Decision Trees Unleashed: Simplifying IoT Malware Detection With Advanced AI Techniques

View Sample PDF

Author(s): Derek Mohammed (Capitol Technology University, USA & Saint Leo University, USA)and Marwan Omar (Capitol Technology University, USA & Illinois Institute of Technology, USA)
Copyright: 2024
Pages: 19
Source title: Innovations, Securities, and Case Studies Across Healthcare, Business, and Technology
Source Author(s)/Editor(s): Darrell Norman Burrell (Marymount University, USA)
DOI: 10.4018/979-8-3693-1906-2.ch013

Keywords: Medical Information Science Reference / Medical Technologies / Medicine & Healthcare

Purchase

View Decision Trees Unleashed: Simplifying IoT Malware Detection With Advanced AI Techniques on the publisher's website for pricing and purchasing information.

Abstract

This chapter presents an in-depth study on the application of decision tree-based classifiers for the detection of malware in internet of things (IoT) environments. With the burgeoning expansion of IoT devices, the threat landscape has grown increasingly complex, making traditional security measures insufficient. This study proposes an innovative approach using decision tree algorithms to address the growing concern of IoT malware. The research methodology encompasses a comprehensive analysis of IoT vulnerabilities, focusing on malware threats and the development of a decision tree-based classifier. The classifier is empirically validated using the MaleVis dataset, a rich source of real-world IoT malware data. Performance metrics such as precision, recall, specificity, F1-score, accuracy, and processing time are meticulously evaluated to determine the efficacy of the model.

The IRMA Community

Research IRM

Decision Trees Unleashed: Simplifying IoT Malware Detection With Advanced AI Techniques

Purchase

Abstract

Related Content

IRMA Sponsors