Detection of Application-Layer Dos Attacks in IoT Devices Using Feature Selection and Machine Learning Models

Mustabeen Aziz; Muhammad Usman Sana; Tayybah Kiren; Tahmina Ehsan; Alvena Ehsan; Fateha Minahil

Authors

Mustabeen Aziz Department of Information Technology, University of Gujrat, Pakistan
Muhammad Usman Sana Department of Software Engineering, University of Gujrat, Pakistan
Tayybah Kiren Department of Computer Science (RCET), University of Engineering and Technology Lahore, Pakistan
Tahmina Ehsan Department of Information Technology, University of Gujrat, Pakistan
Alvena Ehsan Department of Information Technology, University of Gujrat, Pakistan
Fateha Minahil Department of Information Technology, University of Gujrat, Pakistan

Keywords:

Distributed Denial of Service (DDoS), Cybersecurity, Internet of Things, Feature Selection

Abstract

With technological advancements, innovations like the Internet of Things (IoT) have become widespread, connecting more devices to the Internet. However, as the number of connected devices increases, cyber-attacks—especially Distributed Denial of Service (DDoS) attacks—are also becoming more frequent. This research explores these cyber threats, focusing on DDoS attacks, and proposes strategies to protect IoT devices. It specifically aims to detect DDoS attacks in IoT devices using feature selection methods and machine learning algorithms. The study targets attack detection at the application layer of IoT devices by analyzing a relevant dataset. By applying feature selection techniques and machine learning models, we strive to enhance the accuracy and efficiency of DDoS detection, ultimately improving IoT security

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