Intrusion detection in the Internet of Things based on a multilayer combination of misuse and anomaly detection systems
محورهای موضوعی : journal of Artificial Intelligence in Electrical Engineering
Hossein Khosrovifar
1
,
Mohammad Ali Jabraeil Jamali
2
,
Kambiz Majidzadeh
3
,
Mohammad Masdari
4
1 - Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
2 - Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran
3 - Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran.
4 - Islamic Azad University, Urmia Branch
کلید واژه: Internet of Things, Intrusion detection, Machin learning, Classifier systems, GMDH neural network.,
چکیده مقاله :
In light of the rapid and insecure growth of devices connected to the Internet of Things (IoT), intrusion detection systems are recognized as one of the effective security mechanisms in this domain. These systems face significant challenges, including vast amounts of data with numerous features and imbalanced distribution, data imbalance, resource limitations, unknown attack detection, and a high rate of false alarms. This paper introduces a new model for developing an intrusion detection system known as Hybrid Multi-Layer System (HMLS), aimed at reducing false alarms and increasing accuracy in detecting both known and unknown attacks. In the proposed method, a dataset collected from network traffic is preprocessed before being fed into a multi-layer classifier that identifies specific categories of attacks at each layer based on a hybrid intrusion detection framework called Hybrid System of Misuse and Anomaly (HSoMA). Simulation results using the NSL-KDD dataset indicate that the proposed method improves evaluation metrics by 5.49%, 1.09%, and 4.5% in terms of Accuracy, Precision, and False Alarm rates compared to previous works.
In light of the rapid and insecure growth of devices connected to the Internet of Things (IoT), intrusion detection systems are recognized as one of the effective security mechanisms in this domain. These systems face significant challenges, including vast amounts of data with numerous features and imbalanced distribution, data imbalance, resource limitations, unknown attack detection, and a high rate of false alarms. This paper introduces a new model for developing an intrusion detection system known as Hybrid Multi-Layer System (HMLS), aimed at reducing false alarms and increasing accuracy in detecting both known and unknown attacks. In the proposed method, a dataset collected from network traffic is preprocessed before being fed into a multi-layer classifier that identifies specific categories of attacks at each layer based on a hybrid intrusion detection framework called Hybrid System of Misuse and Anomaly (HSoMA). Simulation results using the NSL-KDD dataset indicate that the proposed method improves evaluation metrics by 5.49%, 1.09%, and 4.5% in terms of Accuracy, Precision, and False Alarm rates compared to previous works.
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