Artificial Intelligence Model for Cyber Security Threat Detection
Kantipudi Uma Maheswari Sri Lakshmi
, K Chinna Nagaraju , V Anil Santosh
Artificial intelligence model, cyber security, Threat protection
The difficulty of ensuring cyber-security is steadily growing as a result of the alarming development in computer connectivity and the sizeable number of applications associated to computers in recent years. The system also requires robust defines against the growing number of cyber threats. As a result, a possible role for cyber-security might be performed by developing Intrusion Detection Systems (IDS) to detect inconsistencies and threats in computer networks. An effective data-driven intrusion detection system has been created with the use of Artificial Intelligence, particularly Machine Learning techniques. This research proposes a novel Binary Grasshopper Optimized Twin Support Vector Machine (BGOTSVM) based security model which first considers the security features ranking according to their relevance before developing an IDS model based on the significant features that have been selected. By lowering the feature dimensions, this approach not only improves predictive performance for unidentified tests but also lowers the model's computational expense. Trials are conducted using four common ML techniques to compare the results to those of the current approaches (Decision Tree, Random Decision Forest, Random Tree, and Artificial Neural Network). The experimental findings of this study confirm that the suggested methods may be used as learning-based models for network intrusion detection and demonstrate that, when used in the real world.
"Artificial Intelligence Model for Cyber Security Threat Detection ", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 10, page no.b752-b762, October-2024, Available :https://ijnrd.org/papers/IJNRD2410181.pdf
Volume 9
Issue 10,
October-2024
Pages : b752-b762
Paper Reg. ID: IJNRD_301298
Published Paper Id: IJNRD2410181
Downloads: 00047
Research Area: Science and Technology
Country: Rajamundry , Andhra Pradesh , India
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJNRD (IJ Publication) Janvi Wave
