Paper Title
Machine Learning-Based Risk Assessment and Visualization for Cybersecurity Resilience in Organizations
Article Identifiers
Registration ID: IJNRD_226992
Published ID: IJNRD2408326
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Keywords
Phishing websites, Machine Learning, Content based features, URLs, Attacks
Abstract
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
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How To Cite (APA)
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
Issue
Volume 9 Issue 8, August-2024
Pages : d206-d211
Other Publication Details
Paper Reg. ID: IJNRD_226992
Published Paper Id: IJNRD2408326
Research Area: Engineering
Author Type: Indian Author
Country: Bengaluru, Karnataka, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2408326.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2408326
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