How to Start Intrusion Detection System Projects Using NS3

To start an Intrusion Detection System (IDS) project using NS3 that requires to contain replicating the network traffic, capturing data, and improving detection logic, detecting the malicious activities. In NS3, to configure IDS project, we can experiment diverse detection techniques, replicate attacks, and examine how successfully the IDS can be identified anomalies. This guide walks you through starting and executing IDS simulations in NS3.

Steps to Start IDS Projects in NS3

  1. Define Project Objectives and Scope
  • Identify IDS Use Cases:
    • Anomaly-Based Detection: From typical traffic patterns, we detect the deviations to identify potential intrusions.
    • Signature-Based Detection: We can utilize known attack patterns or signatures to identify certain kinds of malicious activity.
    • Behavior-Based Detection: Observe and examine the node’s behaviour to identify unusual actions or network behaviours like excessive login attempts.
    • Real-Time or Offline Detection: Determine whether IDS will be functioned in real-time (identifying attacks if they occur) or offline (examining logs post-attack).
  • Key Performance Metrics:
    • Detection Rate: For both true positives (correctly identified attacks) and false positives (normal traffic flagged as attacks), we estimate the rate of detection.
    • Latency and Throughput: Monitor how the IDS impacts of network performance such as delay and data rate.
    • False Positive and Negative Rates: Measure the reliability of IDS in differentiating among the malicious and benign traffic.
    • Resource Utilization: Observe CPU and memory usage, knowing the performance effect of IDS logic on network nodes.
  1. Install and Set Up NS3
  • Download NS3: Go to the official NS3 website, we can download the new version of it.
  • Install NS3: We adhere to the installation guide and to check by executing the example scripts.
  • External Libraries (Optional): Install Python and libraries such as NumPy, Pandas, and Scikit-Learn for advanced data analysis, as incorporating the machine learning for anomaly detection.
  1. Design the Network Topology
  • Select Topology Layout:
    • Star or Tree Topology: It is helpful for centralized IDS, to observe traffic to and from a main server.
    • Mesh or Peer-to-Peer Network: It is appropriate for decentralized IDS within a network without a central monitoring point.
    • Segregated Network Segments: To use IDS on each segment for zone-specific monitoring if replicating diverse different zones to utilize routers or switches to isolate segments.
  • Configure Nodes and Devices:
    • Make network nodes to denote the clients, servers, routers, and IDS nodes using NodeContainer.
    • For wired connections, install CsmaHelper or WifiHelper for wireless network configurations.
  1. Implement Data Collection for IDS
  • Packet Capture and Logging:
    • Seize packets on certain nodes or links to utilize NS3’s PcapTrace or AsciiTrace.
    • Allow logging of packet headers, timestamps, source/destination IPs, protocols, and payloads, examining the traffic patterns.
  • Selective Capture:
    • Study data collection at high-value nodes like servers or routers to seize the traffic to movie in and beyond the critical points.
    • Detect certain packet types such as TCP, UDP or traffic at specific ports like port 80 for HTTP to focus the analysis.
  • Storage and Export:
    • We need to store packet data, recording files for offline analysis. We can transfer captured data for in-depth analysis including Python or utilize tools such as Wireshark for visualisation.
  1. Simulate Network Attacks for IDS Testing
  • DDoS Attack:
    • Create high-rate UDP or TCP traffic to a single target node using several nodes, to experiment the capability of IDS identifying the unusual traffic volumes.
  • Port Scanning:
    • Configure a node to examine the target node’s open ports, analysing the IDS’ detection of often connection attempts at various ports.
  • Brute-Force Login Simulation:
    • We can transmit a large volume of login requests to a server node, to replicate the brute-force attacks, and then observe for abnormal login patterns.
  • Data Exfiltration:
    • Replicate the data exfiltration by setting up a node, transmitting sensitive data packets to an unauthorized external server.
  1. Develop and Integrate IDS Detection Logic
  • Rule-Based Detection:
    • Make basic rules to detect the known patterns of attacks like high request rates, unusual IP addresses, or certain protocol signatures.
    • For instance, we can identify the DDoS by measuring packets from numerous sources to a single destination in a set timeframe.
  • Threshold-Based Anomaly Detection:
    • For anomalies, execute the threshold-based detection like:
      • Packet rate thresholds such as flagging when a node obtains more than X packets for each second.
      • Packet size or payload content analysis.
    • If a threshold is surpassed then record the event like suspicious activity.
  • Machine Learning (Advanced):
    • Distribute packet data to Python or other ML environments, then to categorize the traffics like typical or suspicious using machine learning algorithms (e.g., clustering, anomaly detection models).
    • This method needs offline processing by reason of NS3’s restricted support for real-time ML integration.
  1. Set Up Application Layer for Realistic Traffic
  • Normal Traffic:
    • Replicate the legitimate traffic to utilize applications such as UdpEchoClient/UdpEchoServer or OnOffApplication.
    • Set various traffic types like HTTP, FTP to create the IDS’s task more realistic by making a diverse baseline of typical activity.
  • Attack Traffic:
    • Configure the high-rate OnOffApplication for DDoS, TcpSocketFactory for port scanning, or custom payloads, replicating the data exfiltration.
    • Alter attack types and intensities, analysing the versatility and effectiveness of IDS.
  1. Define and Measure Performance Metrics
  • Detection Metrics:
    • Log true positives (correct attack detections), false positives (normal traffic flagged as attacks), false negatives (missed attacks), and true negatives (correctly identified non-attack traffic).
    • We can measure the detection rate, false positive rate, and false negative rate.
  • Latency and Throughput:
    • Compute the IDS on network performance effect that especially any more delay or minimized data rates.
  • Resource Usage:
    • Monitor the CPU and memory usage on nodes to execute the IDS logic, estimating the overhead and scalability.
  1. Simulate and Analyze Results
  • Run Simulations:
    • Experiment diverse situation like various attack types, intensities, and thresholds, measuring the effectiveness of IDS.
    • We equate the outcomes with and without the IDS active, estimating the detection performance and overhead.
  • Data Collection:
    • Accumulate the data on traffic flows, packet delays, and detection events utilising NS3’s tracing tools. For post-simulation analysis, we can save logs.
  • Visualization and Analysis:
    • Envision the traffic patterns, detection accuracy, and other parameters to utilize Matplotlib or Gnuplot.
    • Make logs to sum up detected attacks, key IP addresses included, and any performance influence over the network.

Example Code Outline for Basic IDS with DDoS Detection in NS3

Here’s a simple coding, which replicates a DDoS attack and uses a basic IDS logic identifying abnormal traffic 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;

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;

}

Simulator::Schedule(Seconds(1.0), &MonitorTraffic, 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: Set Up IDS Monitoring with Traffic Threshold

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

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

// Step 6: Run Simulation

Simulator::Run();

Simulator::Destroy();

return 0;

}

This guide provides a step-by-step instruction with example coding to initiate and simulate the Intrusion Detection System projects using NS3 platform, with further details to be shared on this topic.

Send to phdprojects.org where we will guide you to start your  Intrusion Detection System (IDS) project using NS3 tool, we are then number one team to give you on time services and best topics aligned in  a perfect way as per your research  needs