Attack Detection in IoT using combined classification and HHO
Subject Areas : Information Technology in Engineering Design (ITED) Journal
محمدحسین Ekhtiari
1
,
mehdi jafari
2
,
Mahdiyeh Eslami
3
1 - Department of Electrical Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran
2 - Department of Electrical Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran
3 - Department of Electrical Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran
Keywords: Anomaly detection, penetration detection, Harris hawks optimization, data mining,
Abstract :
Computer networks are exposed to various types of attacks due to their vulnerability. Due to many characteristics of network traffic, machine learning models are time-consuming to identify attacks. In this article, the aim is to provide a new method to detect the penetration of attacks. The new method for detecting network intrusion is to use machine learning technique and Shahin Harris optimization algorithm in order to increase the accuracy of detection in detecting intrusion in computer networks.The proposed method is that first classification is done with the help of individual classifications and then diagnosis is done using the final classification. The proposed method is tested on the NSL-KDD dataset. Accuracy, recall and correctness criteria are used to evaluate the proposed method. The accuracy of the proposed method is above 98% in the best test mode, and its validity can be confirmed based on comparison with other sources.
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