Intrusion Prevention Projects examples using omnet++

In network security, Intrusion Prevention Systems (IPS) is vital and build to identify and avoid malevolent activities in real-time. Use OMNeT++ to simulate different IPS situations to learn how these systems are executed and enhanced to guard network against interruptions. Below are some samples of Intrusion Prevention projects that can be implemented using OMNeT++:

Basic Signature-Based Intrusion Prevention System

Objective: Compare the network traffic and database of attack signatures to imitate a signature-based IPS that identifies and evade known attacks.
Implementation: Develop a network where nodes transmit traffic. Implement an IPS module that validates packets for known attack signatures (like SQL injection, buffer overflow). When a match is found, the IPS blocks the traffic and record the incident.
Extension: Expand the signature database with a wide diversity of attack types. Simulate various attacks like DDoS, malware transmission, or web application attacks, and assess the IPS’s effectiveness in inhibiting these threats.

Anomaly-Based Intrusion Prevention System

Objective: Detect deviations from normal network actions to simulate anomaly-based IPS that identifies and blocks interferences.
Implementation: Generate a network where normal traffic patterns are accomplished over time. Execute an IPS that uses statistical analysis or machine learning to state “normal” behavior and inevitably blocks traffic that turns significantly from this baseline.
Extension: Examine the system with different kinds of anomalies like unusual traffic spikes, illegal access attempts, or unexpected protocol usage. Compare the performance and precision of several anomaly detection methods.

Hybrid Intrusion Prevention System

Objective: Simulate a hybrid IPS that associations both signature-based and anomaly-based detection techniques to enhance intrusion prevention precision.
Implementation: Execute a network where an IPS module first uses signature-based detection to rapidly detect known attacks and then performs anomaly-based detection to catch unknown or emerging threats. The system robotically blocks any identified threats.
Extension: Evaluate the trade-offs amongst detection precision, false positives, and system resource usage. Analyze the system’s performance during different attack scenarios as well as sophisticated, multi-vector attacks.

Intrusion Prevention in Software-Defined Networks (SDN)

Objective: Mimic an IPS tailored for Software-Defined Networks (SDN), where the network control plane is detached from the data plane, permitting for more dynamic and centralized intrusion prevention.
Implementation: Execute an IPS module that offers SDN controller communications and data plane activities. The IPS can dynamically update flow rules in reply to identified threats, hindering mischievous traffic in real-time.
Extension: Examine the IPS from SDN-specific attacks like flow rule manipulation, control plane attacks, or data plane poisoning. Assess the system’s capacity to adapt and respond rapidly to evolving threats in an SDN environment.

Intrusion Prevention for IoT Networks

Objective: Imitate an IPS designed for Internet of Things (IoT) networks, where devices frequently have restricted resources and distinct traffic patterns.
Implementation: Develop an IoT network with different devices (e.g., sensors, actuators) communicating with a central hub. Execute an IPS that observes IoT traffic for signs of meddling like abnormal interaction frequencies or illegal access attempts, and blocks suspicious activities.
Extension: Examine the IPS against IoT-specific challenges like botnet formation, device hijacking, or data tampering. Assess the balance amongst security and resource consumption on IoT devices.

Intrusion Prevention in Wireless Networks

Objective: Simulate an IPS that guards wireless networks against common wireless threats like rogue access points, unauthorized attacks, and eavesdropping.
Implementation: Set up a wireless network that has several access points and clients. Execute an IPS that observes wireless-specific metrics (such as signal strength, MAC address behavior) to identify and block malicious activities.
Extension: Mimic wireless attacks includes fake APs, MAC spoofing, or denial of service, and assess the efficiency of the IPS in identifying and averting these threats.

Distributed Intrusion Prevention System (DIPS)

Objective: Simulate a dispersed IPS where several nodes cooperate to identify and block intrusions over a large or segmented network.
Implementation: Build a network with several sub-networks, each guarded by its own IPS node. Implement a coordination mechanism where IPS nodes share notification and collaborate to evade distributed attacks like botnets or coordinated DDoS attacks.
Extension: Evaluate the performance of the distributed IPS in terms of identification speed, precision and resource usage. Compare the effectiveness of a distributed approach using centralized IPS.

Real-Time Intrusion Prevention with Automated Response

Objective: Emulate an IPS that not only identifies and blocks interruptions in real-time but also takes automated actions to alleviate the impact of the intrusion.
Implementation: Execute an IPS that, upon detecting an intrusion, activates systemised replies like isolating compromised nodes, rerdirecting traffic, or updating firewall rules. The system records all behaviors for later analysis.
Extension: Simulate various kinds of interruptions like ransomware attacks or lateral movement, and analyze the efficiency of the systemized replies in containing and ease the threats.

Intrusion Prevention with Honeypot Integration

Objective: Imitate an IPS that integrates with honeypots to draw and detect attackers, permitting the IPS to block malicious activities before they reach vital systems.
Implementation: Deploy honeypot nodes inside the network that imitate weak systems. The IPS observes interactions with the honeypots and blocks any IP addresses or activities allied with malevolent activities.
Extension: Evaluate the data aggregated by the honeypots and use it to optimize the IPS’s detection potential. Analyze the efficiency of various kinds of honeypots (for instance: low-interaction vs. high-interaction) in improving network defense.

Intrusion Prevention with Threat Intelligence Integration

Objective: Simulate an IPS that influences external threat intelligence feeds to dynamically update its finding and prevention ability.
Implementation: Deploy an IPS that frequently updates its signature database, anomaly detection models, and other security rules depends on the real-time threat intelligence feeds. The IPS blocks traffic allied with known threats and adjusts to emerging pressures.
Extension: Imitate situations where new threats are presented to the network, and analyze how rapidly and effectively the IPS react using threat intelligence. Compare the performance of the IPS with and without threat intelligence incorporation.

Machine Learning-Based Intrusion Prevention System

Objective: Simulate an IPS that uses machine learning algorithms to forecast and prevent impositions as per the network traffic patterns.
Implementation: Execute a machine learning-based IPS that constantly learns from network traffic data to detect potential threats. The IPS uses foretelling analytics to block traffic that is likely to be malevolent based on learned patterns.
Extension: Train the IPS on a dataset of network traffic and estimate its performance from different kinds of attacks as well as novel or zero-day threats. Assess the system’s ability to adapt to varying attack patterns over time.

Behavioral Intrusion Prevention System

Objective: Simulate an IPS that concentrates on identifying and preventing intrusions based on user and system activity analysis.
Implementation: Execute an IPS that observes user activities, system processes, and network communication to identify abnormal behavior indicative of interference. The system mechanically blocks activities that deviate significantly against normal behavior.
Extension: Investigate the IPS from insider threats, lateral movement, or data exfiltration tries, and analyze how well it identifies and prevents these threats based on behavioral analysis.

Intrusion Prevention for Cloud Environments

Objective: Mimic an IPS designed for cloud-based environments, where numerous virtual machines (VMs) and containers may be running on common infrastructure.
Implementation: Set up a simulated cloud environment with many VMs and deploy an IPS that oversee inter-VM traffic, resource usage, and virtual network configurations to detect and block interruptions.
Extension: Emulate cloud-specific attacks like VM escape, hypervisor compromise, or inter-tenant data leakage, and assess the IPS’s efficiency in preventing these challenges in a cloud environment.

Adaptive Intrusion Prevention System

Objective: Imitate an adaptive IPS that alters its identification and prevention techniques based on real-time network conditions and danger levels.
Implementation: Execute an IPS that observes network performance, threat levels, and resource availability to dynamically modify its detection thresholds, signature updates, and response strategies. The system intent to uphold optimal protection deprived of overloading network resources.
Extension: Simulate situations with changing network loads and threat levels, and compute how well the flexible IPS balances security with performance. Compare its efficiency with a static IPS configuration.

Through this procedure, we had successfully provided the sample examples of Intrusion Prevention Projects with the help of OMNeT++ tool. we can also offer additional information or other examples of this projects over another simulation. In the context of Intrusion Prevention Projects utilizing the OMNeT++ tool, we provide exemplary project performance. For optimal simulation results, please reach out to omnet-manual.com.