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.
Aravind Ayyagari Reviewer
13 Sep 2024 10:09 AM
Approved
Relevance and Originality:
The Advanced Vulnerability Risk Assessment & Management System (AVRAMS) is highly relevant to the current needs in cybersecurity. It addresses the challenge of managing the influx of daily cyber vulnerabilities by using machine learning to predict and prioritize threats. The system's innovative integration of web scraping, feature engineering, and anomaly detection for comprehensive vulnerability management showcases originality and offers a novel approach to enhancing cybersecurity defenses.
Methodology:
The methodology described is sophisticated, involving web scraping from the CVE Details website, and the application of machine learning techniques to predict vulnerability exploitation likelihood. The use of regression analysis and continuous model refinement through feature engineering indicates a robust approach. More details on the specific machine learning models used and their performance metrics would provide further insight into the methodology's effectiveness.
Validity & Reliability:
The validity of AVRAMS is supported by its use of established vulnerability metrics and advanced machine learning techniques. The system’s reliability is enhanced by its continuous refinement and the use of metrics like accuracy, precision, recall, and F1 score for validation. To strengthen reliability, including additional validation steps such as cross-validation or external benchmarking against industry standards would be beneficial.
Clarity and Structure:
The article is well-structured, with a clear explanation of AVRAMS’s features and capabilities. To improve clarity, the inclusion of visual aids such as flowcharts or diagrams illustrating the system’s workflow and processes would be helpful. Additionally, a more detailed breakdown of how each component contributes to the overall system could enhance the reader’s understanding.
Result Analysis:
The result analysis indicates that AVRAMS effectively predicts which vulnerabilities are most likely to be exploited, with strong performance metrics. For a more comprehensive analysis, including comparisons with other vulnerability management systems and a discussion on how different features impact the accuracy of predictions would provide deeper insights. Addressing any limitations or potential areas for improvement in the system would also be valuable.
IJ Publication Publisher
Done Sir
Aravind Ayyagari Reviewer