Enhancing IoT Security Through Deep Learning-Based Intrusion Detection
- Title
- Enhancing IoT Security Through Deep Learning-Based Intrusion Detection
- Creator
- Jyotsna A.; Mary Anita E.A.
- Description
- The Internet of Things (IoT) has revolutionized the way we interact with technology by connecting everyday devices to the internet. However, this increased connectivity also poses new security challenges, as IoT devices are often vulnerable to intrusion and malicious attacks. In this paper, we propose a deep learning-based intrusion detection system for enhancing IoT security. The proposed work has been experimented on IoT-23 dataset taken from Zenodo. The proposed work has been tested with 10 machine learning classifiers and two deep learning models without feature selection and with feature selection. From the results it can be inferred that the proposed work performs well with feature selection and in deep learning model named as Gated Recurrent Units (GRU) and the GRU is tested with various optimizers namely Follow-the-Regularized-Leader (Ftrl), Adaptive Delta (Adadelta), Adaptive Gradient Algorithm (Adagrad), Root Mean Squared Propagation (RmsProp), Stochastic Gradient Descent (SGD), Nesterov-Accelerated Adaptive Moment Estimation (Nadam), Adaptive Moment Estimation (Adam). Each evaluation is done with the consideration of highest performance metric with low running time. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
- Source
- Communications in Computer and Information Science, Vol-1823 CCIS, pp. 95-105.
- Date
- 2023-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Adaptive Moment Estimation; Edge Computing; Gated Recurrent Units; Intrusion Detection; IoT-23
- Coverage
- Jyotsna A., Department of Computer Science and Engineering, Christ (Deemed to Be University), Bangalore, India; Mary Anita E.A., Department of Computer Science and Engineering, Christ (Deemed to Be University), Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 18650929; ISBN: 978-303135298-0
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Jyotsna A.; Mary Anita E.A., “Enhancing IoT Security Through Deep Learning-Based Intrusion Detection,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/19935.