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

Widespread and ever-increasing cybersecurity attacks against Internet of Things (IoT) systems are causing a wide range of problems for individuals and organizations. The IoT is self-configuring and open, making it vulnerable to insider and outsider attacks. In the IoT, devices are designed to self-c...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 24; číslo 2; s. 713
Hlavní autoři: Altulaihan, Esra, Almaiah, Mohammed Amin, Aljughaiman, Ahmed
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 01.01.2024
MDPI
Témata:
Access control
Algorithms
Automation
Classification
classifier algorithms
Cyberterrorism
Data mining
Datasets
Denial of service attacks
Discriminant analysis
DoS attacks
feature selection
IDS
Intrusion detection systems
IoT network
Machine learning
Network security
Privacy
Reputation management
Germany
DoS attacks
IDS
machine learning
IoT network
classifier algorithms
feature selection
ISSN:1424-8220, 1424-8220
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Shrnutí:Widespread and ever-increasing cybersecurity attacks against Internet of Things (IoT) systems are causing a wide range of problems for individuals and organizations. The IoT is self-configuring and open, making it vulnerable to insider and outsider attacks. In the IoT, devices are designed to self-configure, enabling them to connect to networks autonomously without extensive manual configuration. By using various protocols, technologies, and automated processes, self-configuring IoT devices are able to seamlessly connect to networks, discover services, and adapt their configurations without requiring manual intervention or setup. Users’ security and privacy may be compromised by attackers seeking to obtain access to their personal information, create monetary losses, and spy on them. A Denial of Service (DoS) attack is one of the most devastating attacks against IoT systems because it prevents legitimate users from accessing services. A cyberattack of this type can significantly damage IoT services and smart environment applications in an IoT network. As a result, securing IoT systems has become an increasingly significant concern. Therefore, in this study, we propose an IDS defense mechanism to improve the security of IoT networks against DoS attacks using anomaly detection and machine learning (ML). Anomaly detection is used in the proposed IDS to continuously monitor network traffic for deviations from normal profiles. For that purpose, we used four types of supervised classifier algorithms, namely, Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (kNN), and Support Vector Machine (SVM). In addition, we utilized two types of feature selection algorithms, the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA) and compared their performances. We also utilized the IoTID20 dataset, one of the most recent for detecting anomalous activity in IoT networks, to train our model. The best performances were obtained with DT and RF classifiers when they were trained with features selected by GA. However, other metrics, such as training and testing times, showed that DT was superior.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s24020713