How to Start Network Threat Detection Projects Using NS2

To stimulate a Network Threat Detection projects using NS2 has involves the configuration of replication environment for simulate the real-world network conditions in which the malicious actions for sample attacks, anomalies can be replicated and detected. Below is a comprehensive guide:

Steps to Start Network Threat Detection Projects Using NS2

1. Understand Network Threat Detection in NS2

Network threat detection goals for classify the malicious activities, like as:

• Intrusion Detection: Tracking the network congestion for unauthorized access.
• DDoS Attack Detection: Classifying the abnormal congestion design for detect the attack.
• Packet Tampering or Data Breaches: Detect modify or stolen data.
• Malware Propagation: Identifying the malicious node behaviors for malware propagation.

Key methods for detection:

• Rule-based systems for sample signature detection.
• Statistical analysis for instance abnormal traffic detection.
• Machine learning such as anomaly-based detection.

2. Set p NS2 Environment

1. Install NS2:
   • Install the NS2 (version 2.35 recommended) on a Linux system.
2. Verify Installation:
   • Validate the NS2 through a simple replication script assures operates.
3. Optional Enhancements:
   • Use tools like packet capture for Wireshark and study the threats further comprehensively.
   • Incorporate the third-party tools for threat analysis for sample Python for post-simulation analysis.

3. Define Your Project Objectives

Classify the detailed threat detection problem:

• Traffic-Based Threat Detection:
  • Find the high congestion of loads from the malicious nodes for instance DDoS attacks.
• Behavior-Based Threat Detection:
  • Classify the abnormal behaviors such as frequent retries, packet drops, or unauthorized access.
• Payload-Based Threat Detection:
  • Examine the packet payloads for malicious design.

4. Create a Simulation Script

Write a TCL script for network replication, integrating the malicious activity and detection mechanisms.

Example Steps:

1. Define the Network Topology:

set ns [new Simulator]

set n1 [$ns node]

set n2 [$ns node]

set n3 [$ns node] ;# Malicious node

$ns duplex-link $n1 $n2 10Mb 10ms DropTail

$ns duplex-link $n2 $n3 10Mb 10ms DropTail

1. Simulate Traffic:
   • Add normal traffic between n1 and n2.
   • Enhance the malicious traffic from n3 for replicate the threat for sample high-rate UDP flooding.
2. Log Traffic Data:
   • Ensure the specific logging for study of threat detection:

set tracefile [open threat-trace.tr w]

$ns trace-all $tracefile

1. Insert Detection Logic:
   • It works on the use of detection for analyze the congestion patterns:

proc detect_ddos {packet_count threshold} {

if {$packet_count > $threshold} {

puts “DDoS attack detected!”

}

}

5. Extend NS2 with Custom Modules

Designed for advanced detection mechanisms, encompass the NS2 by C++ modules:

1. Packet Inspection:
   • Alter the packet.h and tcp.cc for study the packet headers or payloads.
2. Traffic Monitoring:
   • Improve the logic for track the congestion rates and flag anomalies.
   • Sample: Apply the attack finding procedures in queue.cc.
3. Recompile NS2:

make clean

make

6. Simulate Threats

Launch the detailed networks threats for detection:

• DDoS Attack:
  • Replicate the high-rate of UDP/TCP congestion from several malicious nodes.
• Packet Tampering:
  • Modify the packet headers or replicate the payloads.
• Intrusion:
  • Unauthorized accesses are replicating the routing traffic through unknown node.

7. Analyze Trace Files

• NS2 generates trace files during simulations. Analyze these files to detect threats.
• Use tools like AWK, Python, or MATLAB for analysis:
  • Sample: Detect abnormal traffic rates:

awk ‘{ if ($1 == “r” && $3 == “malicious”) count++ } END { print “Packets from malicious node:”, count }’ threat-trace.tr

8. Visualize the Simulation

Use the Network Animator (NAM) for envisions:

• The congestion design among the nodes.
• Abnormal traffic or dropped the packets.

9. Validate and Optimize

1. Estimate the detection accuracy:
   • True positives such as threats detected correctly.
   • False positives like as normal traffic flagged as threats.
2. Calculate the performance metrics:
   • Network throughput during attacks.
   • Packet delivery ratio (PDR).
   • Detection delay.

10. Advanced Features

• Machine Learning for Anomaly Detection:
  • Use tools like a Python to train ML models on trace data for instance using scikit-learn or TensorFlow.
• Real-Time Threat Analysis:
  • Encompass the NS2 by external tools for data procedure in real-time.
• Hybrid Detection Systems:
  • Associate the signature-based and anomaly-based detection methods.

11. Document Your Project

Include:

1. The document prepares the start with Project objectives and scope.
2. Then scope process for the network topology and settings.
3. After the setting for replicate the threat and detection methods.
4. Next methods are detected to parameter results and analysis for sample graphs of detection rates.
5. Finally it gives the contribution of conclusion and future work.

Example Project Ideas

1. DDoS Attack Detection:
   • Replicate the several malicious nodes and built the detection procedures according to their congestion rates.
2. Intrusion Detection System (IDS):
   • Build IDS for finding the unauthorized access using behavior-based technique.
3. Packet Tampering Detection:
   • Execute the payload inspection and classify the alter packets.
4. IoT Threat Detection:
   • Replicate the IoT devices and finding the malicious activities in a smart home network.
5. Blockchain-Based Threat Detection:
   • Replicate the secure communication and finding the tampering in blockchain nodes.

In this module, we clearly learned about how to estimate the secure and detect the threat for the particular network system that was applied in Network threat detection project using ns2 tool. For further inquiries about this project, a separate manual will be provided.