# Lightweight Classification of IoT Malware based on Image Recognition

arXiv: https://arxiv.org/abs/1802.03714 · HTML: https://mlsanity.com/paper/1802.03714/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2018.

## Built on by (most cited first, 13 of 13)

- [MalNet: A Large-Scale Image Database of Malicious Software](https://mlsanity.com/paper/2102.01072/) (2021)
- [IoT Malware Detection Architecture using a Novel Channel Boosted and Squeezed CNN](https://mlsanity.com/paper/2202.04121/) (2022)
- [A New Deep Boosted CNN and Ensemble Learning based IoT Malware Detection](https://mlsanity.com/paper/2212.08008/) (2022)
- [Malware Classification Using Deep Boosted Learning](https://mlsanity.com/paper/2107.04008/) (2021)
- [Burning the Adversarial Bridges: Robust Windows Malware Detection Against Binary-level Mutations](https://mlsanity.com/paper/2310.03285/) (2023)
- [Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Survey](https://mlsanity.com/paper/2310.10665/) (2023)
- [Deep Multi-Task Learning for Malware Image Classification](https://mlsanity.com/paper/2405.05906/) (2024)
- [A Survey on Cross-Architectural IoT Malware Threat Hunting](https://mlsanity.com/paper/2306.07989/) (2023)
- [Task-Aware Meta Learning-based Siamese Neural Network for Classifying Obfuscated Malware](https://mlsanity.com/paper/2110.13409/) (2021)
- [On deceiving malware classification with section injection](https://mlsanity.com/paper/2208.06092/) (2022)
- [New Approach to Malware Detection Using Optimized Convolutional Neural Network](https://mlsanity.com/paper/2301.11161/) (2023)
- [Malware Detection in IOT Systems Using Machine Learning Techniques](https://mlsanity.com/paper/2312.17683/) (2023)
- [CADeSH: Collaborative Anomaly Detection for Smart Homes](https://mlsanity.com/paper/2303.01021/) (2023)

## Abstract (arXiv)

The Internet of Things (IoT) is an extension of the traditional Internet, which allows a very large number of smart devices, such as home appliances, network cameras, sensors and controllers to connect to one another to share information and improve user experiences. Current IoT devices are typically micro-computers for domain-specific computations rather than traditional functionspecific embedded devices. Therefore, many existing attacks, targeted at traditional computers connected to the Internet, may also be directed at IoT devices. For example, DDoS attacks have become very common in IoT environments, as these environments currently lack basic security monitoring and protection mechanisms, as shown by the recent Mirai and Brickerbot IoT botnets. In this paper, we propose a novel light-weight approach for detecting DDos malware in IoT environments.We firstly extract one-channel gray-scale images converted from binaries, and then utilize a lightweight convolutional neural network for classifying IoT malware families. The experimental results show that the proposed system can achieve 94.0% accuracy for the classification of goodware and DDoS malware, and 81.8% accuracy for the classification of goodware and two main malware families.