How to Start Network Defense Projects Using NS3

To start a network defense project using NS3 which needs to contain replicating a network environment, to launch potential threats, and executing defensive mechanisms to identify, avoiding, and mitigating attacks. In NS3, network defense projects can concentrate on firewalls, intrusion prevention systems (IPS), traffic filtering, or advanced machine learning-based anomaly detection. Below is a structured approach to configuring and executing the network defense projects in NS3.

Steps to Start Network Defense Projects in NS3

  1. Define Project Objectives and Scope
  • Identify Defense Strategies:
    • Firewall and Access Control: According to the IP addresses, ports, or protocols to enable or obstruct traffic utilising packet filtering.
    • Intrusion Prevention System (IPS): To identify and avoid attacks automatically like DDoS or port scans.
    • Traffic Rate Limiting and Shaping: Restrict bandwidth or drop excessive traffic, avoiding the overload.
    • Anomaly-Based Detection and Response: Identify the unusual patterns and respond to utilize statistical thresholds or machine learning.
  • Define Key Performance Metrics:
    • Detection Accuracy: We need to estimate the true positives (correctly detected threats), false positives, and false negatives.
    • Defense Effectiveness: Measure how successfully the defense minimizes the threats effects.
    • Latency and Throughput Impact: We compute the performance overhead of defense mechanisms.
    • Resource Utilization: Monitor CPU and memory usage, estimating the scalability and resource efficiency.
  1. Install and Set Up NS3
  • Download NS3: Go to official NS3 website then download new version of NS3 on the system.
  • Install NS3: Adhere to installation guides and check with example scripts.
  • Optional External Libraries: If we ready to utilize machine learning for anomaly detection then we install Python and libraries such as Scikit-Learn or TensorFlow.
  1. Design the Network Topology
  • Select Topology Layout:
    • Star or Tree Topology: We can utilize a centralized server targeted by several clients that is helpful for DDoS or centralized IPS testing.
    • Multi-Segment Network: Employ routers to split diverse segments like LAN, DMZ and use defense on critical nodes.
    • Mesh or Ad-Hoc Network: It is helpful for analysing the decentralized defense such as IoT or MANET within environments.
  • Configure Nodes and Connections:
    • Make nodes like clients, servers, routers, and IPS nodes using NodeContainer.
    • Depends on the project requirements, for wired configurations, we can utilize CsmaHelper and WifiHelper for wireless setups.
  1. Implement Defense Mechanisms
  • Firewall and Access Control:
    • According to the IP, port, or protocol type to strain traffic utilizing custom NS3 applications.
    • We describe the access control lists (ACLs), which permit or obstruct certain traffic types, and dropping or sending packets rely on rules using PacketFilter.
  • Intrusion Prevention System (IPS):
    • We want to set rules identifying and to obstruct the particular kinds of attacks like excessive connection demands or certain attack signatures.
    • For instance, if it creates more than a threshold number of requests within a short period, an IPS might block an IP.
  • Traffic Rate Limiting and Shaping:
    • Apply rate limits on incoming connections using TrafficControlHelper that is helpful for mitigating DDoS attacks.
    • We can fix the bandwidth limits or describe token buckets, controlling the packet flow at each node or link.
  • Anomaly-Based Defense:
    • Observe the traffic statistics such as packet rate, connection frequency and equate them to thresholds identifying the anomalies.
    • For more advanced configurations, transfer traffic information to a Python-based machine learning model for real-time anomaly detection and response.
  1. Simulate Cyber Threats to Test Defense Mechanisms
  • DDoS Attack Simulation:
    • Make high-rate UDP or TCP traffic to a server to utilize numerous nodes, to replicate a DDoS attack. Observe how the defense mechanisms manage the traffic.
  • Port Scanning:
    • Replicate the port scanning by trying connections on several ports of a target node. Make sure identifying defense and obstructing repeated scanning attempts.
  • Brute-Force Login Simulation:
    • From a single IP, make often login attempts, after a specific threshold is attained, to permit the IPS identifying and to block the IP.
  • Data Exfiltration:
    • Mimic data leakage by locating a node to transmit sensitive data packets to an unauthorized node. Experiment if the defense mechanism detects the unauthorized transfer.
  1. Develop Defense and Monitoring Functions
  • Traffic Monitoring and Threshold Detection:
    • For packet rates, connection attempts, or packet sizes, we can configure thresholds. If thresholds are surpassed then record the event like suspicious.
    • For instance, record and block any IP, which transmits more than 100 packets for every second.
  • Custom Packet Filtering:
    • Execute the filtering functions, which examine packet headers for certain attributes such as source IP, protocol type.
    • Alter or drop the packets depends on security policies that are described for the network.
  • Real-Time Block Lists:
    • We want to keep a block list of IPs or MAC addresses. Append IPs automatically to this list if they see suspicious activity criteria, to obstruct future packets from that IPs.
  1. Set Up Application Layer for Realistic Traffic
  • Normal Traffic:
    • Replicate the legitimate network traffic using UdpEchoClient/UdpEchoServer or OnOffApplication.
    • Different traffic patterns, making realistic usage with browsing, streaming, and file downloads.
  • Malicious Traffic:
    • Mimic high-rate traffic for DDoS attacks utilising OnOffApplication or use custom applications, simulating the brute-force login attempts or port scanning.
  1. Define and Measure Performance Metrics
  • Detection and Blocking Rate:
    • Monitor the percentage of effective detections along with any false positives (normal traffic flagged) or false negatives (missed attacks).
    • We can compute the detection accuracy and blocking effectiveness.
  • Latency and Throughput Impact:
    • Estimate the impact of the defense mechanisms on network delay and data rates.
  • Resource Usage:
    • Monitor CPU and memory usage at nodes to execute the defense mechanisms to measure scalability and performance effect.
  • Network Stability and Availability:
    • During an attack, we need to assess how successfully the defense sustains the network stability, to make sure that critical services stay available.
  1. Simulate and Analyze Results
  • Run Simulation:
    • Experiment diverse situations along with different attack types and intensities, to observe the network with and without defenses active.
    • We equate the outcomes to measure the defense strategies effectiveness.
  • Data Collection and Analysis:
    • Accumulate data on packet flows, blocked IPs, and overall performance metrics with the help of NS3’s tracing tools.
    • Transfer records for post-simulation analysis or load into external tools like Wireshark for packet inspection or Python for advance data processing.
  • Visualization and Reporting:
    • Visualize the detection and blocking rates, traffic flow, and performance parameters using visualization tools such as Matplotlib.
    • Make reports to sum up defense performance with detection accuracy, network effect, and resource utilization.

Example Code Outline for a Network Defense Project with IPS in NS3

Here’s a simple NS3 code structure replicating a DDoS attack and execute a basic intrusion prevention system (IPS) including threshold-based blocking.

#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>

#include <set>

using namespace ns3;

// Simple IPS function to block IPs exceeding a packet threshold

std::set<Ipv4Address> blockedIps;

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

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

uint32_t packetsReceived = ipv4->GetNReceived();

if (packetsReceived > threshold) {

Ipv4Address ip = ipv4->GetAddress(1, 0).GetLocal();

if (blockedIps.find(ip) == blockedIps.end()) {

std::cout << “Blocking IP ” << ip << ” due to high traffic (” << packetsReceived << ” packets)” << std::endl;

blockedIps.insert(ip);

}

}

Simulator::Schedule(Seconds(1.0), &MonitorAndBlock, node, threshold); // Schedule the next check

}

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 IPS Monitoring on Target Node

uint32_t packetThreshold = 100; // Set packet threshold for IPS

Simulator::Schedule(Seconds(1.0), &MonitorAndBlock, targetNode.Get(0), packetThreshold);

// Step 6: Run Simulation

Simulator::Run();

Simulator::Destroy();

return 0;

}

We had presented step-by-step guide with example coding to set up and execute the Network Defense projects using NS3 environment, with further detailed information will be offered in another guide.

phdprojects.org is dedicated to providing excellent services for complete customer satisfaction. We specialize in firewalls, intrusion prevention systems (IPS), traffic filtering, and advanced machine learning for anomaly detection. Reach out to us to start your Network Defense Projects with NS3. We offer a detailed guide to assist you in setting up your project.