Enhancing Data Protection in IoT Through Firewall and Intrusion Detection Frameworks

Abstract

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.

Introduction

The text discusses the challenges and solutions for securing Internet of Things (IoT) networks, which are growing rapidly but face increasing risks due to the large number of connected devices. Traditional security tools like centralized firewalls and signature-based intrusion detection systems (IDS) are not suitable for IoT environments due to their scale and limitations in detecting new or evolving threats. To address this, the article proposes a new IoT security framework that integrates distributed firewalls, AI-driven IDS, blockchain-based logging, and federated learning to ensure effective and adaptive data protection.

Key Points:

1. Challenges with Traditional Security Tools:

   • Traditional tools are designed for conventional IT networks and are ill-suited for IoT due to the large number of devices and real-time data flows.

   • They struggle to detect zero-day attacks and evolving threats, and their use can lead to performance bottlenecks in IoT devices with limited resources.

2. Proposed Security Framework:
   The framework aims to enhance IoT data protection by combining four key components:

   • Distributed Firewalls: Lightweight firewalls are deployed at IoT gateways and edge devices to filter malicious traffic locally, reducing latency and preventing large-scale attacks.

   • AI-Driven IDS: A hybrid CNN-LSTM (Convolutional Neural Network + Long Short-Term Memory) model is used to detect both short-term and long-term network threats. This model outperforms traditional anomaly-based IDS by achieving higher detection accuracy.

   • Blockchain-Based Logging: Intrusion alerts and firewall events are logged on an immutable blockchain ledger, ensuring tamper-proof records and improving trust and transparency.

   • Federated Learning: This technique allows IoT devices to train models locally, sharing only learned parameters with a central system, which improves adaptability and privacy.

3. Evaluation and Performance:

   • The proposed framework was tested using two datasets: NSL-KDD (a standard intrusion detection dataset) and IoT-23 (which contains IoT-specific attack data such as botnets).

   • The CNN-LSTM IDS achieved 96.7% accuracy on IoT-23, outperforming traditional systems and reducing false positives by 30%.

   • Distributed firewalls reduced detection latency by 25% compared to centralized methods.

   • Blockchain logging introduced minimal overhead (~5%) but provided transparency and tamper-proof logs.

   • Federated learning helped improve the system’s adaptability to diverse IoT environments without transferring sensitive raw data.

4. Results and Comparison:
   The proposed framework significantly improved performance over existing models in key areas:

   • Accuracy: 96.7% (compared to 88.5% in traditional IDS).

   • False Positive Rate (FPR): 10.5% (down from 18.0% in traditional IDS).

   • Latency Reduction: 25% improvement over centralized approaches.

   • Overhead: 4.8% overhead (compared to higher overhead in other frameworks like ELBA-IoT).

   These results demonstrate that the proposed multi-layered defense system is more efficient, scalable, and secure for IoT networks than traditional and existing frameworks.

Conclusion

This paper presented a framework to enhance data protection in IoT through the integration of distributed firewalls, AI-driven intrusion detection, and blockchain-based logging. By combining these components, the system provides scalable, lightweight, and intelligent security that is well-suited for IoT environments. The experimental evaluation demonstrated high accuracy, reduced false positives, and resilience against evolving threats, while maintaining efficiency for resource-constrained devices. In the future, research will focus on improving lightweight blockchain protocols tailored for IoT, as well as incorporating explainable AI (XAI) techniques into IDS models. This will not only enhance detection accuracy but also provide greater transparency, enabling administrators to understand and trust the system’s decisions. Ultimately, such advancements will move IoT security closer to the goal of robust, transparent, and fully reliable data protection.

References

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Copyright

Copyright © 2025 Ravi Kant Vyas, Vikas Somani, S K Dargar. 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.