Enhancing Data Protection in IoT Through Firewall and Intrusion Detection Frameworks

The Internet of Things (IoT) has transformed modern living by interconnecting billions of devices that generate and exchange sensitive data. However, the distributed nature and resource constraints of IoT systems make them highly vulnerable to cyber threats, including denial-of-service (DoS) attacks, malware injection, and data exfiltration. Traditional security solutions such as centralized firewalls and signature-based intrusion detection struggle to safeguard data in these environments. This paper proposes a data protection framework that integrates distributed firewalls with an AI-driven Intrusion Detection System (IDS) to secure IoT networks. The system employs lightweight micro-firewalls at IoT gateways for local traffic filtering, while a hybrid CNN-LSTM model performs anomaly detection on network traffic. To ensure the integrity of event logs, blockchain-based mechanisms are integrated for tamper-proof recording. Experiments on NSL-KDD and IoT-23 datasets demonstrate the framework’s effectiveness, achieving 96.7% detection accuracy, reducing false positives by 30%, and maintaining low overhead for deployment in constrained IoT devices