Dynamic Network Mapping and Traffic Intelligence for Cyber Simulations

Abstract

Ensuring seamless data flow while identifying irregularities is essential in network management. This project utilizes Long Short-Term Memory (LSTM) neural networks to predict network traffic patterns and applies the Isolation Forest algorithm to detect anomalies within the data. A synthetic dataset, designed to reflect fluctuations in traffic, is employed to train the LSTM model for forecasting upcoming traffic volumes. Simultaneously, the Isolation Forest algorithm flags unusual traffic behavior, which may signal network disruptions, cyber threats, or performance bottlenecks. This dual-method strategy strengthens network performance, boosts security, and optimizes resource distribution by delivering precise traffic forecasts and prompt anomaly alerts.

Introduction

The Framework for Network Topology Generation and Traffic Prediction Analytics is an integrated system designed to improve cybersecurity training by automating realistic network simulations and forecasting network traffic using Deep Learning (LSTM) and Machine Learning (Isolation Forest) techniques. It enables organizations to conduct effective cyber exercises by replicating authentic network behavior, detecting anomalies, and assessing defenses in a controlled environment.

Key components include:

• Network topology simulation using NetworkX to model adaptable network layouts with realistic link metrics.

• Synthetic traffic generation and preprocessing to mimic real-world patterns for model training.

• LSTM-based traffic forecasting to predict future network loads, helping prevent congestion and optimize resources.

• Anomaly detection to identify unusual traffic indicative of cyber threats.

The system supports dynamic real-time network mapping, advanced traffic analysis, cyber-attack simulation, and enhanced threat detection. Validated models are saved for ongoing use, ensuring efficient and consistent performance without frequent retraining.

Conclusion

The framework designed for generating network topology and performing traffic prediction analytics significantly strengthens the cybersecurity stance of contemporary digital infrastructures. With the rise in advanced cyber threats, the ability to analyze and anticipate network activity is becoming crucial for proactively mitigating potential security breaches.

References

[1] Barabási, A.-L. (2002). Linked: The New Science of Networks. Perseus Publishing.Offers essential knowledge on network science and the modeling of complex systems, forming a theoretical base for understanding network structures. [2] Zhang, Y., & Paxson, V. (2000). Detecting Stepping Stones. USENIX Security Symposium.A key resource for grasping techniques used to detect intrusions by analyzing patterns within network traffic. [3] Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly Detection: A Survey.Provides an in-depth overview of various anomaly detection strategies that are highly relevant for cybersecurity data analysis.

Copyright

Copyright © 2025 Karthikeyan S , Sarankumar N . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.