Network Threat Detection projects examples using omnet++

To implement the Network threat detection has includes finding malicious activities or security breaches in a network. Using OMNeT++, we can mimic several scenarios to learn how various threat detection mechanisms can be executed and assessed. Given below is some instances of network threat detection projects that can be executed using OMNeT++:

1. Signature-Based Threat Detection System

• Objective: Mimic a signature-based threat detection system that finds known attacks by relating network traffic against a database of attack signatures.
• Implementation: Create a network where nodes interchange traffic. Execute a detection module that checks traffic for signatures of known attacks like SQL injection, cross-site scripting. When a match is obtained, the system activates an alert.
• Extension: Extend the signature database to contain a wide variety of attacks. Mimic various attack kinds, like DDoS or malware transmission, and estimate the system’s efficiency in detecting these threats.

2. Anomaly-Based Threat Detection System

• Objective: Mimic an anomaly-based threat detection system that finds potential threats by detecting deviations from normal network behaviour.
• Implementation: Make a network where normal traffic patterns are ascertained over time. Execute an anomaly detection module that uses statistical techniques or machine learning to state “normal” behaviour and raises alerts when deviations happen.
• Extension: Examine the system with several kinds of network anomalies, like unauthorized access attempts, or unexpected protocol usage, and unusual traffic spike. Compare the act of various anomaly detection algorithms.

3. Real-Time Network Threat Detection and Response System

• Objective: Mimic a system that detects threats in real-time and gets instant actions to mitigate them, like blocking traffic or isolating compromised nodes.
• Implementation: Create a network where a threat detection module observes traffic in real-time. When a threat is identified, the system activates automated responses, like updating firewall rules or blocking particular IP addresses.
• Extension: Mimic attacks such as ransomware spread or worm propagation and calculate how rapidly and efficiently the system responds. Assess the impact of real-time threat detection on network performance.

4. Behavioural Threat Detection in IoT Networks

• Objective: Mimic a behavioural threat detection system customised for Internet of Things (IoT) networks, where devices have particular communication patterns.
• Implementation: Design an IoT network with many devices like sensors, actuators interactive with a central server. Execute a detection module that observes the behaviour of IoT devices and increases alerts if rare patterns are observed, like abnormal frequency of communication or unpredicted data payloads.
• Extension: Assess the system versus IoT-specific threats, like device hijacking, botnet formation, or data tampering. Examine the trade-offs among detection accuracy and resource consumption on IoT devices.

5. Machine Learning-Based Threat Detection System

• Objective: Mimic a threat detection system that leverages machine learning algorithms to classify potential threats based on network traffic patterns.
• Implementation: Execute a network where traffic data is gathered and used to train a machine learning model. The model is then used to incoming traffic to categorise it as normal or malicious. Algorithms such as decision trees, support vector machines, or neural networks can be consumed.
• Extension: Teach the model on a labelled dataset of network traffic and assess its performance on unseen data. Compare the efficiency of various machine learning algorithms and discover the impact of training data quality on detection accurateness.

6. Intrusion Detection System with Threat Intelligence Integration

• Objective: Mimic an Intrusion Detection System (IDS) that incorporates external threat intelligence feeds to improve its ability to detect known and emerging threats.
• Implementation: Execute an IDS that frequently updates its detection capabilities based on threat intelligence feeds, as well as known malicious IP addresses, attack signatures, and indicators of compromise (IoCs). The IDS applies this intelligence to display network traffic and detect threats.
• Extension: Emulate the IDS’s performance with and without threat intelligence integration to assess the impact on detection accuracy and speed. Examine how rapidly the IDS can adjust to new threats based on real-time intelligence.

7. Distributed Threat Detection System (DIDS)

• Objective: Mimic a distributed threat detection system where several nodes work together to monitor and evaluate network traffic in a distributed network environment.
• Implementation: Generate a network topology with numerous sub-networks, each monitored by its own detection node. Execute a coordination mechanism where detection nodes distribute alerts and correlate data to find distributed attacks, like botnets or corresponding DDoS attacks.
• Extension: Emulate a large-scale network attack and calculate how successfully the distributed system coordinates to detect and respond to the threat. Compare the detection performance with a centralized detection system method.

8. Threat Detection in Software-Defined Networks (SDN)

• Objective: Mimic a threat detection system designed exactly for Software-Defined Networks (SDN), where the network control plane is split from the data plane.
• Implementation: Execute a detection module that monitors SDN-specific elements, like control plane messages and flow rules, to detect anomalies or malicious activities. The detection system can be incorporated with the SDN controller for real-time monitoring and response.
• Extension: Check the system versus SDN-specific attacks, like flow table overflows, controller-targeted attacks, or data plane manipulation. Estimate how the integration with the SDN controller improves detection and response capabilities.

9. Threat Detection for Cloud Environments

• Objective: Mimic a threat detection system created for cloud environments, where several virtual machines (VMs) and containers may be running on shared organization.
• Implementation: Make a mimicked cloud environment including numerous VMs and execute a detection system that monitors inter-VM traffic, resource usage, and virtual network configurations to detect threats.
• Extension: Mimic cloud-specific attacks, like VM escape, hypervisor compromise, or inter-tenant data leakage, and estimate the system’s efficiency in detecting and mitigating these threats.

10. Covert Channel Detection in Network Traffic

• Objective: Emulate a system that detects covert channels in the network traffic, where data is hidden in normal communication streams to avoid detection.
• Implementation: Execute a detection module that examines network traffic for signs of covert channels, like unusual packet timing, packet size anomalies, or encoding of hidden data in protocol fields. The system increases alerts when hidden communication is detected.
• Extension: Mimic various kinds of covert channels, like timing channels or protocol manipulation, and assess the system’s ability to detect them. Compare the effectiveness of various detection methods in finding covert channels.

11. Behavioural Threat Detection in Wireless Networks

• Objective: Mimic a behavioural threat detection system for wireless networks, concentrating on classifying threats based on deviations from normal wireless communication patterns.
• Implementation: Create a wireless network with numerous nodes, containing access points and client devices. Execute a detection module that displays wireless-specific metrics, like signal strength, MAC address behaviour, and channel usage, to detect anomalies.
• Extension: Examine the system versus common wireless threats, like rogue access points, MAC spoofing, or deauthentication attacks, and assess the detection system’s efficiency in finding and responding to these threats.

12. Threat Detection in Industrial Control Systems (ICS)

• Objective: Mimic a threat detection system customized for Industrial Control Systems (ICS), where the focus is on detecting threats that could disturb critical infrastructure operations.
• Implementation: Make a network representing an ICS environment, comprising SCADA systems, sensors, and actuators. Execute a detection module that observes ICS-specific protocols like Modbus, DNP3 and detects anomalies that could indicate a cyberattack, like unauthorized commands or abnormal sensor readings.
• Extension: Mimic attacks on the ICS network, like command injection or replay attacks, and assess the detection system’s ability to classify and mitigate these threats. Improve countermeasures that can be automatically activated in response to detected threats.

13. Early Warning Threat Detection System

• Objective: Emulate an early warning threat detection system that classifies potential threats before they can fully manifest, offering time for pre-emptive action.
• Implementation: Execute a detection module that uses predictive analytics and early indicators of compromise (IoCs) to detect the first stages of an attack, like reconnaissance or privilege escalation. The system makes alerts that permit for proactive measures to be taken.
• Extension: Check the system’s ability to detect the early stages of numerous kinds of attacks, like spear-phishing, lateral movement, or malware installation. Evaluate how early detection develops complete network security and decreases the impact of potential threats.

In this setup, we had learned more details about the instances of network threat detection projects that can be implemented using OMNeT++. More informations will be offered based on your requirements.

Kickstart your Network Threat Detection projects with the OMNeT++ tool! We provide best network performance for your projects. Reach out to omnet-manual.com for the best simulation results. We focus on a variety of threat detection methods that can be implemented and evaluated.