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Optimized feature selection and zero-parameter channel attention BiLSTM for RPL-attack classification in IoT

Sudha Rani Unnam¹, Kareemulla Shaik¹

¹School of Computer Science and Engineering, VIT-A.P. University, Amaravati, Andra Pradesh, India.

|June 18, 2026

Summary

This study introduces an optimized deep learning framework to detect and classify routing attacks in Internet of Things (IoT) networks using the Routing Protocol for Low-Power and Lossy Networks (RPL). The method achieves high accuracy in identifying various RPL attacks.

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Area of Science:

Cybersecurity
Network Security
Deep Learning Applications

Background:

Internet of Things (IoT) adoption presents security challenges, especially in resource-constrained networks using the Routing Protocol for Low-Power and Lossy Networks (RPL).
Conventional security solutions are often ineffective due to the limitations of RPL networks.
RPL-based routing attacks pose a significant threat to the integrity and availability of IoT communications.

Purpose of the Study:

To develop and evaluate an optimized deep learning framework for detecting and classifying RPL-based routing attacks.
To enhance the security of RPL networks against common routing threats.
To address the limitations of traditional security measures in constrained IoT environments.

Main Methods:

Related Experiment Videos

An optimized deep learning framework integrating Chaotic Pied Kingfisher Optimization (Ch-PKO) for feature selection and a Zero-parameter Channel Attention Bidirectional Long Short-Term Memory (ZCAtt-BiLSTM) model for classification.

Data preprocessing techniques including cleaning, one-hot encoding, and Pareto scaling for improved data quality and learning stability.

Evaluation on the IoT-RPL dataset, specifically targeting Blackhole, Flooding, DODAG Version Number, and Decreased Rank attacks.

Main Results:

The framework achieved high performance metrics: 99.305% accuracy, 98.57% F1-score, 0.61% false discovery rate, and 97.949% Matthews Correlation Coefficient (MCC).
Comparative analysis demonstrated superior performance compared to baseline models.
The proposed method effectively detects and classifies various RPL routing attacks.

Conclusions:

The optimized deep learning framework offers a robust solution for securing RPL-based IoT networks against routing attacks.
While effective on simulated data, further research is needed to address the computational cost for real-time deployment.
The study highlights the potential of advanced optimization and deep learning techniques in enhancing IoT security.