How to Implement Network Behavioral Detection in NS2

To implement the Network Behavioral detection in NS2, we have to observe traffic structure, identifying anomalies and detecting mischievous or unexpected activities in term of specified rules or dynamic learning algorithms. In NS2, we can replicate it by setting up node actions, tracking traffic statistics and including detection rules or thresholds to detect deviations from actual behavior.

Follow the below step-by-step guide to implementing Network Behavioral Detection in NS2:

Step-by-Step Implementation:

  1. Set Up NS2

Make certain that NS2 is installed on your system. If not, you can install it using:

sudo apt-get install ns2

  1. Define the Network Topology

Set up the simple network topology in NS2. The behavioral detection system will observe traffic patterns amongst nodes.

Example topology:

set ns [new Simulator]

set tracefile [open behavioral_detection.tr w]

$ns trace-all $tracefile

# Create nodes

set n1 [$ns node]  ;# Normal node (Sender)

set n2 [$ns node]  ;# Normal node (Receiver)

set detection_node [$ns node]  ;# Behavioral detection system node

# Create links between the nodes

$ns duplex-link $n1 $n2 1Mb 10ms DropTail

$ns duplex-link $n1 $detection_node 1Mb 10ms DropTail

  1. Simulate Normal Traffic Behavior

State a traffic flow amidst n1 (sender) and n2 (receiver). The detection node will see traffic and identify any anomalies according to the definite behavior rules.

# Set up normal UDP traffic between n1 and n2

set udp1 [new Agent/UDP]

set null1 [new Agent/Null]

$ns attach-agent $n1 $udp1

$ns attach-agent $n2 $null1

$ns connect $udp1 $null1

# Create a CBR (Constant Bit Rate) traffic generator attached to UDP

set cbr1 [new Application/Traffic/CBR]

$cbr1 set packetSize_ 512

$cbr1 set rate_ 1Mb

$cbr1 attach-agent $udp1

# Start normal traffic

$ns at 1.0 “$cbr1 start”

  1. Implement Behavioral Detection Rules

The Behavioral Detection Node will see the traffic and detect any abnormal activities. You can execute detection rules in term of traffic volume, packet size, or time breaks. For instance, if traffic volume surpasses a particular threshold, it could denote a potential attack (such as DDoS).

(A) Traffic Monitoring Function

Configure a function to observe traffic aspects packet count, rate, or inter-arrival times.

# Variables to track traffic statistics

set pkt_count 0

set rate_threshold 1000  ;# Set a traffic rate threshold (in packets)

set last_pkt_time 0

# Function to monitor traffic

proc monitor_traffic {packet_time} {

global pkt_count rate_threshold last_pkt_time

# Increment packet count

incr pkt_count

# Calculate inter-arrival time between packets

set inter_arrival_time [expr $packet_time – $last_pkt_time]

set last_pkt_time $packet_time

# Check for abnormal traffic (e.g., too many packets or short inter-arrival time)

if { $pkt_count > $rate_threshold || $inter_arrival_time < 0.001 } {

puts “Abnormal behavior detected: High traffic volume or packet rate”

return 1 ;# Return 1 if abnormal behavior is detected

} else {

return 0 ;# Return 0 if traffic is normal

}

}

(B) Simulate Behavioral Detection

The detection node will observe the traffic and flag any abnormal actions. For this instance, it will see the packet count and inter-arrival times to identify high traffic volumes or unusual rates.

# Simulate the detection system at the detection node

set udpDetection [new Agent/UDP]

$ns attach-agent $detection_node $udpDetection

# Function to simulate packet arrival at the detection node

proc capture_packet {source dest size time} {

global pkt_count

puts “Packet captured by detection node: Source=$source Dest=$dest Size=$size Time=$time”

# Call traffic monitoring function

if {[monitor_traffic $time]} {

puts “Abnormal behavior detected: Trigger mitigation”

# Add mitigation actions here (e.g., throttling or blocking)

}

}

# Capture packets at the detection node and monitor behavior

$ns at 1.5 “capture_packet n1 n2 512 1.5”

  1. Simulate Abnormal Traffic Behavior

Examine the behavioral detection system by replicating an abnormal traffic event. For example, the mischievous node n1 might initiate producing excessive traffic, activating the detection node to detect and mitigate the attack.

Example: Simulating a traffic flooding attack (abnormal behavior) from n1:

# Increase the rate of traffic (simulating an attack) from node n1

set cbr_attack [new Application/Traffic/CBR]

$cbr_attack set packetSize_ 512

$cbr_attack set rate_ 10Mb  ;# Set a high rate to simulate traffic flooding

$cbr_attack attach-agent $udp1

# Start abnormal traffic

$ns at 2.0 “$cbr_attack start”

  1. Implement Mitigation Measures

Once abnormal behavior is detected, the detection node can start a mitigation response. For example, it can throttle the traffic rate from the offending node or block further communication.

Example: Throttling or blocking malicious traffic:

# Function to throttle traffic from a node

proc throttle_traffic {node new_rate} {

puts “Throttling traffic from $node to $new_rate”

$node set rate_ $new_rate  ;# Reduce traffic rate

}

# Function to block a node from communicating

proc block_node {node} {

puts “Blocking traffic from $node”

$ns detach-agent $node  ;# Detach the node from the network

}

# Trigger traffic throttling if abnormal behavior is detected

$ns at 2.5 “throttle_traffic $cbr_attack 512kb”

  1. Run the Simulation

Once the Tcl script is ready, execute the simulation using NS2:

ns your_script.tcl

  1. Analyze Results

After the simulation is done, compute the trace file (behavioral_detection.tr) to see:

  • How the detection node observed traffic.
  • Whether abnormal behavior was identified.
  • How mitigation actions (throttling or blocking) affected the network.

You can also visualize the network activity using NAM (Network Animator) to learn the influence of behavioral detection and mitigation.

  1. Extend the Simulation
  • Advanced Detection Algorithms: Execute more advanced detection algorithms like machine learning-based anomaly detection or statistical models.
  • Multi-node Monitoring: Extend the system to see several nodes and identify coordinated attacks like Distributed Denial of Service (DDoS).
  • Attack Variations: Replicate various attack types like packet injection, replay attacks, or slow-rate attacks, and see how the detection system reacts.
  • False Positive/Negative Analysis: Analyze the performance of the detection system by assessing false positives (normal behavior flagged as attacks) and false negatives (missed attacks).

This manual delivered the necessary details to guide you through the implementation of Behavioral Detection within the network using the ns2 tool to detect the abnormal activities by monitoring and applying some rules into the simulation. You can also be able to extend the simulation by including the given features into it. So if you seek more implementation help then you can contact us we are ready with numerous ideas stay in touch with us for more support.