A Layered Machine Learning and Smart Contract Framework for DDoS Botnet Defense in IoT Networks

Abstract

As the Internet of Things (IoT) continues to expand across various domains, the number of connected devices is rapidly increasing, exposing IoT environments to large-scale Distributed Denial-of-Service (DDoS) botnet attacks. Due to limited computational and memory resources, IoT devices remain particularly vulnerable to traffic flooding and coordinated malicious behavior. This paper presents a layered security framework that integrates machine learning, protected gateway servers, and blockchain-based smart contracts to detect and mitigate DDoS botnet attacks in IoT environments. The proposed model performs behavioral anomaly detection off-chain using a two-stage machine learning process, while leveraging smart contracts on the blockchain for tamper-resistant logging, automated policy enforcement, and controlled economic penalties. A bounded spending mechanism and quarantine policy are introduced to discourage large-scale malicious traffic while limiting the impact on compromised legitimate devices. The system architecture and enforcement algorithms are presented to demonstrate the feasibility, scalability, and security advantages of the proposed framework.