Paper Title
Artificial Intelligence-Based Cybersecurity Threat Detection Model
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Registration ID: IJNRD_224962
Published ID: IJNRD2407230
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Keywords
Artificial Intelligence, Cybersecurity Threat Detection Model.
Abstract
The task of ensuring cybersecurity is becoming more difficult owing to the exponential increase in computer connections and the extensive range of applications interconnected with computers. With the expansion of networks, the number of possible entry points for cyber attacks also increases, highlighting the need for strong defenses. An effective approach is the creation of Intrusion Detection Systems (IDS) that can detect and pinpoint irregularities and potential dangers inside computer networks. Artificial Intelligence, namely Machine Learning, has greatly improved IDS capabilities in recent years. This study presents a new security model called Binary Grasshopper Optimized Twin Support Vector Machine (BGOTSVM). The model starts by prioritizing security elements based on their significance, guaranteeing that the most crucial traits are given priority in the development of the IDS model. By decreasing the number of feature dimensions, this method enhances the ability to forecast outcomes for unfamiliar tests and reduces the computing expense of the model. Efficiency is crucial for real-time applications where both speed and accuracy are of utmost importance. Experiments were carried out using four widely used machine learning approaches: Decision Tree, Random Decision Forest, Random Tree, and Artificial Neural Network to compare the outcomes with established methodologies. The experimental results suggest that the suggested BGOTSVM technique functions as a resilient learning-based model for network intrusion detection. When used in real-world situations, it outperforms typical machine learning algorithms, showcasing its promise as a robust tool in cybersecurity.
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How To Cite (APA)
Kakaraparthi Durga prasad & Dr. M Sumender Roy (July-2024). Artificial Intelligence-Based Cybersecurity Threat Detection Model. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(7), c296-c308. https://ijnrd.org/papers/IJNRD2407230.pdf
Issue
Volume 9 Issue 7, July-2024
Pages : c296-c308
Other Publication Details
Paper Reg. ID: IJNRD_224962
Published Paper Id: IJNRD2407230
Research Area: Computer EngineeringÂ
Author Type: Indian Author
Country: Alluri seetharamarju district, Andra Pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2407230.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2407230
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