Towards a machine learning-based framework for DDOS attack detection in software-defined IoT (SD-IoT) networks

Aug 4, 2026

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The Internet of Things (IoT) relies on resource-constrained sensors and devices that are highly exposed to security threats, especially Distributed Denial of Services (DDoS) attacks. Integrating Software Defined Networking with IoT can improve security and access control, yet DDoS remains a major risk through botnet or zombie mechanisms. This work proposes a machine learning-based framework for detecting DDoS attacks in an SDN-WISE IoT controller, integrating a detection module, generating and capturing attack traffic, preprocessing logs into a dataset, and classifying packets with Naive Bayes, Decision Tree, and SVM for high detection accuracy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages17

Reading Time43 min

AccessFree to download

Introduction

IoT security challenges and attack surface

Need for scalable security mechanisms

DDoS threat complexity in IoT environments

Towards a machine learning-based framework for DDOS attack detection in software-defined IoT (SD-IoT) networks Jalal Bhayo a, Syed Attique Shah b,∗, Sufian Hameed a, Awais Ahmed c, Jamal Nasir a, Dirk Draheim d a Department of Computer Science, National University of Computer and Emerging Sciences (NUCES-FAST), 75160, Karachi, Pakistan b School of Computing and Digital Technology, Birmingham City University, STEAMhouse, B47RQ, Birmingham, United Kingdom c University of Electronic Science and Technology of China (UESTC), 610056, Sichuan, China d Information Systems Group, Tallinn University of Technology, 12618 Tallinn, Estonia A R T I C L E I N F O Keywords: Internet of things (IoT) DDoS attacks Software defined networks (SDN) SDN-WISE Intrusion detection system (IDS) Machine learning A B S T R A C T The Internet of Things (IoT) is a complex and diverse network consisting of resource-constrained sensors/devices/things that are vulnerable to various security threats, particularly Distributed Denial of Services (DDoS) attacks. Recently, the integration of Software Defined Networking (SDN) with IoT has emerged as a promising approach for improving security and access control mechanisms. However, DDoS attacks continue to pose a significant threat to IoT networks, as they can be executed through botnet or zombie attacks. Machine learning-based security frameworks offer a viable solution to scrutinize the behavior of IoT devices and compile a profile that enables the decision-making process to maintain the integrity of the IoT environment. In this paper, we present a machine learning-based approach to detect DDoS attacks in an SDN-WISE IoT controller. We have integrated a machine learning-based detection module into the controller and set up a testbed environment to simulate DDoS attack traffic generation. The traffic is captured by a logging mechanism added to the SDN-WISE controller, which writes network logs into a log file that is pre-processed and converted into a dataset. The machine learning DDoS detection module, integrated into the SDN-WISE controller, uses Naive Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM) algorithms to classify SDN-IoT network packets. We evaluate the performance of the proposed framework using different traffic simulation scenarios and compare the results generated by the machine learning DDoS detection module. The proposed framework achieved an accuracy rate of 97.4%, 96.1%, and 98.1% for NB, SVM, and DT, respectively. The attack detection module takes up to 30% usage of memory and CPU, and it saves about 70% memory while keeping the CPU free up to 70% to process the SD-IoT network traffic with an average throughput of 48 packets per second, achieving an accuracy of 97.2%. Our experimental results demonstrate the superiority of the proposed framework in detecting DDoS attacks in an SDN-WISE IoT environment. The proposed approach can be used to enhance the security of IoT networks and mitigate the risk of DDoS attacks. 1. Introduction With the advancing Internet of Things (IoT) innovations, there is exponential growth in the inclusion of various kinds of ‘‘things’’/ devices/ sensors/ objects into the Internet. These resource-constrained ‘‘things’’can be an easy target for attackers to launch various types of attacks, including Denial-of-Service (DoS), Man-In-The-Middle (MITM), and malware attacks. In the last decade, the escalating usage of heterogeneous IoT devices has extended challenges related to security, performance, accessibility, and scalability. With this growing IoT dilemma, more connected devices mean more assault vectors and more conceivable outcomes for attackers to target (Wang et al., 2020; Ali et al., 2020). Therefore, there is a high demand to rapidly address these rising security concerns, or IoT applications will face inevitable threats. However, due to the heterogeneous nature of IoT devices, it is challenging to deploy security mechanis

FAQs

What problem does the paper address in SDN-WISE IoT networks?

The paper addresses the challenge of detecting Distributed Denial of Services (DDoS) attacks in software-defined IoT environments, where attacks can be carried out via botnet or zombie techniques.

How is the machine learning detection module integrated and evaluated?

A machine learning-based module is integrated into the SDN-WISE controller, traffic is generated and captured through controller logging, logs are preprocessed into a dataset, and packet classification is evaluated under different simulation scenarios.

Which machine learning algorithms are used for DDoS classification and what results are reported?

Naive Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM) are used. Reported accuracy values are 97.4% (NB), 96.1% (SVM), 98.1% (DT), and an overall framework result of about 97.2% with reduced memory usage.

Aug 4, 2026

Upload by Anda