# LocKedge: Low-Complexity Cyberattack Detection in IoT Edge Computing

arXiv: https://arxiv.org/abs/2011.14194 · HTML: https://mlsanity.com/paper/2011.14194/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges](https://mlsanity.com/paper/2108.00974/) (2021)
- [Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications](https://mlsanity.com/paper/2310.05269/) (2023)
- [Federated Deep Learning for Intrusion Detection in IoT Networks](https://mlsanity.com/paper/2306.02715/) (2023)
- [Are Trees Really Green? A Detection Approach of IoT Malware Attacks](https://mlsanity.com/paper/2506.07836/) (2025)
- [OptiFLIDS: Optimized Federated Learning for Energy-Efficient Intrusion Detection in IoT](https://mlsanity.com/paper/2510.05180/) (2025)
- [CLAD: A Clustered Label-Agnostic Federated Learning Framework for Joint Anomaly Detection and Attack Classification](https://mlsanity.com/paper/2605.06571/) (2026)

## Abstract (arXiv)

Internet of Things and its applications are becoming commonplace with more devices, but always at risk of network security. It is therefore crucial for an IoT network design to identify attackers accurately, quickly and promptly. Many solutions have been proposed, mainly concerning secure IoT architectures and classification algorithms, but none of them have paid enough attention to reducing the complexity. Our proposal in this paper is an edge cloud architecture that fulfills the detection task right at the edge layer, near the source of the attacks for quick response, versatility, as well as reducing the workload of the cloud. We also propose a multi attack detection mechanism called LocKedge Low Complexity Cyberattack Detection in IoT Edge Computing, which has low complexity for deployment at the edge zone while still maintaining high accuracy. LocKedge is implemented in two manners: centralized and federated learning manners in order to verify the performance of the architecture from different perspectives. The performance of our proposed mechanism is compared with that of other machine learning and deep learning methods using the most updated BoT IoT data set. The results show that LocKedge outperforms other algorithms such as NN, CNN, RNN, KNN, SVM, KNN, RF and Decision Tree in terms of accuracy and NN in terms of complexity.