis='both')
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- plt.title("MSS New Values Distribution")
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+ plt.title("MSS Novelity Distribution")
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plt.xlabel('Timestamp')
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- plt.ylabel('New values count')
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+ plt.ylabel('Novel values count')
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plt.xlim([0, len(graphx)])
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plt.grid(True)
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width = 0.1
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@@ -941,7 +941,7 @@ class Statistics:
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plt.locator_params(axis='x', nbins=20)
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plt.bar(x, graphy, width, align='center', linewidth=1, color='red', edgecolor='red')
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- out = self.pcap_filepath.replace('.pcap', '_plot-interval-new-mss-dist' + file_ending)
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+ out = self.pcap_filepath.replace('.pcap', '_plot-interval-novel-mss-dist' + file_ending)
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plt.savefig(out, dpi=500)
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# Calculate Standart Deviation
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@@ -976,7 +976,7 @@ class Statistics:
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# Aidmar
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def calculate_complement_packet_rates(self, pps):
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"""
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- Calculates the complement packet rates of the background traffic packet rates per interval.
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+ Calculates the complement packet rates of the background traffic packet rates for each interval.
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Then normalize it to maximum boundary, which is the input parameter pps
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:return: normalized packet rates for each time interval.
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