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Anomaly Detection Ids For Detecting Dos Attacks In Iot Networks Based On Machine Learning Algorithms.

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Anomaly Detection Ids For Detecting Dos Attacks In Iot Networks Based On Machine Learning Algorithms.

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Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms.

Esra Altulaihan¹, Mohammed Amin Almaiah^2,3,4, Ahmed Aljughaiman¹

¹Department of Computer Networks and Communications, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia.

Sensors (Basel, Switzerland)

|January 26, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces an Intrusion Detection System (IDS) using machine learning to combat Denial of Service (DoS) attacks in Internet of Things (IoT) networks. Decision Tree and Random Forest classifiers with Genetic Algorithm feature selection showed the best performance for IoT security.

Keywords:

DoS attacks IDS IoT network classifier algorithms feature selection machine learning

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Area of Science:

Cybersecurity
Machine Learning
Internet of Things (IoT)

Background:

Internet of Things (IoT) systems face increasing cybersecurity threats, including Denial of Service (DoS) attacks, due to their self-configuring and open nature.
These attacks compromise user security and privacy, leading to monetary losses and service disruptions.
Securing IoT networks against sophisticated cyberattacks is a significant and growing concern for individuals and organizations.

Purpose of the Study:

To propose and evaluate an Intrusion Detection System (IDS) designed to enhance the security of Internet of Things (IoT) networks specifically against Denial of Service (DoS) attacks.
To leverage anomaly detection and machine learning (ML) techniques for identifying and mitigating these threats within IoT environments.

Main Methods:

An IDS was developed employing anomaly detection to monitor IoT network traffic for deviations from normal behavior.

Four supervised machine learning classifiers were utilized: Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (kNN), and Support Vector Machine (SVM).

Feature selection was performed using the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA), with performance compared on the IoTID20 dataset.

Main Results:

The Decision Tree (DT) and Random Forest (RF) classifiers achieved the best performance when their features were selected using the Genetic Algorithm (GA).
While DT and RF demonstrated high accuracy, the Decision Tree (DT) classifier exhibited superior performance in terms of training and testing times.
The proposed IDS effectively identified anomalous activities within the IoT network using the IoTID20 dataset.

Conclusions:

The study successfully demonstrated the efficacy of an ML-based IDS in defending IoT networks against DoS attacks.
The combination of Genetic Algorithm feature selection with Decision Tree or Random Forest classifiers offers a promising approach for robust IoT security.
Decision Tree classifiers provide a favorable balance of performance and efficiency for real-time DoS attack detection in IoT systems.