Transparent Peer Review By Scholar9
Advanced Vulnerability Risk Assessment & Management System
Abstract
The Advanced Vulnerability Risk Assessment & Management System is an innovative solution aimed at tackling the growing challenges of cybersecurity vulnerability management. With approximately 20 new cyber vulnerabilities emerging daily, information security managers must prioritize which vulnerabilities to patch. AVRAMS leverages machine learning to provide a comprehensive and dynamic approach to assessing and mitigating software vulnerabilities. By analyzing factors such as CVSS scores, vulnerability types, and historical exploitation data, the system predicts the likelihood of a vulnerability being exploited. This system employs web scraping techniques to extract comprehensive vulnerability data from CVE Details website, including CVE IDs, CVSS scores, vulnerability types, vendor, publication and update dates, applying machine learning techniques to develop and deploy this system for cybersecurity companies. The system enhances the accuracy of its predictions through feature engineering, focusing on the most relevant attributes, and continuously refining the model with new data. Regression analysis plays a crucial role in assessing and ranking vulnerabilities based on their predicted likelihood of exploitation. This process is vital for efficiently prioritizing security efforts, enabling organizations to focus on the most critical vulnerabilities first. The system further incorporates anomaly detection and patch availability monitoring to identify unusual patterns in vulnerability data and track vendors or versions with frequent updates, potentially signaling higher risks. These advanced features enable AVRAMS to generate actionable insights and recommend timely mitigation strategies, helping organizations reduce the risk of cyberattacks. Feature engineering and selection processes ensure the most relevant attributes are used to enhance prediction accuracy. The effectiveness of the prediction system is validated using metrics such as accuracy, precision, recall, and the F1 score. By accurately predicting which vulnerabilities are most likely to be exploited, AVRAMS empowers organizations to allocate resources more efficiently, reduce the window of opportunity for attackers, and significantly enhance their overall security posture. This proactive approach to vulnerability management is essential for maintaining robust cybersecurity defenses in today’s rapidly evolving threat landscape.
Chandrasekhara (Samba) Mokkapati Reviewer
13 Sep 2024 10:18 AM
Approved
Relevance and Originality:
The Advanced Vulnerability Risk Assessment & Management System (AVRAMS) is highly relevant given the increasing frequency of cyber vulnerabilities, with about 20 new ones emerging daily. Its use of machine learning to predict and prioritize vulnerabilities is both innovative and timely, offering a dynamic approach to a pressing cybersecurity challenge. The integration of web scraping for comprehensive data collection and advanced techniques such as feature engineering further emphasizes the system's originality and its contribution to improving cybersecurity practices.
Methodology:
The methodology of AVRAMS is well-structured and comprehensive, utilizing machine learning to analyze key factors like CVSS scores and historical data to predict vulnerability exploitation. The use of web scraping to gather detailed vulnerability data and feature engineering to refine predictions demonstrates a thorough approach. However, the paper could benefit from more detailed descriptions of the specific machine learning algorithms employed and their implementation, as well as a clearer explanation of how regression analysis is utilized to rank vulnerabilities.
Validity & Reliability:
The system’s validity is underscored by its application of real-world data and advanced machine learning techniques, with performance validated through metrics like accuracy, precision, recall, and F1 score. To strengthen reliability, additional information on cross-validation methods or comparisons with other vulnerability management solutions would be beneficial. This would provide a clearer picture of how AVRAMS performs in various scenarios and against other systems.
Clarity and Structure:
The presentation of AVRAMS is generally clear and organized, effectively outlining its features and capabilities. For enhanced clarity, incorporating visual aids such as diagrams or flowcharts to depict the system's architecture and processes would be useful. More detailed sectioning and summaries could also help in making complex information more accessible and easier to follow.
Result Analysis:
The result analysis effectively highlights AVRAMS’s predictive accuracy and its ability to help organizations prioritize vulnerabilities. To provide a deeper insight, the analysis could include a discussion on how different features and attributes impact prediction accuracy, as well as a comparison with existing methods. Addressing potential limitations of the system and suggesting areas for improvement would offer a more comprehensive evaluation of its effectiveness and practical applications.
4o mini
IJ Publication Publisher
Done Sir
Chandrasekhara (Samba) Mokkapati Reviewer