| Title | Investigation of Machine Learning Techniques in Intrusion Detection System for IoT Network |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Swarna Sugi, S. Shinly, Ratna, S. Raja |
| Conference Name | 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS) |
| Date Published | dec |
| Keywords | Conferences, Deep Learning, feature extraction, Internet of Things, Intrusion detection, intrusion detection system, IoT security, machine learning, machine learning algorithms, security |
| Abstract | Internet of Things (IoT) combines the internet and physical objects to transfer information among the objects. In the emerging IoT networks, providing security is the major issue. IoT device is exposed to various security issues due to its low computational efficiency. In recent years, the Intrusion Detection System valuable tool deployed to secure the information in the network. This article exposes the Intrusion Detection System (IDS) based on deep learning and machine learning to overcome the security attacks in IoT networks. Long Short-Term Memory (LSTM) and K-Nearest Neighbor (KNN) are used in the attack detection model and performances of those algorithms are compared with each other based on detection time, kappa statistic, geometric mean, and sensitivity. The effectiveness of the developed IDS is evaluated by using Bot-IoT datasets. |
| DOI | 10.1109/ICISS49785.2020.9315900 |
| Citation Key | swarna_sugi_investigation_2020 |
Investigation of Machine Learning Techniques in Intrusion Detection System for IoT Network