# Efficient IoT Intrusion Detection with an Improved Attention-Based CNN-BiLSTM Architecture

arXiv: https://arxiv.org/abs/2503.19339 · HTML: https://mlsanity.com/paper/2503.19339/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 25 Mar 2025.

## Builds on (most cited first, 2 of 2)

- [Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection](https://mlsanity.com/paper/1803.04967/) (2018)
- [CellSecure: Securing Image Data in Industrial Internet-of-Things via Cellular Automata and Chaos-Based Encryption](https://mlsanity.com/paper/2309.11476/) (2023)

## Abstract (arXiv)

The ever-increasing security vulnerabilities in the Internet-of-Things (IoT) systems require improved threat detection approaches. This paper presents a compact and efficient approach to detect botnet attacks by employing an integrated approach that consists of traffic pattern analysis, temporal support learning, and focused feature extraction. The proposed attention-based model benefits from a hybrid CNN-BiLSTM architecture and achieves 99% classification accuracy in detecting botnet attacks utilizing the N-BaIoT dataset, while maintaining high precision and recall across various scenarios. The proposed model's performance is further validated by key parameters, such as Mathews Correlation Coefficient and Cohen's kappa Correlation Coefficient. The close-to-ideal results for these parameters demonstrate the proposed model's ability to detect botnet attacks accurately and efficiently in practical settings and on unseen data. The proposed model proved to be a powerful defense mechanism for IoT networks to face emerging security challenges.