In an era marked by a significant increase in devices connected to Internet Protocol (IP) networks, the complexity of network management has intensified. This growth, primarily driven by machine-to-machine (M2M) communications, necessitates innovative solutions to manage and secure extensive networks efficiently. A new method for automated network topology discovery and secure data storage has been introduced, utilizing machine learning (ML) to enhance network management.
The Challenge of Modern Networks
The explosion in network-connected devices has made it increasingly difficult for traditional network management methods to keep pace. Accurate and up-to-date network topology information is essential for diagnostics, resource allocation, and security tasks. However, inferring network topology often needs help with incomplete data and the dynamic nature of modern networks.
A Dual-Module Solution
This innovative solution consists of two key components: a network topology inference algorithm and a secure data storage module.
Network Topology Inference
Developed in Python, the network topology inference algorithm uses procedural methods involving monitors and a Network Operating Center (NOC). Monitors gather network traces and send them to the NOC, which processes the data to map the network. This approach can accurately reconstruct network architectures, even with partial information, and detect changes such as the addition or removal of nodes. Regularly updated topologies enable administrators to make informed decisions regarding network improvements and troubleshooting, while detecting changes in topology can identify unauthorized devices and potential security threats.
Secure Data Storage
To ensure the security and integrity of the inferred network topologies, the system integrates a robust data storage solution. This solution securely stores topology data, creating an immutable and tamper-resistant record. The decentralized nature of this storage technology eliminates the need for a central authority, enhancing trust among network stakeholders. It ensures that stored topological information remains secure and tamper-proof, and decentralized storage enhances the scalability and reliability of network management systems.
Experimental Success
Using Mininet, a network simulator, the system’s performance was rigorously tested. Simulations demonstrated the accuracy and efficiency of the network topology inference algorithm across various network configurations. The algorithm accurately mapped network topologies, ensuring high precision and recall in diverse scenarios. The system effectively managed dynamic network changes, maintaining accurate topology maps even in the presence of blocking routers and firewalls.
Looking Forward
This dual-module approach offers a robust solution for modern network management, combining the power of machine learning and secure data storage. Future enhancements will include advanced alias resolution techniques and utilizing inferred topologies for vulnerability scanning and cyber risk management.
About the Author: Sharath Chandra Macha
Sharath Chandra Macha is a seasoned inventor in Information Technology, specializing in Cloud Applications and Security Operations. His expertise spans IBM QRadar, HackerOne, Virtual Assistant technology, Generative AI, Machine Learning, and identity management systems. Sharath has been recognized for his innovative contributions with awards from CVS/Aetna and CBRE. He holds a Bachelor’s degree in Electronics and Communication Engineering and a Master’s degree in Computer Science, and he is currently pursuing a Doctorate degree. His dedication to ongoing education and excellence in IT strategy and execution is evident in his work.
Published by: Martin De Juan
