How to Start Network Threat Detection Projects Using NS3

To start a network threat detection project using NS3, we will need to replicate network traffic, launching potential threats, and improving techniques to identify them. By configuring this kind of project then we can be estimated the effectiveness of diverse threat detection methods like signature-based, anomaly-based, and behavior-based approaches. Below is a sequential approach to configuring and executing a network threat detection project in NS3.

Steps to Start Network Threat Detection Projects in NS3

  1. Define Project Objectives and Scope
  • Identify Detection Use Cases:
    • Anomaly-Based Detection: To identify the unusual patterns within network traffic.
    • Signature-Based Detection: Detect certain attack signatures like known malware or DDoS patterns.
    • Behavior-Based Detection: We can observe for suspicious behaviors like unauthorized data transfer or repeated login attempts.
    • Hybrid Detection: Aggregate several detection methods, enhancing accuracy and to minimize false positives.
  • Define Key Performance Metrics:
    • Detection Accuracy: We estimate the true positives, false positives, false negatives, and true negatives.
    • Latency and Throughput: Observe the effect of detection mechanisms upon network performance.
    • Resource Utilization: We need to monitor CPU and memory usage at nodes, which execute threat detection logic.
    • Detection Delay: Estimate the duration to identify threats after they are launched.
  1. Install and Set Up NS3
  • Download NS3: From the NS3 official sit, we can get the new version.
  • Install NS3: We adhere to guidelines then confirm including example scripts.
  • Optional External Libraries: For anomaly detection, we plan to utilize machine to study how to install Python including Scikit-Learn or TensorFlow for data analysis.
  1. Design the Network Topology
  • Select Topology Layout:
    • Star Topology: It is optimal for centralized threat detection including one server performing like the primary target and clients as potential attackers.
    • Multi-Segment Network: We replicate diverse areas like LAN, DMZ along with routers separating segments, to permit for segment-specific threat detection.
    • Mesh or Ad-Hoc Network: It is helpful for analysing the threat detection within decentralized networks like IoT or MANET.
  • Configure Nodes and Connections:
    • Make nodes like clients, servers, and routers using NodeContainer.
    • For wired configurations, we can utilize CsmaHelper or WifiHelper for wireless connections depends on the project needs.
  1. Set Up Data Collection for Threat Detection
  • Packet Capture and Logging:
    • PcapTrace: Seize in-depth packet information at certain nodes or links utilising Pcap tracing that is helpful for identifying anomalies or signatures.
    • AsciiTrace: Allow high-level logging seizing packet headers, protocol usage, and timing.
    • Set capture on key points like at routers, servers, and any security devices, observing for suspicious traffic.
  • Log Export:
    • Save records for post-simulation analysis to permit for offline analysis and integration including external analysis tools such as Wireshark or Python-based analysis scripts.
  1. Simulate Cyber Threats for Detection Testing
  • DDoS Attack Simulation:
    • Transmit high-rate traffic targeting a certain server using numerous nodes, to experiment the ability of system to identify unusual traffic volumes.
  • Brute-Force Login Attempt:
    • Set a node transmitting repeated login demands to a server, to permit the system identifying abnormal login behavior.
  • Port Scanning:
    • We execute port scanning by containing a node attempt connections at various ports on a target, to experiment the ability of IDS to identify repeated connection attempts.
  • Data Exfiltration:
    • Mimic data leakage by containing a node transmits sensitive information occasionally to an unauthorized receiver.
  • Packet Manipulation (Spoofing):
    • Fine-tune packet headers like IP addresses to replicate spoofing, to experiment the detection mechanisms versus IP spoofing or ARP spoofing attacks.
  1. Implement Threat Detection Techniques
  • Signature-Based Detection:
    • We can make rules, which identify certain packet patterns like known attack signatures such as SYN flood for DDoS.
    • For well-known attacks, describe basic signatures like verifying for a high number of SYN packets without an ACK response.
  • Anomaly Detection Using Thresholds:
    • Implement the thresholds for traffic parameters like packet rate, connection attempts, or data volume.
    • Identify traffic like suspicious as thresholds are surpassed such as a high packet-per-second rate might display a DDoS.
  • Machine Learning for Anomaly Detection (Advanced):
    • Transfer packet data to Python or other ML environments enforcing the machine learning algorithms, like clustering or anomaly detection models, to categorize traffic like typical or malicious.
    • Execute a pipeline transferring packet aspects such as source IP, packet size, and interval and train models identifying the anomalies.
  1. Set Up Application Layer for Realistic Traffic
  • Normal Traffic Simulation:
    • Replicate regular user traffic to utilize applications such as UdpEchoClient/UdpEchoServer and OnOffApplication.
    • Replicate diverse traffic patterns making a realistic baseline with regular browsing, file downloads, and video streaming.
  • Malicious Traffic Simulation:
    • For DDoS configure high-rate OnOffApplication or use TcpSocketFactory for port scanning, to make traffic patterns consistent including certain attack types.
    • Set custom payloads permitting to experiment the detection of unauthorized data transfers for data exfiltration scenarios.
  1. Define and Measure Performance Metrics
  • Detection Metrics:
    • We monitor true positives (correctly detected attacks), false positives (normal traffic flagged as attacks), false negatives (missed attacks), and true negatives.
    • Estimate the detection rate, false positive rate, and false negative rate, measuring threat detection performance.
  • Latency and Throughput Impact:
    • Calculate how detection impacts overall network performance like delay and data rates.
  • Detection Delay:
    • Measure the duration to identify threats that is crucial for real-time detection systems after they begin.
  • Resource Usage:
    • Monitor CPU and memory usage, which execute IDS or other detection logic, measuring scalability and resource overhead for nodes.
  1. Simulate and Analyze Results
  • Run Simulation:
    • Experiment the detection effectiveness over diverse attack scenarios and intensities.
    • We equate the performance of network with and without threat detection mechanisms dynamic.
  • Data Collection:
    • Accumulate data on packet flows, detection events, and any delays to utilize NS3’s tracing tools.
    • Transfer records to Wireshark or Python for in-depth inspection and analysis.
  • Visualization and Reporting:
    • Envision parameters like detection accuracy, traffic flow, and influence over network performance to utilize visualization tools such as Matplotlib or Gnuplot.
    • Make logs to sum up detected threats, detection time, and any resource effect.

Example Code Outline for Basic Threat Detection in NS3

Here’s a basic code structure of NS3 replicating a DDoS attack and utilize simple threshold-based threat detection on packet rates.

#include “ns3/core-module.h”

#include “ns3/network-module.h”

#include “ns3/internet-module.h”

#include “ns3/point-to-point-module.h”

#include “ns3/applications-module.h”

#include <iostream>

using namespace ns3;

// Function to monitor and detect high packet rates (threshold-based detection)

void MonitorTraffic(Ptr<Node> node, uint32_t threshold) {

Ptr<Ipv4> ipv4 = node->GetObject<Ipv4>();

uint32_t packetsReceived = ipv4->GetNReceived();

if (packetsReceived > threshold) {

std::cout << “Potential DDoS detected on Node ” << node->GetId()

<< ” – Packets received: ” << packetsReceived << std::endl;

}

// Schedule the next check for packet count

Simulator::Schedule(Seconds(1.0), &MonitorTraffic, node, threshold);

}

int main(int argc, char *argv[]) {

// Step 1: Create Nodes

NodeContainer attackers, targetNode;

attackers.Create(5); // Five attacking nodes

targetNode.Create(1); // One target node

// Step 2: Set Up Point-to-Point Links

PointToPointHelper p2p;

p2p.SetDeviceAttribute(“DataRate”, StringValue(“10Mbps”));

p2p.SetChannelAttribute(“Delay”, StringValue(“2ms”));

NetDeviceContainer devices;

for (uint32_t i = 0; i < attackers.GetN(); ++i) {

devices.Add(p2p.Install(attackers.Get(i), targetNode.Get(0)));

}

// Step 3: Install Internet Stack

InternetStackHelper internet;

internet.Install(attackers);

internet.Install(targetNode);

Ipv4AddressHelper address;

address.SetBase(“10.1.1.0”, “255.255.255.0”);

address.Assign(devices);

// Step 4: Set Up DDoS Attack Traffic

uint16_t port = 8080;

OnOffHelper onOffHelper(“ns3::UdpSocketFactory”, InetSocketAddress(Ipv4Address(“10.1.1.1”), port));

onOffHelper.SetConstantRate(DataRate(“1Mbps”)); // High-rate traffic to simulate DDoS

ApplicationContainer attackerApps;

for (uint32_t i = 0; i < attackers.GetN(); ++i) {

attackerApps.Add(onOffHelper.Install(attackers.Get(i)));

}

attackerApps.Start(Seconds(1.0));

attackerApps.Stop(Seconds(10.0));

// Step 5: Schedule Traffic Monitoring with a Threshold for DDoS Detection

uint32_t threshold = 100; // Set a packet count threshold for DDoS detection

Simulator::Schedule(Seconds(1.0), &MonitorTraffic, targetNode.Get(0), threshold);

// Step 6: Run Simulation

Simulator::Run();

Simulator::Destroy();

return 0;

}

Over this manual, we had demonstrated the step-by-step technique with sample code outline for Network Threat Detection Projects, which were initiated and simulated with the help of NS3 tool. Moreover, we will also be shared extension of this topic.

We’re here to help you set up and run your projects with personalized support. The team at phdprojects.org specializes in Network Threat Detection Projects using the NS3 tool, so let us assist you in getting your configuration just right with a clear explanation.