An Automated Network Threat Detection and Response System using Blockchain Technology

Abstract

For modern digital networks, it has become necessary for them to have faster and more accurate mechanisms for the identification of malicious activities carried out through them. In this regard, the existing security mechanisms, which mostly rely on rule-based approaches, have proven to be inadequate, especially regarding the recording of malicious activities. To overcome these challenges, the current paper has proposed a new security mechanism based on the integration of deep learning techniques and blockchain technology for the identification of malicious activities carried out through digital networks. In the proposed system, a CNN-based approach has been utilized, where the model has been trained using the CICIDS2017 dataset for the identification of malicious activities carried out through digital networks, including the identification of the type of attack carried out by the attackers, i.e., through a multi- class approach. Once the threat is identified, the level of severity of the threat is assessed, and response measures are taken to mitigate the threat, such as rate limiting, quarantining of suspicious connections, and blocking of malicious sources. This reduces the time taken to respond to a threat and removes the necessity of human involvement in the response mechanism. Additionally, all the identified threats and response measures taken to mitigate the threat are stored securely using a blockchain ledger. A dashboard is created to display the results of threat detection, system performance, and blockchain status in a real-time environment using a web-based application. The proposed system is evaluated experimentally to prove the efficiency of the system in providing accurate results in threat detection with minimal response time.

Introduction

The text discusses the growing need for advanced cybersecurity systems due to increasing network usage and rising cyber threats. Traditional rule-based security systems are no longer sufficient to handle modern and unknown attacks efficiently.

To address this, the proposed approach combines deep learning (CNN), blockchain technology, and automated response systems. The CNN model analyzes network traffic to detect and classify attacks (using datasets like CICIDS2017), while blockchain ensures secure, tamper-proof storage of all detected threats and actions. An automated response module immediately mitigates attacks by actions such as blocking IPs or limiting traffic, reducing dependence on human intervention. A web-based dashboard provides real-time visualization of threats and system activity.

The literature review highlights that:

• AI methods like CNNs, RNNs, and reinforcement learning improve threat detection accuracy.
• Blockchain provides secure and immutable logging but may introduce latency.
• Software Defined Networking (SDN) can help in dynamic threat response.
• Existing datasets (e.g., CICIDS2017, UNSW-NB15) are essential for training models.
• Key challenges include computational cost, blockchain delays, and adapting to evolving attacks.

The proposed system integrates:

• CNN-based intrusion detection,
• Blockchain-based threat logging,
• Automated real-time response mechanisms,
• A monitoring dashboard.

Conclusion

This paper presented an automated threat detection and response system that integrates deep learning techniques with blockchain technology to enhance network security. The proposed framework uses a CNN model to accurately detect and classify malicious network traffic while leveraging blockchain to securely log detected incidents and response actions in an immutable manner. Experimental evaluation using the CICIDS2017 dataset demonstrates that the system achieves high detection accuracy with a very low false positive rate. The automated response mechanism ensures immediate mitigation of detected threats, reducing reliance on manual intervention and minimizing potential network damage. The blockchain component adds transparency, auditability, and trustworthiness to security logs, which are essential for forensic analysis and compliance requirements. The inclusion of a web-based dashboard further improves usability by providing real- time visualization of detected threats, system performance metrics, and blockchain status. By combining accurate detection, automated response, and tamper-proof logging, the proposed system offers a comprehensive and efficient solution for modern network security challenges. Overall, the results confirm that integrating CNN-based threat detection with blockchain- based logging significantly enhances the effectiveness, reliability, and transparency of cybersecurity systems.

References

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Copyright

Copyright © 2026 Praveena B, Priyanga Saleth Mary S, Kanisha G. 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.