Python network data visualization
DNS Data
If you modify the previous code slightly, you can print DNS lookups. Instead of pkt[IP].src
, you use pkt.haslayer(DNS)
. Again, you create an empty list and append to it; then use Scapy to check for DNS
and affirm that the packet is a query (with
as the QR type) and not a response, which would have a 1
in the QR field. (Listing 4). Again, count and print (Figure 3).
Listing 4
DNS Lookups
01 from scapy.all import * 02 from collections import Counter 03 import plotly 04 05 packets = rdpcap("example.pcap") 06 07 lookups=[] 08 for pkt in packets: 09 if IP in pkt: 10 try: 11 if pkt.haslayer(DNS) and pkt.getlayer(DNS).qr == 0: 12 lookup=(pkt.getlayer(DNS).qd.qname).decode("utf-8") 13 lookups.append(lookup) 14 except: 15 pass 16 17 cnt=Counter() 18 for lookup in lookups: 19 cnt[lookup] += 1 20 21 xData=[] 22 yData=[] 23 24 for lookup, count in cnt.most_common(): 25 xData.append(lookup) 26 yData.append(count) 27 28 plotly.offline.plot({ 29 "data":[plotly.graph_objs.Bar(x=xData, y=yData)] })
Packets Through Time
At first glance, plotting packets over time is an easy problem to solve. Just grab the packet and use pkt[IP].len
; however, if you have a reasonable data collection, you will almost always print data of 1500 bytes (the default MTU in most routers), which produces an uninteresting graph. With the pandas Python data analysis library, you can make human-readable dates from the packets, which are in epoch (unix time) and then bin the date and time. (Listing 5). First, you have to install pandas:
sudo pip3 install pandas
Listing 5
Using the pandas Library
01 from scapy.all import * 02 import plotly 03 from datetime import datetime 04 import pandas as pd 05 06 #Read the packets from file 07 packets = rdpcap('example.pcap') 08 09 #Lists to hold packet info 10 pktBytes=[] 11 pktTimes=[] 12 13 #Read each packet and append to the lists. 14 for pkt in packets: 15 if IP in pkt: 16 try: 17 pktBytes.append(pkt[IP].len) 18 19 #First we need to covert Epoch time to a datetime 20 pktTime=datetime.fromtimestamp(pkt.time) 21 #Then convert to a format we like 22 pktTimes.append(pktTime.strftime("%Y-%m-%d %H:%M:%S.%f")) 23 24 except: 25 pass 26 27 #This converts list to series 28 bytes = pd.Series(pktBytes).astype(int) 29 30 #Convert the timestamp list to a pd date_time 31 times = pd.to_datetime(pd.Series(pktTimes).astype(str), errors='coerce') 32 33 #Create the dataframe 34 df = pd.DataFrame({"Bytes": bytes, "Times":times}) 35 36 #set the date from a range to an timestamp 37 df = df.set_index('Times') 38 39 #Create a new dataframe of 2 second sums to pass to plotly 40 df2=df.resample('2S').sum() 41 print(df2) 42 43 #Create the graph 44 plotly.offline.plot({ 45 "data":[plotly.graph_objs.Scatter(x=df2.index, y=df2['Bytes'])], 46 "layout":plotly.graph_objs.Layout(title="Bytes over Time ", 47 xaxis=dict(title="Time"), 48 yaxis=dict(title="Bytes"))})
As before, you create lists to hold data (lines 10-11) and, this time, store the length of bytes in a packet and the timestamp of the packet. Next, you will get the length of the packet with (pkt[IP].len)
and convert the time using datetime
(lines 13-25). With the pandas library, you convert the list to a pandas series and then convert to a timestamp, create the pandas dataframe, and organize the data in to two-second bins (lines 21-41). Now you can use Plotly to print the chart. Lines 46-48 add a title with graph_objs.Layout
. The time (x) axis was created during resampling, with the y axis data in bytes (Figure 4).
Conclusion
You can do much more with Scapy, such as grab URLs, pull files from PCAPs, and more; by slightly modifying the examples in this article, you can add more features. The open source PacketExaminer project offers a pre-made harness for PCAP analysis [2], and all of the code in these examples can be found in the training
folder of the repo. If you have any questions, just let me know at joe.mcmanus@canonical.com.
Infos
- Scapy: https://scapy.net
- PacketExaminer project on GitHub: https://github.com/joemcmanus/packetexaminer
« Previous 1 2
Buy this article as PDF
(incl. VAT)
Buy Linux Magazine
Direct Download
Read full article as PDF:
Price $2.95
News
-
Another New Linux Laptop has Arrived
Slimbook has released a monster of a Linux gaming laptop.
-
Mozilla VPN Now Available for Linux
The promised subscription-based VPN service from Mozilla is now available for the Linux platform.
-
Wayland and New App Menu Coming to KDE
The 2021 roadmap for the KDE desktop environment includes some exciting features and improvements.
-
Deepin 20.1 has Arrived
Debian-based Deepin 20.1 has been released with some interesting new features.
-
CloudLinux Commits Over 1 Million Dollars to CentOS Replacement
An open source, drop-in replacement for CentOS is on its way.
-
Linux Mint 20.1 Beta has Been Released
The first beta of Linux Mint, Ulyssa, is now available for downloading.
-
Manjaro Linux 20.2 has Been Unleashed
The latest iteration of Manjaro Linux has been released with a few interesting new features.
-
Patreon Project Looks to Bring Linux to Apple Silicon
Developer Hector Martin has created a patreon page to fund his work on developing a port of Linux for Apple Silicon Macs.
-
A New Chrome OS-Like Ubuntu Remix is Now Available
Ubuntu Web looks to be your Chrome OS alternative.
-
System76 Refreshes the Galago Pro Laptop
Linux hardware maker has revamped one of their most popular laptops.