IOT intrusion detection using deep transfer learning technique | |
| Author | Nattaya Sriphop |
| Call Number | AIT RSPR no.TC-23-02 |
| Subject(s) | Internet of things Machine learning Artificial intelligence |
| Note | A research study submitted in partial fulfillment of the requirements the degree of Master of Engineering in Telecommunications |
| Publisher | Asian Institute of Technology |
| Abstract | The Internet of Things (IoT) has become an integral part of people’s lives, connecting various aspects such as health, transportation, and entertainment. However, the con venience of IoT comes at a cost, as these devices can access sensitive information and are vulnerable to various intrusions. Attack classification is an essential aspect of IoT security, as it helps to identify the type and severity of attacks. This paper presents an experimental study of Deep Transfer Learning (DTL) for intrusion detection in IoT networks. The experiment uses a pre-trained VGG-19 model and the IoT-23 dataset to evaluate the accuracy of DTL compared to traditional deep learning (DL) and machine learning (ML) approaches. The focus of the experiment is on attack classification to identify the di erent types of intrusions that can occur in an IoT network. The results show that DTL is an e ective technique for intrusion detection in IoT networks to im prove traditional convolution neural networks (CNN). The experiment demonstrates the potential of deep learning-based approaches for improving IoT security and provides insights into e ective intrusion detection techniques that can be used to protect IoT de vices and networks. In conclusion, this research highlights the importance of attack classification in IoT security and presents an experimental study of DTL for intrusion detection. The results of this study can inform the development of more e ective IoT security solutions, ultimately contributing to the protection of sensitive information and the prevention of attacks on IoT devices and networks. |
| Year | 2023 |
| Type | Research Study Project Report (RSPR) |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Telecommunications (TC) |
| Chairperson(s) | Attaphongse Taparugssanagorn |
| Examination Committee(s) | Teerapat Sanguankotchakorn;Poompat Saengudomlert |
| Scholarship Donor(s) | MEA-AIT Academic Cooperation Program;AIT Scholarships |
| Degree | Research Studies Project Report (M. Eng.) - Asian Institute of Technology, 2023 |