Harnessing the Power of Decision Trees to Detect IoT Malware

TL;DR

This paper introduces a decision tree-based method for detecting IoT malware, demonstrating high accuracy and outperforming existing solutions using real-world data.

Contribution

The paper presents a novel, simple decision tree approach for IoT malware detection that achieves superior performance on a real-world dataset.

Findings

Achieves 97.23% precision and 95.89% recall.

Outperforms existing state-of-the-art methods.

Provides high accuracy and F1-score.

Abstract

Due to its simple installation and connectivity, the Internet of Things (IoT) is susceptible to malware attacks. Being able to operate autonomously. As IoT devices have become more prevalent, they have become the most tempting targets for malware. Weak, guessable, or hard-coded passwords, and a lack of security measures contribute to these vulnerabilities along with insecure network connections and outdated update procedures. To understand IoT malware, current methods and analysis ,using static methods, are ineffective. The field of deep learning has made great strides in recent years due to their tremendous data mining, learning, and expression capabilities, cybersecurity has enjoyed tremendous growth in recent years. As a result, malware analysts will not have to spend as much time analyzing malware. In this paper, we propose a novel detection and analysis method that harnesses the power and simplicity of decision trees. The experiments are conducted using a real word dataset, MaleVis which is a publicly available dataset. Based on the results, we show that our proposed approach outperforms existing state-of-the-art solutions in that it achieves 97.23% precision and 95.89% recall in terms of detection and classification. A specificity of 96.58%, F1-score of 96.40%, an accuracy of 96.43.