To simulate Intrusion Detection System (IDS) projects using NS2 has includes to monitoring network traffic, classifying abnormal patterns, and raising warnings when malevolent activities are identified. IDS can be replicated by evaluating the traffic among the nodes and apply the logic to flag potential attacks like Denial of Service (DoS) attacks, port scans, or unauthorized data transmissions.
Here’s how you can simulate Intrusion Detection projects in NS2:
Steps to Simulate Intrusion Detection Projects in NS2
- Install NS2
Make sure NS2 is installed on the system:
sudo apt-get install ns2
- Key Components in IDS Simulation
- Nodes (Client, Server, and Attacker): Represent numerous participants in the network that involves the legitimate users and potential attackers.
- Traffic Patterns: Describe normal traffic such as file transfers, data exchange and abnormal traffic like DoS attack, suspicious behaviour.
- Intrusion Detection Logic: Execute detection mechanisms that track traffic and find malicious patterns like high packet rates, unusual port use, or abnormal packet types.
- Alert Mechanism: produce alerts when abnormal activity is identified.
- Common IDS Scenarios
- DoS/DDoS Attack Detection: observe the traffic for unusually high packet rates that could designate a Denial of Service (DoS) attack.
- Port Scanning Detection: Identify when an attacker is scanning the network to find open ports.
- Unauthorized Access Detection: Detect attempts to connect to services or resources without authorization.
- TCL Script for Intrusion Detection Simulation
Example: DoS Attack Detection
This sample replicates normal traffic among a client and server, in addition to an attacker introducing a DoS attack. IDS track the traffic and raise an alert if the packet rate from the attacker is abnormally high.
# Create a simulator object
set ns [new Simulator]
# Open trace and NAM files
set tracefile [open “ids_simulation.tr” w]
$ns trace-all $tracefile
set namfile [open “ids_simulation.nam” w]
$ns namtrace-all $namfile
# Define nodes (Client, Server, Attacker, and IDS)
set client [$ns node]
set server [$ns node]
set attacker [$ns node]
set ids [$ns node] ;# Intrusion Detection System (monitoring traffic)
# Set positions for visualization (optional)
$client set X_ 100
$client set Y_ 100
$server set X_ 300
$server set Y_ 100
$attacker set X_ 200
$attacker set Y_ 200
$ids set X_ 200
$ids set Y_ 300
# Define wired links between nodes
$ns duplex-link $client $server 100Mb 10ms DropTail
$ns duplex-link $attacker $server 100Mb 10ms DropTail
$ns duplex-link $ids $server 100Mb 10ms DropTail ;# IDS monitors the server’s traffic
# Normal TCP traffic from client to server
set tcp_client [new Agent/TCP]
$ns attach-agent $client $tcp_client
set tcp_sink_server [new Agent/TCPSink]
$ns attach-agent $server $tcp_sink_server
$ns connect $tcp_client $tcp_sink_server
set app_client [new Application/Traffic/FTP]
$app_client attach-agent $tcp_client
$ns at 1.0 “$app_client start”
# Malicious traffic from attacker to server (DoS flood via UDP)
set udp_attacker [new Agent/UDP]
$ns attach-agent $attacker $udp_attacker
set udp_sink_server [new Agent/Null]
$ns attach-agent $server $udp_sink_server
$ns connect $udp_attacker $udp_sink_server
set app_attacker [new Application/Traffic/CBR]
$app_attacker attach-agent $udp_attacker
$app_attacker set packetSize_ 1024
$app_attacker set interval_ 0.01 ;# High-rate packet transmission simulating a DoS attack
$ns at 2.0 “$app_attacker start”
# IDS logic to monitor traffic and detect DoS attack
proc monitor_traffic {} {
global ns attacker server
set packet_rate [get_packet_rate $attacker $server]
if { $packet_rate > 100 } {
puts “Intrusion Detected: High packet rate from attacker”
}
# Schedule the next check
$ns at [expr [clock seconds] + 1] “monitor_traffic”
}
# Start the IDS monitoring process
$ns at 2.0 “monitor_traffic”
# End the simulation after 10 seconds
$ns at 10.0 “finish”
$ns run
- Run the Simulation
Once the simulation script is done, execute it with the following command:
ns ids_simulation.tcl
- Visualize the Simulation
We can utilize NAM (Network Animator) to envision the traffic and track on how the IDS identify and respond to the DoS attack:
nam ids_simulation.nam
- Analyse the Trace File
The trace file (ids_simulation.tr) can be measured to extract key parameters, such as:
- Packet Rate: The number of packets transmits by the attacker to the server. We can extract and measure the traffic logs using AWK or Python scripts.
- Alert Generation: observe when and how usual the IDS raise alerts according to abnormal traffic patterns.
Sample AWK script to analyze the packet rate:
awk ‘{if ($1 == “s” && $4 == “attacker” && $5 == “server”) print $0}’ ids_simulation.tr
- Common Intrusion Detection Scenarios and Enhancements
8.1 DDoS Attack Detection
Expand the environment to multiple attackers replicating a Distributed Denial of Service (DDoS) attack. The IDS must classify and flag the high-volume traffic coming from numerous sources.
8.2 Port Scanning Detection
Replicate an attacker attempt to scan the network for open ports by sending connection requests to numerous ports. The IDS can flag unusual scanning activity.
8.3 Unauthorized Access Detection
Design a scenario in which an attacker attempt to gain access to a restricted server. The IDS can track for unauthorized connection try and raise alerts.
8.4 False Positives/Negatives
Replicate both normal and malicious traffic to validate the accuracy of the IDS and evaluate its susceptibility to false positives (flagging legitimate traffic as malicious) and false negatives (failing to identify real attacks).
- Extending the Simulation
We can expand the intrusion detection simulation by:
- Adding Advanced Detection Mechanisms: Utilize anomaly detection or pattern matching approaches to enhance the IDS.
- Simulating Response Mechanisms: Execute automatic response actions such as blocking IPs, sending alerts when the IDS identify an attack.
- Testing Different Attack Types: Replicate other types of attacks, like Man-in-the-Middle (MITM), IP Spoofing, or Brute Force attacks, to assess the IDS’s efficiency.
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