How to Calculate Network Detection Accuracy in NS2

To calculate the Network detection accuracy in NS2, which refers to the capability of a network system (often in the context of intrusion detection systems, anomaly detection systems, etc.) to appropriately find the malicious activity or abnormal events against the typical performance. In the simulation platform NS2, replicating detection accuracy normally needs to mimicking a network situation in which both normal and malicious events happen also then estimating how successfully the system detects these events. Given below is a key metrics and calculation procedure to compute it in NS2:

Key Metrics for Detection Accuracy:

  • True Positives (TP): Appropriately detected the malicious events.
  • False Positives (FP): Typical events are incorrectly flagged as malicious.
  • True Negatives (TN): Usual events are properly identified as non-malicious.
  • False Negatives (FN): Malicious events missed by the system.

Detection Accuracy Formula:

Detection accuracy is normally estimated as:

Detection Accuracy=TP+TNTP+TN+FP+FN\text{Detection Accuracy} = \frac{TP + TN}{TP + TN + FP + FN}Detection Accuracy=TP+TN+FP+FNTP+TN​

Where:

  • TP: True Positives
  • TN: True Negatives
  • FP: False Positives
  • FN: False Negatives

Steps to Calculate Detection Accuracy in NS2:

  1. Simulate Normal and Malicious Traffic:
    Describe a network situation in which both normal traffic and malicious traffic (or abnormal behavior) happen. We can be replicated the malicious traffic like Denial of Service (DoS) attacks, spoofing, or abnormal packet flows.
  2. Implement a Detection System:
    Execute or replicate a detection mechanism, like an Intrusion Detection System (IDS) that can flag packets or flows as malicious or normal.
  3. Log Detection Events:
    In the simulation, log which events are identified as malicious and that are classified as normal. We require to capture both the real nature of the event (normal or malicious) and the system’s classification of the event (detected as malicious or not).
  4. Compare Detected Events with Actual Events:
    After running the simulation, we compare the events are flagged as malicious including the actual events to ascertain the TP, FP, TN, and FN.

Example Tcl Script to Simulate Normal and Malicious Traffic:

The following is an instance NS2 Tcl script, which configures a network with both normal and malicious traffic, in which an IDS is replicated to detect malicious traffic.

# Create a new simulator instance

set ns [new Simulator]

# Define nodes

set node0 [$ns node]

set node1 [$ns node]

set node2 [$ns node]

# Set up duplex links between the nodes

$ns duplex-link $node0 $node1 1Mb 10ms DropTail

$ns duplex-link $node1 $node2 1Mb 10ms DropTail

# Attach UDP agents to nodes

set udp0 [new Agent/UDP]

set udp1 [new Agent/UDP]

set null0 [new Agent/Null]

$ns attach-agent $node0 $udp0

$ns attach-agent $node2 $null0

$ns connect $udp0 $null0

# Attach UDP agents for malicious traffic

set udp_attack [new Agent/UDP]

set null_attack [new Agent/Null]

$ns attach-agent $node1 $udp_attack

$ns attach-agent $node2 $null_attack

$ns connect $udp_attack $null_attack

# Create traffic sources for normal traffic

set cbr0 [new Application/Traffic/CBR]

$cbr0 attach-agent $udp0

$cbr0 set packetSize_ 512

$cbr0 set rate_ 500Kb

# Create traffic source for malicious traffic (high rate)

set cbr_attack [new Application/Traffic/CBR]

$cbr_attack attach-agent $udp_attack

$cbr_attack set packetSize_ 512

$cbr_attack set rate_ 2Mb ;# High rate to simulate malicious behavior

# Start normal and malicious traffic

$ns at 1.0 “$cbr0 start”

$ns at 1.5 “$cbr_attack start”  ;# Start malicious traffic after normal traffic

# Stop traffic after 5 seconds

$ns at 5.0 “$cbr0 stop”

$ns at 5.0 “$cbr_attack stop”

# Open trace file to log events

set tracefile [open trace.tr w]

$ns trace-all $tracefile

# Define finish procedure to close trace file

proc finish {} {

global ns tracefile

$ns flush-trace

close $tracefile

exit 0

}

# End the simulation at 6 seconds

$ns at 6.0 “finish”

$ns run

Explanation:

  1. Normal Traffic:
    Normal traffic is generated among the nodes node0 and node2 using a UDP agent and CBR traffic source with a rate of 500 Kbps.
  2. Malicious Traffic:
    Malicious traffic is mimicked by generating high-rate traffic (2 Mbps) among node1 and node2 to replicate a Denial of Service (DoS) attack. The high traffic rate signifies the malicious behaviour.
  3. Traffic Classification:
    The trace file will be logged all packet transmissions, receptions, and other events, permitting to estimate which packets were part of normal traffic and which were malicious.
  1. Implement Detection Logic:

We can be executed logic to flag malicious traffic rely on particular criteria (e.g., high traffic rate), after the simulation. Physically we can be categorised the traffic or use a script to identify abnormal behaviour according to the patterns like high packet rates, unusual packet sizes, or deviations from normal traffic flow.

  1. Analyse the Trace File:

We can be used the trace file to classify which packets were part of the normal and malicious flows. Then we compare the system’s detection of these packets to the real nature of the traffic (normal or malicious).

Example Bash Script for Trace File Analysis:

# Count total number of packets sent (normal and malicious)

sent_normal=$(grep “^s” trace.tr | grep “_0_” | wc -l)

sent_malicious=$(grep “^s” trace.tr | grep “_1_” | wc -l)

# Count packets received (true positives and true negatives)

received_normal=$(grep “^r” trace.tr | grep “_0_” | wc -l)

received_malicious=$(grep “^r” trace.tr | grep “_1_” | wc -l)

# Simulated IDS flagging logic (you can set your criteria for malicious traffic)

# For this example, we’ll assume packets from node 1 are malicious

# True Positives (malicious correctly detected)

tp=$(grep “^r” trace.tr | grep “_1_” | wc -l)

# False Positives (normal traffic incorrectly flagged as malicious)

fp=$(grep “^r” trace.tr | grep “_0_” | wc -l)

# False Negatives (malicious traffic missed)

fn=$(grep “^s” trace.tr | grep “_1_” | wc -l)

# True Negatives (normal traffic correctly classified as normal)

tn=$(grep “^s” trace.tr | grep “_0_” | wc -l)

# Calculate Detection Accuracy

accuracy=$(echo “scale=2; ($tp + $tn) / ($tp + $tn + $fp + $fn) * 100” | bc)

echo “Detection Accuracy: $accuracy%”

  1. Evaluate Detection Accuracy:

After running the bash script, we will be acquired the detection accuracy of the system that expresses how successfully the detection mechanism executed in identifying malicious packets against the normal ones.

Summary:

  1. Replicate the normal and malicious traffic in NS2 utilising various traffic patterns (e.g., normal CBR traffic and high-rate traffic to simulate attacks).
  2. Execute detection logic (manual or automated) to categorise the traffic as either normal or malicious rely on the criteria we define.
  3. Estimate the trace file to count the true positives, false positives, true negatives, and false negatives.
  4. Assess the detection accuracy using the below formula:

Detection Accuracy=TP+TNTP+TN+FP+FN\text{Detection Accuracy} = \frac{TP + TN}{TP + TN + FP + FN}Detection Accuracy=TP+TN+FP+FNTP+TN​

By following these steps, we can be replicated and compute the detection accuracy of a network system in NS2. Let me understand if we require further assistance or clarification!

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