Machine Learning-Based Risk Assessment and Visualization for Cybersecurity Resilience in Organizations

Registration ID: IJNRD_226992

Published ID: IJNRD2408326

DOI: Click Here to Get

S.Suriya , Dr. Jayanthi M G

Phishing websites, Machine Learning, Content based features, URLs, Attacks

Organizations are more vulnerable to cyber-attacks, especially malware, due to the growing reliance on digital networks for the transmission of critical information. Computers, smartphones, tablets, and servers used by organizations are the target of this research, which seeks to determine how susceptible they are to cyberattacks. Organizations may take proactive steps to improve their cybersecurity resilience if they are able to estimate the possibility of malware and similar threats compromising these endpoints. The research does this by analyzing endpoint attributes and predicting their attack vulnerability using sophisticated machine learning methods. These approaches include multiple imputation for addressing missing data and ensemble learning algorithms like boosting and bagging. To guarantee a thorough assessment of endpoint vulnerabilities, the main approaches center on processing and modeling data taken from a publicly available cybersecurity dataset. The results show that certain endpoint characteristics are strongly associated with increased cyberattack risks, which helps to identify which devices in a company are most at danger. Organizations may successfully minimize possible risks by implementing customized cybersecurity strategies based on these findings. This research provides a data-driven strategy for improving corporate cybersecurity via the identification of high-risk endpoints. This will help avert significant financial and reputational losses that may be caused by cyber intrusions

Published Paper   E-Certificate

S.Suriya & Dr. Jayanthi M G (August-2024). Machine Learning-Based Risk Assessment and Visualization for Cybersecurity Resilience in Organizations. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(8), d206-d211. https://ijnrd.org/papers/IJNRD2408326.pdf

Volume 9 Issue 8, August-2024

Pages : d206-d211

Paper Reg. ID: IJNRD_226992

Published Paper Id: IJNRD2408326

Downloads: 000121978

Research Area: Engineering

Country: Bengaluru, Karnataka, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2408326.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2408326

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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