# An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks

arXiv: https://arxiv.org/abs/2101.12270 · HTML: https://mlsanity.com/paper/2101.12270/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 64 references that are in the map.
- First posted on arXiv 10 Jan 2021.

## Builds on (most cited first, 4 of 4)

- [Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset](https://mlsanity.com/paper/1811.00701/) (2018)
- [Machine Learning in IoT Security: Current Solutions and Future Challenges](https://mlsanity.com/paper/1904.05735/) (2019)
- [Evaluation of Machine Learning Algorithms for Intrusion Detection System](https://mlsanity.com/paper/1801.02330/) (2018)
- [IoT Device Fingerprint using Deep Learning](https://mlsanity.com/paper/1902.01926/) (2019)

## Built on by (most cited first, 8 of 8)

- [E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT](https://mlsanity.com/paper/2103.16329/) (2021)
- [Effect of Balancing Data Using Synthetic Data on the Performance of Machine Learning Classifiers for Intrusion Detection in Computer Networks](https://mlsanity.com/paper/2204.00144/) (2022)
- [IoTGeM: Generalizable Models for Behaviour-Based IoT Attack Detection](https://mlsanity.com/paper/2401.01343/) (2023)
- [LLM-based Multi-class Attack Analysis and Mitigation Framework in IoT/IIoT Networks](https://mlsanity.com/paper/2510.26941/) (2025)
- [Modelling DDoS Attacks in IoT Networks using Machine Learning](https://mlsanity.com/paper/2112.05477/) (2021)
- [BS-GAT Behavior Similarity Based Graph Attention Network for Network Intrusion Detection](https://mlsanity.com/paper/2304.07226/) (2023)
- [Network Traffic Classification Using Machine Learning, Transformer, and Large Language Models](https://mlsanity.com/paper/2503.02141/) (2025)
- [Are Trees Really Green? A Detection Approach of IoT Malware Attacks](https://mlsanity.com/paper/2506.07836/) (2025)

## Abstract (arXiv)

In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices in IoT networks can be problematic due to their resource-constrained nature, and integrating security on these devices is often overlooked. This has resulted in attackers having an increased incentive to target IoT devices. As the number of attacks possible on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS. In this work, ML algorithms are compared for both binary and multi-class classification on Bot-IoT dataset. Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms. In the case of HTTP distributed denial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, other simulation results-based precision, recall, F1 score, and log loss metric reveal that RF outperforms on all types of attacks in binary classification. However, in multi-class classification, KNN outperforms other ML algorithms with an accuracy of 99%, which is 4% higher than RF.