Snort is an open-source intrusion detection system (IDS) designed to monitor network traffic and detect abnormal behavior and attacks through rule matching. Python, a popular programming language, offers powerful data processing and analysis capabilities. By integrating Snort with Python, we can utilize Python to analyze the network traffic captured by Snort. Additionally, with the help of Matplotlib, we can visualize and better understand this data.
First, we need to install Snort and the corresponding Python modules, such as PyCap and py-idstools. You can refer to the official documentation or third-party tutorials for installation methods, which are not detailed here.
Next, we can use the PyCap module to capture network traffic. Below is a simple example code:
import pcapy
cap = pcapy.open_live("eth0", 65536, 1, 0) # eth0 is the interface name
while True:
(header, packet) = cap.next()
print(packet)
This code opens the network interface named “eth0” and continuously prints out the captured packets.
Next, we can use the py-idstools module to parse Snort rules and match them against the packets. Below is a sample code:
from idstools import Rule
from idstools import SnortRules
rules = SnortRules(rules_file='snort.rules') # snort.rules is the Snort rules file name
def process_packet(packet):
for rule in rules:
if rule.matches(packet):
print("Rule match: %s" % rule.signature) # signature is the rule name
cap = pcapy.open_live("eth0", 65536, 1, 0)
while True:
(header, packet) = cap.next()
process_packet(packet)
This code will load all the rules from the rules file into memory and match each captured packet against these rules. If there is a matching rule, it prints out the rule name.
Finally, we can use other Python libraries to analyze and visualize the traffic, such as using Matplotlib to plot traffic statistics charts.
import matplotlib.pyplot as plt
import numpy as np
from collections import Counter
packets = []
cap = pcapy.open_live("eth0", 65536, 1, 0)
while True:
(header, packet) = cap.next()
packets.append(packet)
if len(packets) >= 100000:
break
c = Counter(packets)
labels, values = zip(*c.items())
indexes = np.arange(len(labels))
plt.bar(indexes, values)
plt.xticks(indexes, labels, rotation='vertical')
plt.show()
This code will capture 100,000 packets and use the Counter class to tally the occurrence frequency of each packet. Then, we can use Matplotlib to plot a bar chart showing the proportion of different types of packets in traffic.
In conclusion, by integrating Snort and Python, we can quickly and conveniently analyze network traffic and detect abnormal behaviors and attacks in the network. With additional Python libraries, we can perform more data analysis and visualization.