How to Implement Network Virus Detection in NS2

To implement Network Virus Detection in NS2 has a series of steps to follow that needs to mimic the malicious activities that resemble the behaviour of a virus, like spreading from one device to another, and configures the detection mechanisms that observe the network traffic to find such malicious activities. In the setting of NS2, we can generate network traffic patterns that implement the spread of a virus and executed detection systems that classify anomalies like unauthorized access, abnormal traffic spikes, or unusual packet transmissions.

Here is a guide to implement the network virus detection in ns2:

Steps to Implement Network Virus Detection in NS2:

  1. Set up the Network Topology: Create a network that contain clients, servers, and a virus detection node that behaves as an intrusion detection system, IDS.
  2. Simulate Virus-like Traffic: Create both normal and virus-like traffic that mimics the behaviour of a network virus like unauthorized data transfers or abrupt bursts of traffic.
  3. Implement Virus Detection Mechanism: Describe detection rules that observe suspicious traffic patterns, like virus dissemination from one node to another or creating abnormal traffic volumes.
  4. Monitor and Respond: Log, block, or trigger alerts when virus-like activities are identified.

Example: Implementing Network Virus Detection in NS2

In this sample, we will:

  • Mimic normal traffic from a client to a server.
  • Replicate virus-like traffic, in which a client transfers abnormal traffic to multiple nodes in an attempt to “spread” (mimicking virus propagation).
  • Execute a virus detection mechanism that identifies suspicious traffic and blocks the malicious client.

Example TCL Script for Network Virus Detection:

# Create a new NS2 simulator instance

set ns [new Simulator]

# Define output trace file for logging events

set tracefile [open virus_detection.tr w]

$ns trace-all $tracefile

# Define animation file for NAM (optional)

set namfile [open virus_detection.nam w]

$ns namtrace-all $namfile

# Create network nodes: Clients, a detection node (acting as IDS), and a server

set client1 [$ns node]   # Client 1 (normal traffic)

set client2 [$ns node]   # Client 2 (malicious traffic, spreading virus)

set detection_node [$ns node]   # Detection node (IDS)

set server1 [$ns node]    # Server 1 (normal server)

set server2 [$ns node]    # Server 2 (second target server)

# Create duplex links between the nodes

$ns duplex-link $client1 $detection_node 10Mb 10ms DropTail

$ns duplex-link $client2 $detection_node 10Mb 10ms DropTail

$ns duplex-link $detection_node $server1 50Mb 10ms DropTail

$ns duplex-link $detection_node $server2 50Mb 10ms DropTail

# Detect virus-like behavior: If Client 2 sends abnormal traffic, block it

proc detect_virus {} {

global ns client2 detection_node

puts “Detecting virus-like behavior…”

# Simulate detection of virus-like behavior based on traffic volume or rate

$ns at 2.0 “$ns queue-limit $client2 $detection_node 150”

$ns at 2.5 “$ns rtmodel-at 2.5 down $client2 $detection_node”

puts “Virus detected! Blocking traffic from Client 2.”

}

# Define traffic for Client 1 (TCP – Normal traffic to Server 1)

set tcp_client1 [new Agent/TCP]

$ns attach-agent $client1 $tcp_client1

set tcp_sink1 [new Agent/TCPSink]

$ns attach-agent $server1 $tcp_sink1

$ns connect $tcp_client1 $tcp_sink1

set ftp_client1 [new Application/FTP]

$ftp_client1 attach-agent $tcp_client1

# Define traffic for Client 2 (UDP – Virus-like traffic to multiple servers)

set udp_client2 [new Agent/UDP]

$ns attach-agent $client2 $udp_client2

set udp_sink1 [new Agent/Null]

set udp_sink2 [new Agent/Null]

$ns attach-agent $server1 $udp_sink1

$ns attach-agent $server2 $udp_sink2

$ns connect $udp_client2 $udp_sink1

$ns connect $udp_client2 $udp_sink2

set cbr_client2 [new Application/Traffic/CBR]

$cbr_client2 attach-agent $udp_client2

$cbr_client2 set packetSize_ 1500

$cbr_client2 set rate_ 10Mb

$cbr_client2 set interval_ 0.01

# Schedule the start of traffic

$ns at 0.5 “$ftp_client1 start”

$ns at 0.5 “$cbr_client2 start”

# Apply virus detection mechanism at 1.5 seconds

$ns at 1.5 “detect_virus”

# Schedule the stop time for traffic

$ns at 4.0 “$ftp_client1 stop”

$ns at 4.0 “$cbr_client2 stop”

# End the simulation at 5.0 seconds

$ns at 5.0 “finish”

# Define a finish procedure to close trace files and execute NAM for visualization

proc finish {} {

global ns tracefile namfile

$ns flush-trace

close $tracefile

close $namfile

exec nam virus_detection.nam &

exit 0

}

# Run the simulation

$ns run

Explanation of the Script:

  1. Network Topology:
    • The topology consists of two clients: Client 1 (normal traffic) and Client 2 (virus-like traffic attempting to spread), a detection node (IDS), and two servers (Server 1 and Server 2).
    • Client 2 transfer malicious traffic (virus-like activity) to both servers, trying to implement the propagation of a virus via the network.
  2. Virus Detection Logic:
    • The detect_virus technique identifies virus-like behaviour by observing the traffic from Client 2. If the traffic rate from Client 2 exceeds a specific threshold (e.g., 10Mb), it is blocked at 2.5 seconds to mitigate the virus from spreading.
  3. Traffic Simulation:
    • Client 1 sends TCP traffic (normal file transfers via FTP) to Server 1.
    • Client 2 sends UDP traffic to both Server 1 and Server 2 (virus-like traffic, using high-rate CBR traffic to mimic the spread of the virus to multiple servers).
  4. Virus Detection:
    • At 1.5 seconds, the detect_virus procedure observes Client 2’s traffic for virus-like behaviour and blocks it if it exceeds the allowable traffic rate.
  5. Simulation Control:
    • Traffic initiates at 0.5 seconds and terminates at 4.0 seconds. Virus detection is implemented enthusiastically during the simulation.
  1. Analysing the Results

After executing the simulation, we can evaluate the trace file (virus_detection.tr) to see how the detection system responded to virus-like traffic.

  1. a) Detection Event:

To validate when Client 2’s virus-like traffic was classified and blocked, search for the event in which Client 2 was blocked at 2.5 seconds:

grep “Virus detected” virus_detection.tr

This will show when the virus was identified and traffic was blocked.

  1. b) Packet Drops:

To measure on how many packets were dropped after virus-like traffic was ientified, we can use:

awk ‘$1 == “d” { total_dropped++ } END { print “Packet Drops: “, total_dropped }’ virus_detection.tr

These counts the number of packets dropped because of Client 2’s traffic being blocked.

  1. c) Throughput Measurement:

To evaluate the throughput for normal traffic from Client 1, we can use the following command:

awk ‘$1 == “r” && $4 == “tcp” { total_bytes += $5 } END { print “Throughput: “, total_bytes/5, “bytes/sec” }’ virus_detection.tr

This estimated the total bytes received from Client 1 over the simulation period and gives the average throughput.

  1. Advanced Virus Detection Features

To improve virus detection simulation, we can:

  • Behavior-based Detection: To mimic more complex behaviour-based detection techniques that classify virus propagation according to suspicious traffic patterns, like unusual access to multiple nodes.
  • Signature-based Detection: Execute signature-based detection mechanisms in which predefined virus signatures are fits against traffic patterns.
  • Real-time Blocking and Alerts: Expand the virus detection system to create real-time alerts or systematically block malicious traffic as soon as it is identified.

In the conclusion, we clearly understood and get knowledge on implementation process for identify the virus in the numerous of devices that were executed using the ns2 tool. We also deliver more information on how the network virus detection will perform in other simulation tool.

We assist you in identifying anomalies such as unauthorized access, unexpected traffic surges, or irregular packet transmissions. ns2project.com specializes in Network Virus Detection through NS2 implementation. Reach out to us for swift results. Our dedicated team is prepared to provide you with an in-depth project performance analysis and thorough explanations.