Design and Simulation of a Secure Enterprise IoT Network Using Cisco Packet Tracer with a Federated Learning-Based Secure Method.

Abstract

The rapid growth of the Internet of Things (IoT) in businesses has led to major security issues, with botnet attacks being a serious threat. Although Federated Learning (FL) provides a way to detect threats while preserving privacy, it is still vulnerable to data poisoning from harmful devices. Current blockchain solutions for securing FL are often too heavy on resources for widespread use in IoT. This paper offers a two-part integrated approach. First, an enterprise IoT network was designed and simulated securely using a prototype with Cisco Packet Tracer. Second, a new lightweight novel FL framework was developed that did not rely on blockchain, using the N-BaIoT dataset to protect against botnet attacks. The paper proposed Reputation- Weighted Coordinate Median with Update Validity Tests (RWCM+UVT) framework incorporates a reputation-based system, a robust aggregation algorithm, and an adaptive update validation gate. By simulating botnet attacks within this controlled environment, this paper demonstrates that the RWCM+UVT framework effectively identifies and mitigates the impact of malicious devices, achieving near-perfect detection accuracy without the prohibitive overhead of blockchain technology.