Increasing Security in Internet of Things Networks by Transfer Learning
A. Samiee
1
(
School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
)
E. Khedersolh
2
(
School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
)
H .S. Shahhoseini
3
(
School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
)
Keywords: Internet of things, intrusion detection systems, transfer learning, deep learning.,
Abstract :
Intrusion detection systems (IDS) play a fundamental role in the defense of computer networks by detecting unauthorized access and investigating possible security breaches. Traditional IDSs face difficulties in combating newly developed sophisticated security attacks. Hence, there is a growing need for an automated intrusion detection solution that can detect malicious activities more accurately. In this paper, a new model of Internet of Things (IoT) network intrusion detection using a neural network based on the pre-trained VGG16 architecture is proposed. The model is a two-stage process: in the first stage, some layers of VGG16 are pre-trained on the ImageNet dataset, and we transfer its weights; in the second stage, fine-tuning is performed specifically to detect penetration in IoT networks. These networks have specific vulnerabilities due to resource limitations, unique protocols, and a variety of devices that require their own intrusion detection methods. The model is applied to UNSW-NB15 and NSL-KDD datasets. The results show that the proposed method can learn effectively by producing real performance in terms of accuracy, precision, recall, and F1 score.
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