# Malware Detection in IOT Systems Using Machine Learning Techniques

arXiv: https://arxiv.org/abs/2312.17683 · HTML: https://mlsanity.com/paper/2312.17683/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 29 Dec 2023.

## Builds on (most cited first, 3 of 3)

- [Lightweight Classification of IoT Malware based on Image Recognition](https://mlsanity.com/paper/1802.03714/) (2018)
- [A Haystack Full of Needles: Scalable Detection of IoT Devices in the Wild](https://mlsanity.com/paper/2009.01880/) (2020)
- [IoT Malware Network Traffic Classification using Visual Representation and Deep Learning](https://mlsanity.com/paper/2010.01712/) (2020)

## Built on by (most cited first, 1 of 1)

- [An adaptive network-based approach for advanced forecasting of cryptocurrency values](https://mlsanity.com/paper/2401.05441/) (2024)

## Abstract (arXiv)

Malware detection in IoT environments necessitates robust methodologies. This study introduces a CNN-LSTM hybrid model for IoT malware identification and evaluates its performance against established methods. Leveraging K-fold cross-validation, the proposed approach achieved 95.5% accuracy, surpassing existing methods. The CNN algorithm enabled superior learning model construction, and the LSTM classifier exhibited heightened accuracy in classification. Comparative analysis against prevalent techniques demonstrated the efficacy of the proposed model, highlighting its potential for enhancing IoT security. The study advocates for future exploration of SVMs as alternatives, emphasizes the need for distributed detection strategies, and underscores the importance of predictive analyses for a more powerful IOT security. This research serves as a platform for developing more resilient security measures in IoT ecosystems.