Python Network Analysis with Snort: Traffic Monitoring, Detection, and Visualization

Snort is an open-source Intrusion Detection System (IDS) that monitors network traffic and detects abnormal behavior or attacks using rule-based matching. Python, a widely-used programming language, offers powerful data processing and analysis capabilities. By integrating the two, Python network analysis can be applied to process and analyze network traffic captured by Snort.

First, we need to install Snort and the corresponding Python modules, such as PyCap and py-idstools. The installation methods can be found in the official documentation or third-party tutorials and will not be detailed here.

Next, we can use the PyCap module to capture network traffic. Below is a simple sample code:

import pcapy

cap = pcapy.open_live("eth0", 65536, 1, 0)   # "eth0" is the network interface name
while True:
    (header, packet) = cap.next()
    print(packet)

This code opens a network interface named “eth0” and continuously prints the captured packets.

Then, we can use the py-idstools module to parse Snort rules and match them against the packets. Here’s an example 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 description

cap = pcapy.open_live("eth0", 65536, 1, 0)
while True:
    (header, packet) = cap.next()
    process_packet(packet)

This code loads each rule from the rules file into memory and matches it against each captured packet. If a rule matches, it prints the rule description.

Finally, we can use other Python libraries to analyze and visualize the traffic, such as using Matplotlib to create traffic statistics graphs:

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 captures 100,000 packets and uses the Counter class to calculate the frequency of each packet type. Then, Matplotlib is used to create a bar chart showing the proportion of different types of packets in the traffic.

In conclusion, by combining Snort and Python, we can quickly and conveniently analyze network traffic and detect abnormal behaviors or attacks on the network. With additional Python libraries, we can perform more advanced data analysis and visualization.