IOT-BASED cyber security identification model through machine learning technique
- Title
- IOT-BASED cyber security identification model through machine learning technique
- Creator
- Lal B.; Ravichandran S.; Kavin R.; Anil Kumar N.; Bordoloi D.; Ganesh Kumar R.
- Description
- Manual vulnerability evaluation tools produce erroneous data and lead to difficult analytical thinking. Such security concerns are exacerbated by the variety, imperfection, and redundancies of modern security repositories. These problems were common traits of producers and public vulnerability disclosures, which make it more difficult to identify security flaws through direct analysis through the Internet of Things (IoT). Recent breakthroughs in Machine Learning (ML) methods promise new solutions to each of these infamous diversification and asymmetric information problems throughout the constantly increasing vulnerability reporting databases. Due to their varied methodologies, those procedures themselves display varying levels of performance. The authors provide a method for cognitive cybersecurity that enhances human cognitive capacity in two ways. To create trustworthy data sets, initially reconcile competing vulnerability reports and then pre-process advanced embedded indicators. This proposed methodology's full potential has yet to be fulfilled, both in terms of its execution and its significance for security evaluation in application software. The study shows that the recommended mental security methodology works better when addressing the above inadequacies and the constraints of variation among cybersecurity alert mechanisms. Intriguing trade-offs are presented by the experimental analysis of our program, in particular the ensemble method that detects tendencies of computational security defects on data sources. 2023 The Authors
- Source
- Measurement: Sensors, Vol-27
- Date
- 2023-01-01
- Publisher
- Elsevier Ltd
- Subject
- Cyber security; Internet of things; Machine learning algorithms; Meta-classifier methods; Repositories; Security
- Coverage
- Lal B., Dept. of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur Dist, A.P, India; Ravichandran S., Department of Computer Science and Engineering, School of Technology, GITAM University, Rudraram, Telangana, Hyderabad, 502 329, India; Kavin R., Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Tamil Nadu, Coimbatore, 641008, India; Anil Kumar N., Department of Electronics & Communication Engineering, School of Engineering & Technology, Mohan Babu University (Erst while Sree Vidyanikethan Engineering College), Andhra Pradesh, Tirupati, India; Bordoloi D., Computer Science and Engineering, Graphic Era Hill University, Dehradun, India, R/S, CSE, Graphic Era Deemed To Be University, Uttarakhand, Dehradun, 248002, India; Ganesh Kumar R., Department of Computer Science and Engineering, CHRIST (Deemed to be University), School of Engineering and Technology, Kengeri Campus, Kumbalgodu, Bangalore, 560074, India
- Rights
- All Open Access; Gold Open Access
- Relation
- ISSN: 26659174
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Lal B.; Ravichandran S.; Kavin R.; Anil Kumar N.; Bordoloi D.; Ganesh Kumar R., “IOT-BASED cyber security identification model through machine learning technique,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 26, 2025, https://archives.christuniversity.in/items/show/14185.