  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connections and outdated update procedures.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.
