Abstract

This project introduces a novel approach to enhance the efficiency of Intrusion Detection Systems (IDS) in Mobile Ad Hoc Networks (MANETs). With the growing reliance on MANETs for critical communications in military and emergency scenarios, securing these networks from various cyber threats is paramount. The proposed solution leverages advanced machine learning algorithms to detect and mitigate potential intrusions dynamically, thereby ensuring robust network security with minimal performance overhead.

Index Terms

• Mobile Ad Hoc Networks (MANETs)
• Intrusion Detection System (IDS)
• Machine Learning
• Network Security
• Cybersecurity

Introduction

Mobile Ad Hoc Networks (MANETs) are increasingly prevalent in various applications, particularly where traditional network infrastructure is unavailable or impractical. These networks, however, are highly susceptible to security threats due to their dynamic topology and lack of centralized control. An efficient Intrusion Detection System (IDS) is crucial for these environments to preemptively identify and respond to security breaches. This project aims to develop a novel IDS that not only detects intrusions but also adapts to the ever-changing topology of MANETs with minimal impact on network performance.

Existing System

Existing intrusion detection systems for MANETs often rely on static, rule-based approaches that are not effective against new or evolving threats. Furthermore, these systems can significantly burden the network resources, leading to degraded performance and reduced throughput. Many of these systems fail to scale with larger network sizes or adapt to the dynamic nature of MANETs, making them less effective in real-world applications.

Proposed System

The proposed system introduces a machine learning-based IDS tailored for MANETs. Key components and innovations include:

• Dynamic learning capabilities: The system uses supervised and unsupervised learning to continuously adapt its detection algorithms based on incoming data, enhancing its responsiveness to new threats.
• Decentralized detection mechanism: Instead of relying on a single detection node, the IDS is distributed across multiple nodes within the network, allowing for faster response times and reduced network traffic.
• Resource efficiency: The model is designed to be lightweight, minimizing its computational and bandwidth footprint on the network nodes.
• Anomaly and signature-based detection: Integrates both anomaly detection to catch new, unknown threats, and signature-based detection for known vulnerabilities.

Conclusion

The novel approach proposed for enhancing the efficiency of intrusion detection in Mobile Ad Hoc Networks represents a significant advancement in network security. By integrating machine learning algorithms and a decentralized architecture, the system is not only capable of detecting a wide range of intrusions but also does so efficiently, preserving the essential performance characteristics of MANETs. Future enhancements will focus on improving the learning algorithms’ accuracy, reducing false positives, and further minimizing the system’s impact on network resources. The ultimate goal is to provide a robust, scalable, and self-adaptive IDS that can protect MANETs in diverse and challenging environments.