Intrusion Detection System projects examples using omnet++

Intrusion Detection Systems (IDS) are critical modules in network security that is intended to monitor network traffic and classify the suspicious activities or potential threats. Using OMNeT++, we can mimic different kinds of IDSs and evaluate their efficiency in numerous network environments.

At omnet-manual.com, we undertake Intrusion Detection System projects utilizing OMNeT++ due to our team of highly skilled developers who ensure timely completion of your projects.

The given below are the some samples of IDS projects that can be implemented using OMNeT++:

1. Signature-Based Intrusion Detection System

• Objective: To emulate signature-based IDS that identifies known attacks by matching network traffic against a database of attack signatures.
• Implementation: Plan a network with multiple nodes that interchange the traffic. Execute an IDS module that examines the traffic and compares it to a database of known attack signatures like patterns of DDoS attacks, SQL injections. When a match is found, the IDS raise an alert.
• Extension: Extend the signature database with several attack types and evaluate the IDS’s efficiency in identifying the numerous kinds of attacks. Emulate attacks such as SYN flood, port scanning, and malware communication to validate the IDS.

2. Anomaly-Based Intrusion Detection System

• Objective: To emulate anomaly-based IDS that classifies the deviations from normal network behaviour to detect the potential threats.
• Implementation: Generate a network in which normal traffic patterns are introduced over time. Execute IDS that uses statistical models or machine learning to describe “normal” behaviour. The IDS increases alerts when traffic diverges expressively from these patterns.
• Extension: To emulate the several network scenarios like sudden spikes in traffic that represents a potential DDoS attack or unusual access times, and assess the IDS’s ability to identify these anomalies. Associate the performance of various anomaly detection algorithms.

3. Hybrid Intrusion Detection System

• Objective: To execute a hybrid IDS that integrates both signature-based and anomaly-based detection approaches to enhance the detection accuracy.
• Implementation: Execute a network with an IDS module that first uses signature-based detection to rapidly identify known attacks, and then implements an anomaly-based detection to catch unknown or emerging threats.
• Extension: Evaluate the trade-offs among the detection accuracy and resource consumption like CPU, memory when using hybrid detection. Validate the IDS against both known attacks and new, unseen attack patterns.

4. Distributed Intrusion Detection System (DIDS)

• Objective: To mimic a distributed IDS where multiple IDS nodes collaborate to monitor and evaluate network traffic in a dispersed in network environment.
• Implementation: Generate a network topology with several sub-networks, each monitored by its own IDS node. Execute a coordination mechanism in which IDS nodes distributes alerts and relate data to identify distributed attacks, like botnets or coordinated DDoS attacks.
• Extension: To emulate a large-scale network attack and measure how well the dispersed IDS coordinates to identify and respond to the threat. Compare the detection performance with a centralized IDS approach.

5. Host-Based Intrusion Detection System (HIDS)

• Objective: Ape a HIDS that track the activities on individual hosts like system logs, file integrity, and process activity, to identify the intrusions.
• Implementation: Enterprise a network in which each host like servers, workstations executes an HIDS module that observes local activities, like unauthorized file access, changes to critical system files, or abnormal process behavior.
• Extension: Integrates HIDS with network-based IDS (NIDS) to generate a inclusive intrusion detection system that observes both host activities and network traffic. Evaluate how the combined system enhances the detection capabilities.

6. Real-Time Intrusion Detection and Response System

• Objective: To mimic real-time IDS that not only classifies intrusions but also takes immediate actions to prevent the threat, like blocking traffic or separating the compromised nodes.
• Implementation: Execute IDS in the network that observes traffic in real-time and activates automated responses like updating firewall rules or blocking IP addresses when a threat is identified.
• Extension: Assess the efficiency of numerous automated responses in containing the threat and reducing the damage. Emulate the attacks such as worm propagation or ransomware, and evaluate how rapidly and effectively the IDS respond.

7. Intrusion Detection System for Wireless Networks

• Objective: To emulate IDS designed especially for wireless networks that concentrate on detecting attacks such as rogue access points, deauthentication attacks, and eavesdropping.
• Implementation: Generate a wireless network with multiple mobile nodes. Execute IDS that observes wireless-specific metrics like signal strength, MAC address behaviour, and channel usage, to classify wireless network intrusions.
• Extension: Validate the IDS against common wireless attacks like fake APs or MAC spoofing, and measure the efficiency of detection and response mechanisms in a mobile environment.

8. Machine Learning-Based Intrusion Detection System

• Objective: To emulate IDS that uses machine learning algorithms to learn from network traffic data and enhance its detection ability over time.
• Implementation: Execute an IDS that implements machine learning algorithms like supervised learning such as decision trees, support vector machines or unsupervised learning like clustering, anomaly detection, to categorize traffic as normal or malicious.
• Extension: Train the IDS on a dataset of network traffic and then validate it on a numerous dataset to measure its ability to simplify and identify the new attacks. Compare the performance of various machine learning techniques.

9. Intrusion Detection System for IoT Networks

• Objective: To emulate IDS tailored for Internet of Things (IoT) networks in which devices are usually resource-constrained and have distinct traffic patterns.
• Implementation: Plan an IoT network with multiple devices interacting with a central server and execute IDS those observers IoT-specific traffic patterns like communication frequency and data payload sizes, to identify the anomalies.
• Extension: To emulate attacks especially to IoT environments, like botnet formation or device hijacking, and measure the IDS’s capability to identify these threats. evaluate the trade-offs among the detection accuracy and resource consumption on IoT devices.

10. Intrusion Detection in Software-Defined Networks (SDN)

• Objective: Emulate an IDS for a Software-Defined Network (SDN) in which the network control plane is isolated from the data plane, making it a distinct environment for intrusion detection.
• Implementation: Execute IDS that monitors SDN-specific elements like control plane messages and flow rules, to identify anomalies or malicious activities. The IDS can be incorporated with the SDN controller for real-time monitoring and response.
• Extension: Validate the IDS against SDN-specific attacks, like flow table overflows, controller-targeted attacks, or data plane manipulation. Measure how the integration with the SDN controller improves the detection and response abilities.

11. Intrusion Detection System for Cloud Environments

• Objective: To emulate an IDS that planned for cloud environments, in which the multiple virtual machines (VMs) and containers can be running on shared infrastructure.
• Implementation: Generate a simulated cloud environment with multiple VMs and executes IDS that observes inter-VM traffic, resource usage, and virtual network configurations to identify the intrusions.
• Extension: To emulate the cloud-specific attacks, like VM escape, hypervisor compromise, or inter-tenant data leakage, and assess the IDS’s efficiency in detecting and preventing these threats.

12. Intrusion Detection System with Honeypot Integration

• Objective: Mimic IDS that incorporates with honeypots to lure attackers and collect information about their approaches, while also securing the network.
• Implementation: Implement honeypot nodes within the network and configure the IDS to observe the communication with these honeypots. The IDS can use the data collected by honeypots to improve its detection capabilities.
• Extension: Measure the efficiency of honeypots in attracting attackers and the IDS’s capability to use this data to robust the network security. Evaluate the setup against numerous attack scenarios to measure the combined approach.

13. Intrusion Detection System with Threat Intelligence Integration

• Objective: To emulate IDS that incorporates with external threat intelligence feeds to improve its capabilities to identify the known and emerging threats.
• Implementation: Execute IDS that frequently updates its detection abilities based on threat intelligence feeds that has encompasses to known malicious IP addresses, attack signatures, and indicators of compromise (IoCs).
• Extension: Mimic the IDS’s performance with and without threat intelligence incorporation to assess the effect on detection accuracy and speed. Evaluate how rapidly the IDS can familiarize to new threats based on real-time intelligence.

In this demonstration we clearly showed the sample projects that related to the intrusion detection system that were executed in OMNeT++ simulation tool. Additional specific details were also provided about the intrusion detection system.