Deep learning algorithms for intrusion detection systems in internet of things using CIC-IDS 2017 dataset
- Title
- Deep learning algorithms for intrusion detection systems in internet of things using CIC-IDS 2017 dataset
- Creator
- Jose J.; Jose D.V.
- Description
- Due to technological advancements in recent years, the availability and usage of smart electronic gadgets have drastically increased. Adoption of these smart devices for a variety of applications in our day-to-day life has become a new normal. As these devices collect and store data, which is of prime importance, securing is a mandatory requirement by being vigilant against intruders. Many traditional techniques are prevailing for the same, but they may not be a good solution for the devices with resource constraints. The impact of artificial intelligence is not negligible in this concern. This study is an attempt to understand and analyze the performance of deep learning algorithms in intrusion detection. A comparative analysis of the performance of deep neural network, convolutional neural network, and long short-term memory using the CIC-IDS 2017 dataset. 2023 Institute of Advanced Engineering and Science. All rights reserved.
- Source
- International Journal of Electrical and Computer Engineering, Vol-13, No. 1, pp. 1134-1141.
- Date
- 2023-01-01
- Publisher
- Institute of Advanced Engineering and Science
- Subject
- CIC-IDS 2017 dataset; Convolution neural network; Deep neural network; Internet of things; Intrusion detection system
- Coverage
- Jose J., Department of Computer Science, CHRIST University, Bangalore, India, Department of Computer Science, Rajagiri College of Social Sciences, Cochin, India; Jose D.V., Department of Computer Science, CHRIST University, Bangalore, India
- Rights
- All Open Access; Gold Open Access
- Relation
- ISSN: 20888708
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Jose J.; Jose D.V., “Deep learning algorithms for intrusion detection systems in internet of things using CIC-IDS 2017 dataset,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/14437.