SCSLnO-SqueezeNet: Sine Cosine-Sea Lion Optimization enabled SqueezeNet for intrusion detection in IoT
- Title
- SCSLnO-SqueezeNet: Sine Cosine-Sea Lion Optimization enabled SqueezeNet for intrusion detection in IoT
- Creator
- Masthan M.; Pazhanikumar K.; Chavan M.; Mandala J.; Prasad Kumar S.N.
- Description
- Security and privacy are regarded as the greatest priority in any real-world smart ecosystem built on the Internet of Things (IoT) paradigm. In this study, a SqueezeNet model for IoT threat detection is built using Sine Cosine Sea Lion Optimization (SCSLnO). The Base Station (BS) carries out intrusion detection. The Hausdorff distance is used to determine which features are important. Using the SqueezeNet model, attack detection is carried out, and the network classifier is trained using SCSLnO, which is developed by combining the Sine Cosine Algorithm (SCA) with Sea Lion Optimization (SLnO). BoT-IoT and NSL-KDD datasets are used for the analysis. In comparison to existing approaches, PSO-KNN/SVM, Voting Ensemble Classifier, Deep NN, and Deep learning, the accuracy value produced by devised method for the BoT-IoT dataset is 10.75%, 8.45%, 6.36%, and 3.51% higher when the training percentage is 90. 2023 Informa UK Limited, trading as Taylor & Francis Group.
- Source
- Network: Computation in Neural Systems, Vol-34, No. 4, pp. 343-373.
- Date
- 2023-01-01
- Publisher
- Taylor and Francis Ltd.
- Subject
- Internet of Things (IoT); intrusion detection; Sea Lion Optimization (SLnO); Sine Cosine algorithm (SCA); SqueezeNet
- Coverage
- Masthan M., Arffy Technologies, Karnataka, India; Pazhanikumar K., Dept of Computer Science, S.T.Hindu College, Nagercoil, India; Chavan M., Department of Electronics& Communication, Bharati Vidyapeeth(Deemed to be University) College of Engineering, Pune, India; Mandala J., Department of Computer Science and Engineering, Christ (Deemed to be) University, Bangalore, India; Prasad Kumar S.N., Data Scientist, San Francisco, CA, United States
- Rights
- Restricted Access
- Relation
- ISSN: 0954898X; PubMed ID: 37807939
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Masthan M.; Pazhanikumar K.; Chavan M.; Mandala J.; Prasad Kumar S.N., “SCSLnO-SqueezeNet: Sine Cosine-Sea Lion Optimization enabled SqueezeNet for intrusion detection in IoT,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/14521.