Artificial Intelligence-Based Cybersecurity Threat Detection Model

Registration ID: IJNRD_224962

Published ID: IJNRD2407230

DOI: Click Here to Get

Kakaraparthi Durga prasad , Dr. M Sumender Roy

Artificial Intelligence, Cybersecurity Threat Detection Model.

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.

Published Paper   E-Certificate

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

Volume 9 Issue 7, July-2024

Pages : c296-c308

Paper Reg. ID: IJNRD_224962

Published Paper Id: IJNRD2407230

Downloads: 000121977

Research Area: Computer Engineering 

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

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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