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Home Columns Security Technology Using Snort for Network Intrusion Detection

Using Snort for Network Intrusion Detection

Published on: 2024-04-06 02:16:27Read Count: 61Subscription Count: 36

Real-time Detection: Intrusion Detection Based on Snort

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Detailed Explanation of Intrusion Detection System Based on Snort

Snort is a widely popular open-source network intrusion detection system (IDS), created by Martin Roesch in 1998. As a cross-platform tool, Snort can run on various operating systems such as Linux, Windows, etc. Its lightweight nature allows it to efficiently monitor network traffic, identify potential security threats, and provide real-time protection for network defense.

Snort operates based on a rule engine, which contains signatures of various known attack patterns. When network packets pass through Snort, it parses and matches these rules. If a packet matches a rule, Snort triggers an alert or takes other predefined actions. This enables Snort to detect various types of network attacks, such as DoS attacks, scans, backdoors, buffer overflows, and more.

Snort’s three main operating modes include sniffing, packet logging, and network intrusion detection. In sniffing mode, Snort simply captures packets on the network; in packet logging mode, it stores packet information in log files for later analysis; and network intrusion detection mode is Snort’s core functionality, where it searches for potential malicious behavior in packets.

Components and Configuration of Snort

Snort’s components mainly include the following parts:

1. Preprocessor: The preprocessor is responsible for preprocessing packets, such as splitting TCP streams, checking DNS requests, etc., to improve detection efficiency.

2. Rule Engine: The rule engine is the core of Snort, containing a series of user-defined rules for matching packet characteristics.

3. Output Plugin: Snort supports multiple output methods, such as standard output, log files, databases, etc., to facilitate analysis and response.

4. Detection Engine: The detection engine executes rule matching, and once a match is found, it triggers corresponding actions, such as alarms, discarding packets, etc.

Installation and Configuration of Snort

Installing Snort typically involves compiling the source code, configuring options, and installing dependencies. The configuration file `snort.conf` is Snort’s core configuration file, where you can set operating modes, rule paths, output plugins, etc. For beginners, understanding and customizing `snort.conf` is a key step in using Snort.

Writing and Managing Snort Rules

Snort rules consist of multiple fields, including protocol, direction, service, rule header, content, options, etc. Rules can be obtained from online resources or community sharing, or custom-written based on specific needs. Managing rules usually involves importing, updating, testing, and optimizing to ensure the effectiveness and performance of the rules.

Integration of Snort with Other Tools

Snort can be integrated with various security tools, such as Bro, Suricata, etc., to enhance overall network security capabilities. Additionally, it can work with SIEM (Security Information and Event Management) systems, such as Splunk, Elasticsearch, to achieve event visualization and analysis.

Learning Resources

For readers who wish to delve deeper into Snort, the following resources are recommended:
1. Official Documentation: Provides detailed installation and configuration guides.
2. “Snort 3 User Manual”: The official manual introducing the latest features and usage of Snort 3.
3. “The Art of Snort Intrusion Detection”: A classic book that explains Snort’s working principles and practical applications in detail.
4. Online Forums and Communities: Such as GitHub, Stack Overflow, where you can find user-shared rules, problem-solving, and best practices.

As a powerful intrusion detection system, Snort’s flexibility and open-source nature make it an important tool in the field of network security. Through in-depth learning and practice, we can effectively use Snort to protect networks from attacks.

# 1. Introduction

## 1.1 What is Network Intrusion Detection

In the field of network security, network intrusion detection is a crucial security mechanism used to monitor network traffic and identify potential malicious activities and attacks. By analyzing network packets and traffic patterns, network intrusion detection systems can promptly detect and respond to abnormal events that may harm the network.

## 1.2 Overview and Background of Snort

Snort is an open-source network intrusion detection system created by Martin Roesch in 1998. It combines signature detection, protocol analysis, and content searching techniques to help network administrators detect various network attacks, such as port scans, malware propagation, denial-of-service attacks, and more.

## 1.3 Overview of This Article

This article will provide an in-depth introduction to the basic principles of Snort, installation and configuration methods, rule writing and optimization techniques, and practical application cases. By studying the content of this article, readers will be able to comprehensively understand how to use Snort for network intrusion detection and enhance network security defense capabilities.

# 2. Basic Principles of Snort

As an open-source network intrusion detection system, Snort’s basic principles include the working principle of the detection engine, rule detection mechanism, and the advantages and limitations of Snort. These will be introduced one by one below.

# 3. Installation and Configuration of Snort

As a popular network intrusion detection system, Snort needs to be correctly installed and configured in practical applications to function effectively. This chapter will introduce the installation steps, configuration methods, and rule library download and update.

#### 3.1 Installation Steps of Snort

The installation of Snort is relatively simple, and the basic steps are as follows:

1. Download the Snort source package:

wget https://www.snort.org/downloads/snort/snort-2.9.13.tar.gz

2. Extract the source package:

tar -zxvf snort-2.9.13.tar.gz

3. Enter the extracted directory:

cd snort-2.9.13

4. Configure, compile, and install:

./configure
make
sudo make install

5. Verify the installation result:

snort -V

#### 3.2 Configuring Snort for Network Traffic Monitoring

The Snort configuration file is located at /etc/snort/snort.conf, and the default configuration already includes many common rules. You can modify it as needed, such as specifying the network interface to monitor and the log output path.

#### 3.3 Downloading and Updating the Rule Library

Snort’s rule library can be downloaded from the official website and manually updated. Use the following command to download the latest rule file:

wget https://www.snort.org/rules/snortrules-snapshot-29120.tar.gz

Then extract and overwrite the existing rule files:

tar -zxvf snortrules-snapshot-29120.tar.gz -C /etc/snort/

By following these steps, you can successfully install and configure Snort for network intrusion detection to protect your network system’s security.

# 4. Writing and Optimizing Snort Rules

When using Snort for network intrusion detection, writing and optimizing rules is very important. By writing accurate and effective rules, you can improve detection accuracy and efficiency. This chapter will introduce the methods for writing and optimizing Snort rules.

#### 4.1 Introduction to Rule Syntax

Snort rules are written based on a specific syntax format, including the following important parts:

– Action Identifier: Specifies the action to take when the rule conditions are met, such as alert (alarm), log (record), etc.
– Protocol Detector: Specifies the protocol type that the rule matches, such as TCP, UDP, etc.
– Source and Destination IP: Specifies the matching conditions for the source and destination addresses.
– Source and Destination Port: Specifies the matching conditions for the source and destination ports.
– Rule Options: Specifies how the engine processes matching packets and can include rule descriptions, classifications, priorities, etc.

#### 4.2 Examples of Rules for Common Attack Types

Here are some examples of rules for common attack types:

– Scan Attack Detection:

plaintext
alert tcp any any -> $HOME_NET 21 (msg: “FTP Brute Force Login”; flow: to_server,established; content: “530 Login incorrect”; sid:100001;)

– SQL Injection Detection:

plaintext
alert tcp any any -> $EXTERNAL_NET any (msg:”SQL Injection Attack Detected”; flow:to_server,established; content:”union select”; sid:100002;)

– DDoS Attack Detection:

plaintext
alert ip any any -> $HOME_NET any (msg:”Possible DDoS Attack Detected”; threshold: type threshold, track by_src, count 100, seconds 60; sid:100003;)

#### 4.3 Rule Optimization and Performance Tuning

When writing rules, to improve performance, consider the following optimization methods:

– Reduce Redundant Detection Items: Avoid duplicate conditions and reduce unnecessary detections in rules.
– Use Engine Options Reasonably: Choose appropriate engine options based on actual needs to avoid unnecessary resource consumption.
– Regularly Review and Update Rules: As network attack forms evolve, the rule library needs to be continuously updated and optimized.

By continuously optimizing and adjusting rules, Snort can play a more effective role in network intrusion detection.

# 5. Practical Application Cases

As an open-source and powerful software, the network intrusion detection tool Snort is widely used in the field of network security. The following will introduce several practical application cases to demonstrate Snort’s effectiveness in different scenarios.

#### 5.1 Using Snort to Detect DDoS Attacks

DDoS (Distributed Denial of Service) attacks are a common network attack method that uses a large number of zombie hosts to send a large number of requests to the target server, causing the target server to exhaust resources and be unable to provide normal services. Snort can identify and block DDoS attacks by monitoring abnormal requests in the traffic.

python
# Example Code: Detecting DDoS Attacks Based on Snort Rules

alert tcp any any ->
$HOME_NET 80 (msg: “Potential DDoS Attack Detected”; \
flow: to_server, established; content: “GET”; \
detection_filter: track by_src, count 50, seconds 10; sid:10001;)

Code Analysis:
– The above rule is designed to detect “GET” requests on port 80. An alert is triggered when a single source IP sends more than 50 requests within 10 seconds, identifying it as a potential DDoS attack.
– `flow: to_server, established` specifies that the rule applies to traffic flowing from the client to the server.
– `detection_filter: track by_src, count 50, seconds 10` sets the detection condition to 50 requests per 10 seconds.
– `sid:10001` is the rule ID used to uniquely identify this rule.

Code Summary:
By writing such rules, specific conditions can be set to detect potential DDoS attacks, enabling timely detection and response to malicious activity.

Result Explanation:
When network traffic matches the conditions defined in this rule, Snort will trigger an alert, allowing administrators to take appropriate measures to mitigate the DDoS attack.

#### 5.2 Combating Malware Propagation

Malware propagation is a significant challenge in network security. Snort can detect specific characteristics of malware propagation, enabling timely identification and prevention of malicious activities.

#### 5.3 Real-Time Intrusion Detection with Snort

By leveraging Snort’s real-time traffic monitoring and rule-based detection capabilities, real-time network intrusion detection can be achieved. This helps in promptly identifying and blocking network attacks, thereby enhancing overall network security.

# 6. Conclusion and Outlook

In this article, we explored how to use Snort for network intrusion detection. Below, we summarize Snort’s key features in intrusion detection and discuss future trends in Snort and network security.

#### 6.1 Review of Snort’s Intrusion Detection Features

Snort, as a flexible and powerful open-source network intrusion detection system, plays a crucial role in network security. Through in-depth protocol analysis and rule matching, Snort effectively identifies and reports various network attacks. Its advantages include:

– Real-Time Monitoring: Snort can monitor network traffic in real time, detecting potential threats promptly.
– Flexibility: Users can write and modify rules as needed to detect and defend against specific attacks.
– Community Support: Snort has an active development community, and its rule library is widely maintained and updated, ensuring timely and accurate detection capabilities.

However, Snort has some limitations, such as challenges in handling encrypted traffic and high performance overhead. In practical applications, it should be used in conjunction with other security tools and measures to build a multi-layered defense system and improve overall security.

#### 6.2 Future Trends in Snort and Network Security

As network attacks become increasingly complex and diverse, the future of Snort and network security will likely involve the following trends:

– Application of Intelligent Technologies: With the rapid development of artificial intelligence and machine learning, these technologies will be more widely applied in network security, improving the accuracy and efficiency of intrusion detection.
– Big Data Analytics: The introduction of big data analytics can help security professionals better understand and predict network threats, enhancing the ability to counter network attacks.
– IoT Security: With the proliferation of IoT devices, network security will face new challenges. Tools like Snort will need continuous upgrades and optimizations to detect emerging threats effectively.

In conclusion, Snort, as a mature and powerful network intrusion detection tool, will continue to play a vital role in the field of network security. However, it must keep innovating and evolving to adapt to the ever-changing landscape of network threats.Security Technology Expert
Fudan University Computer Science Master’s, Senior Security Technology Expert, previously served as a Security Technology Engineer at a renowned large technology company, responsible for the overall security architecture design and implementation.

Column Introduction
The “Network Intrusion Detection” column delves into the technologies and tools for network intrusion detection. The articles cover a wide range of topics from basic concepts to advanced techniques, including:

* Wireshark Network Traffic Analysis
* Snort and Suricata Network Intrusion Detection
* Zeek Network Intrusion Detection
* Machine Learning-Based Anomaly Detection
* YARA Rule Engine
* IDS and IPS Technologies
* Honeypot Technologies
* Moloch Network Traffic Capture and Visualization
* Content-Based Intrusion Detection
* Bro_Zeek Script Extensions
* ELK Stack Log Analysis and Intrusion Detection
* Snort Rule Syntax and Optimization
* Splunk Network Security Monitoring and Incident Response
* Deep Learning Intrusion Detection
* Comprehensive Monitoring with SOC Tools
* Web Application Security Defense
* Intrusion Log Analysis
* Network Intrusion Detection and Prevention Architecture Design

Through in-depth tutorials and practical examples, this column aims to help cybersecurity professionals understand and implement effective network intrusion detection strategies to protect their networks from malicious activities.

Real-time Detection Column Directory

• 
  Introduction to Basic Concepts of Network Intrusion Detection
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• 
  Network Traffic Analysis with Wireshark
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  Network Intrusion Detection with Snort
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• 
  Exploring Suricata Network Security Monitoring Tool
  
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  Learning Network Intrusion Detection with Zeek
  
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  Introduction to Machine Learning-Based Network Anomaly Detection
  
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  Mastering the Use of YARA Rule Engine
  
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  In-Depth Understanding of IDS and IPS Technologies in Network Intrusion Detection
  
• 
  Applying Honeypot Technology to Identify Network Intrusions
  
• 
  Using Open Source Tool Moloch for Network Traffic Capture and Visualization
  
• 
  Building Custom Rules for Content-Based Network Intrusion Detection
  
• 
  Extending Network Intrusion Detection Capabilities with Bro_Zeek Scripts
  
• 
  Network Log Analysis and Intrusion Detection with ELK Stack
  
• 
  In-Depth Understanding of Snort Rule Syntax and Optimization Techniques
  
• 
  Network Security Monitoring and Incident Response with Splunk
  
• 
  Exploring Deep Learning-Based Network Intrusion Detection Technologies
  
• 
  Comprehensive Network Intrusion Monitoring with SOC Tools
  
• 
  Sharing Web Application Security Defense and Intrusion Detection Techniques
  
• 
  Extracting Valuable Information from Network Intrusion Logs
  
• 
  Network Intrusion Detection and Prevention in Architecture Design
  

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Real-time Detection Column Directory

• 
  Introduction to Basic Concepts of Network Intrusion Detection Technology
  Preview
• 
  Network Traffic Analysis Using Wireshark
  Preview
• 
  Network Intrusion Detection Using Snort
  Preview
• 
  Exploring Suricata Network Security Monitoring Tool
  
• 
  Learning to Use Zeek for Network Intrusion Detection
  
• 
  Introduction to Machine Learning-Based Network Anomaly Detection Technology
  
• 
  Mastering the Use of YARA Rule Engine
  
• 
  In-Depth Understanding of IDS and IPS Technologies in Network Intrusion Detection
  
• 
  Applying Honeypot Technology to Identify Network Intrusion Behaviors
  
• 
  Using Open Source Tool Moloch for Network Traffic Capture and Visualization
  
• 
  Building Custom Rules for Content-Based Network Intrusion Detection
  
• 
  Extending Network Intrusion Detection Functions with Bro_Zeek Scripts
  
• 
  Network Log Analysis and Intrusion Detection Using the ELK Stack
  
• 
  In-Depth Understanding of Snort Rule Syntax and Rule Optimization Techniques
  
• 
  Network Security Monitoring and Incident Response Using Splunk
  
• 
  Exploring Deep Learning-Based Network Intrusion Detection Technology
  
• 
  Comprehensive Network Intrusion Monitoring Using SOC Tools
  
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  Web Application Security Defense and Intrusion Detection Technology Sharing
  
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  Extracting Valuable Information from Network Intrusion Logs
  
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