A Risk-Aware Automated Framework for Correlated Vulnerability Detection via Multi-Tool Security Analysis

A Risk-Aware Automated Framework for Correlated Vulnerability Detection via Multi-Tool Security Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Risk-Aware Automated Framework for Correlated Vulnerability Detection via Multi-Tool Security Analysis Y MANOHAR REDDY, Dr. Muniraju Naidu Vadlamudi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9398780/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Automated vulnerability scanning tools are popular for vulnerability assessment in web and networked applications. However, most of the existing tools are standalone and produce heterogeneous results with high redundancy and low prioritization, making the task of security analysts more complex. This paper proposes AVD-FW, an automated vulnerability detection and visualization framework that combines the results of various security scanning tools using a common vulnerability abstraction model. The proposed framework normalizes the results of the scanning tools, removes redundancy using correlation-based aggregation, and assigns confidence-based risk scores for effective prioritization. The proposed framework is evaluated using a controlled dataset of 100 authorized benchmark targets scanned from popular vulnerable platforms. Experimental results show that AVD-FW outperforms the existing standalone scanning tools like OWASP ZAP, Nuclei, and Nmap in terms of vulnerability detection coverage, removal of duplicate results, and confidence scores. Systems and Networking Vulnerability Assessment Security Automation Vulnerability Correlation Risk Scoring Cybersecurity Framework Figures Figure 1 Figure 2 Figure 3 Figure 4 I. Introduction The increasing complexity of modern software systems and the rapid uptake of cloud-native and web-based architectures have significantly increased the attack surface of business environments. Automated vulnerability assessment has therefore emerged as It is a crucial part of risk management for cybersecurity. Numerous well-known tools, like OWASP ZAP, Nuclei, and Nmap, are frequently used to find vulnerabilities at the network and application layer levels. Despite their great effectiveness, these technologies are frequently used independently and produce vulnerability scans in various formats, which leads to inconsistent severity, redundant results, and poor prioritization. According to recent studies, employing several scanners does not always improve security posture because serious vulnerabilities may be obscured by overlapping results and false positives. Furthermore, automated technologies that help with correlation, prioritization, and visualization of the results are required in large-scale networks in addition to scanning for vulnerabilities. To address these challenges, this paper proposes AVD- FW is an executable and automated framework for risk-aware visualization and linked vulnerability detection. The suggested methodology, in contrast to conventional methods, emphasizes improved prioritizing using confidence-aware risk scores, linked vulnerability identification across several instruments, and the unification of disparate scanner outputs. The framework is tested on a big dataset of approved benchmark goals and was created with ethical and repeatable experimentation in mind. The contributions of this paper are: 1. A unified vulnerability abstraction model for tool-independent representation. 2. A correlation-based aggregation algorithm to reduce duplication and improve confidence. 3. A risk-aware vulnerability visualization approach for effective prioritization. 4. A large-scale experimental evaluation on 100 benchmark targets II. Related Work Automated vulnerability assessment has been widely investigated in the literature. Various recent surveys have pointed out the limitations of traditional standalone vulnerability scanners in a large-scale setting and the need for automation and correlation. Cyber threat intelligence research has further emphasized the need for contextual analysis of security findings. Several studies [1] [2] [3] have analyzed the aggregation and correlation of vulnerability scan results. The existing solutions are mainly concentrated on aggregating scanner results but lack end-to-end systems with executable and reproducible evaluation. In addition, most of the existing solutions[16][17]1[20] only depend on CVSS scores for prioritization, which may not represent the actual risk. Recent studies [1] [4] [5] have analyzed confidence-aware and context-based vulnerability prioritization methods, showing better risk assessment performance than traditional severity-based methods. Vulnerable platforms such as [14][16]DVWA, OWASP Juice Shop, WebGoat, and Metasploitable are commonly recognized for ethical security testing and assessment. Different from the existing solutions, [3][6][the proposed AVD-FW framework combines multi-tool vulnerability scanning, correlation-based aggregation, confidence-aware risk scoring, and large-scale empirical assessment in a single executable system III. Proposed Framework and Methods 3.1 Framework Architecture The proposed AVD-FW framework architecture follows a modular and extensible architecture consisting of six core components: target management, tool integration, vulnerability normalization, correlation engine, risk analysis, and visualization. The framework orchestrates multiple vulnerability scanners through adapter modules and processes their outputs through a unified analysis pipeline. 3.2 Unified Vulnerability Abstraction Model To mitigate the heterogeneity in the output of scanners, the framework proposes a unified vulnerability abstraction model. Vulnerability attributes such as source identification, CVSS ranking, severity, affected endpoint, and vulnerability name are standardized by this approach. Vulnerability analysis and correlation between various scanning process are made easier by this level of abstraction. 3.3 Correlation-Based Aggregation The correlation engine is responsible for correlating and coordinating the vulnerabilities identified by multiple scanning process on the same destinations. The results from multiple scanning are combined into a single vulnerability, and the confidence levels are also updated. This reduces the redundancy of the results and makes them more reliable. 3.4 Risk Scoring and Visualization This methodology uses the combination of confidence levels and CVSS severity to calculate a final risk score. Analysts can give rank between vulnerabilities according to their effect and degree of confidence according to this risk-aware prioritizing technique. The framework offers a browser-based dashboard as well as structured console output. 3.5 Raw and Correlated Vulnerability Outputs: The raw vulnerability data prepared by individual scanners and the results generated upon correlation are clearly distinguished by this approach. Correlation findings are the final vulnerability results demonstrated for analysis, while its results are only for calculate the baseline. IV. Experimental Setup .1 Dataset Description A corpus of 100 authentic targets launched from weak benchmark platforms like DVWA, OWASP Juice Shop, WebGoat, Mutillidae, and Metasploitable was used to assess the framework. To test the framework on a big scale in an ethical way, several clones of each platform were launched on various ports and IP addresses. 4.2 Baseline Methods The proposed framework was compared with standalone vulnerability scanners such as OWASP ZAP, Nuclei, and Nmap. Each vulnerability scanner was run independently under the same conditions. 4.3 Automated Experimental Execution: To ensure consistency and reproducibility, the entire experimental process from vulnerability scanning to analysis is performed by a single automated test runner. This ensures that there is no human intervention in the process and that all results are obtained under the same conditions. V. Evaluation Metrics The following metrics were used to evaluate framework performance: Vulnerability Coverage (VC): Average number of vulnerabilities detected per target. Duplicate Reduction Rate (DRR): Percentage reduction in duplicate findings after correlation. Confidence Improvement (CI): Increase in average confidence score compared to baseline scanners. Severity Distribution Ratio (SDR): Proportion of High and Critical vulnerabilities. Baseline Vulnerability Definition: The baseline vulnerability count is the cumulative number of vulnerabilities reported independently by the integrated scanners prior to correlation. This is calculated automatically during framework execution. It is different from the hard-coded baseline in that it ensures that the duplicate reduction metrics are accurate representations of the scanner overlap and dataset size. VI. Results and Discussion Table 1 Baseline Comparison of Vulnerability Detection Tools Table 2 Duplicate Reduction Using Correlation Engine The results show that AVD-FW has a better coverage of vulnerabilities and that the number of duplicate findings has been significantly reduced. The correlation-based aggregation mechanism resulted in a reduction of about 32% in duplicate reports. Result Generation and Visualization: All tables and figures presented in this paper are generated automatically from consolidated vulnerability assessment results use the formulae Duplicate Reduction Rate (%) = (Before−After) ×100 Before . This approach ensures consistency between numerical metrics and visual representations, and prevents manual post-processing or subjective interpretation of results. VII. Limitations and Future Work The existing implementation is centered on vulnerability detection and aggregation and does not involve active exploitation and verification. Future work will include the use of machine learning to reduce false positives, model asset criticality dynamically, and integrate with DevSec Ops pipelines. VIII. Conclusion This developed framework's emphasis on automation, reproducibility, and assessment integrity clears that for suitable in large-scale vulnerability assessment studies in future. AVD-FW, an automated methodological approach for correlated vulnerability discovery and risk-aware visualization, was proposed in this research. Through the utilization of several scanners' strengths and correlation-based aggregation, the framework lowers redundancy, boosts confidence, and improves vulnerability ranking. Large-scale evaluation on approved benchmark goals demonstrates the scalability and effectiveness of the suggested system. The suggested architecture offers a useful and replicable starting point for further automated vulnerability intelligence research. References M. Alenezi, K. Almustafa, and M. Alshayeb,“Towards intelligent vulnerability management: A systematic literature review,”IEEE Access, vol. 8, pp. 185858–185879, 2020. R. Behl and S. Behl,“Automation in cybersecurity: A comprehensive review,” Journal of Information Security and Applications , vol. 55, 2020. Y. Li, J. Chen, and Z. Zhou,“Context-aware vulnerability risk assessment for enterprise systems,” Computers & Security , vol. 104, 2021. cd..cdA. Rahman, S. Verma, and P. Singh,“Visualization techniques for effective vulnerability assessment and risk analysis,” Future Internet , vol. 14, no. 9, 2022. A. Sharma and R. Kumar, “Automated vulnerability assessment techniques for modern web applications: A survey,” IEEE Access , vol. 8, pp. 219311–219328, 2020. S. Alqahtani, M. K. Khan, and A. Alzahrani, “A comprehensive analysis of web vulnerability scanners,” Journal of Information Security and Applications , vol. 58, 2021. M. Conti, T. Dargahi, and A. Dehghantanha, “Cyber threat intelligence: A systematic review,” IEEE Communications Surveys & Tutorials , vol. 23, no. 4, pp. 2398–2431, 2021. R. G. Salles, R. Sadre, and A. Pras, “A framework for correlating vulnerability assessment results,” Computers & Security , vol. 99, 2020. Y. Zhang, X. Chen, and H. Li, “Security vulnerability aggregation and correlation using multi-source scanning,” Future Generation Computer Systems , vol. 113, pp. 482–493, 2020. P. Kumar and M. Tripathi, “Correlation-based vulnerability management for large-scale networks,” IEEE Access , vol. 9, pp. 13421–13435, 2021. FIRST.Org, “Common Vulnerability Scoring System (CVSS) v3.1,” 2019. (still mandatory) J. Chen, Y. Wang, and L. Sun, “Risk-aware vulnerability prioritization using contextual and confidence metrics,” Journal of Network and Computer Applications , vol. 176, 2021. S. Patil and R. Kulkarni, “Context-based vulnerability risk scoring for enterprise systems,” Computers & Security , vol. 108, 2021. A. K. Dewald and B. Thompson, “Damn Vulnerable Web Application (DVWA): A benchmark platform,” OWASP Project, 2021. Rapid7, “Metasploitable: An intentionally vulnerable system for security testing,” 2022. H. Abie and I. Balasingham, “Risk-based adaptive security framework for cyber-physical systems,” IEEE Access , vol. 8, pp. 200210–200224, 2020. M. Husák et al., “Survey of attack projection, prediction, and forecasting in cyber security,” IEEE Communications Surveys & Tutorials , vol. 21, no. 1, pp. 640–660, 2020. K. Wang, S. Chen, and Q. Li, “Security data analytics for vulnerability management,” ACM Computing Surveys , vol. 54, no. 6, 2022. A. Alzahrani and M. Sheikh, “Scalable vulnerability assessment in cloud-based infrastructures,” IEEE Access , vol. 10, pp. 122311–122324, 2022. R. Kumar et al., “Automated security assessment frameworks for cloud-native applications,” Journal of Cloud Computing , vol. 12, no. 1, 2023. S. Verma and P. Singh, “Intelligent vulnerability correlation using hybrid analytical models,” Computers & Security , vol. 123, 2023. M. Alenezi et al., “Security assessment frameworks for modern DevSecOps pipelines,” IEEE Access , vol. 11, pp. 78412–78428, 2023. A. Rahman and K. Roy, “Automated vulnerability intelligence and visualization techniques,” Future Internet , vol. 15, no. 8, 2023. Y. Li and Z. Zhou, “Confidence-aware vulnerability assessment using multi-source security data,” Journal of Information Security and Applications , vol. 74, 2024. N. Ahmed et al., “Scalable and explainable vulnerability analytics for enterprise systems,” IEEE Access , vol. 12, 2025 (Early Access). Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9398780","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":621939341,"identity":"3dbe7808-9b88-4515-9454-f11c9e40423a","order_by":0,"name":"Y MANOHAR REDDY","email":"","orcid":"","institution":"Koneru Lakshmaiah Education Foundation, Bowrampet, Hyderabad, Telangana, India","correspondingAuthor":false,"prefix":"","firstName":"Y","middleName":"MANOHAR","lastName":"REDDY","suffix":""},{"id":621939437,"identity":"fe55b588-1487-4718-b64a-4f558b1d02eb","order_by":1,"name":"Dr. Muniraju Naidu Vadlamudi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIie3NPQuCQBjA8QvhWs5ahV6+QXAgVNKX8QhqMZcWB4kLwSmaG6TP4FS0KYItQqvQ5N7S0hg9RkiL1hh0f467Z7gfD0Ii0Y8WIIThqXEFWXqbwqg8z3ck0clXpEipuQUpr7eZhZFlN9i+Hi3P8nZCBkgKzwQNzTLST009TGLMDivmjOSdQTSOxyOClHk5MWjIMWZ+wNyWvLMIDUi/BYTxSnIHcsqAeDlp3j6TpQskzbdwI9+Cq0lyAbLGqp9mjubFE0IjrGoerSBHQ73yW9zxT9MsvdjjLj06MFiLUvIqfpul/KLV/yH74w+RSCT64x7oCFhle7vevwAAAABJRU5ErkJggg==","orcid":"","institution":"Koneru Lakshmaiah Education Foundation, Bowrampet, Hyderabad, Telangana, India","correspondingAuthor":true,"prefix":"Dr.","firstName":"Muniraju","middleName":"Naidu","lastName":"Vadlamudi","suffix":""}],"badges":[],"createdAt":"2026-04-13 04:56:34","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-9398780/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9398780/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106862079,"identity":"3f872277-1b45-4273-92c9-e8340364f8b5","added_by":"auto","created_at":"2026-04-14 08:27:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19900,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAVD-FW Architecture\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9398780/v1/5ad7807482c07ce89e75c84a.png"},{"id":106862083,"identity":"d4df93b5-4532-4551-a0ee-b984f1871dba","added_by":"auto","created_at":"2026-04-14 08:27:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33739,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 1 Severity Distribution of Detected Vulnerabilities\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9398780/v1/403385fbb84b6ce0242a05fc.png"},{"id":106862097,"identity":"32a34c0e-33e2-4e71-bb62-dfdc4fc459d8","added_by":"auto","created_at":"2026-04-14 08:27:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28007,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 2 Duplicate Reduction After Correlation\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9398780/v1/2d654ea523caae80b729f6d9.png"},{"id":106862110,"identity":"0836967c-9958-4816-9803-a6bf3545621c","added_by":"auto","created_at":"2026-04-14 08:27:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":24848,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 3 Vulnerabilities per target Average\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9398780/v1/83e91f15a0342edcc9de6cf4.png"},{"id":106862119,"identity":"8cd89a61-050f-418e-b7cf-3eaca4fbc504","added_by":"auto","created_at":"2026-04-14 08:28:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":771071,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9398780/v1/b4c6f806-d4a7-4481-9031-16fac68e317c.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eA Risk-Aware Automated Framework for Correlated Vulnerability Detection via Multi-Tool Security Analysis\u003c/p\u003e","fulltext":[{"header":"I. Introduction","content":"\u003cp\u003eThe increasing complexity of modern software systems and the rapid uptake of cloud-native and web-based architectures have significantly increased the attack surface of business environments. Automated vulnerability assessment has therefore emerged as\u003c/p\u003e\n\u003cp\u003eIt is a crucial part of risk management for cybersecurity. Numerous well-known tools, like OWASP ZAP, Nuclei, and Nmap, are frequently used to find vulnerabilities at the network and application layer levels. Despite their great effectiveness, these technologies are frequently used independently and produce vulnerability scans in various formats, which leads to inconsistent severity, redundant results, and poor prioritization.\u003c/p\u003e\n\u003cp\u003eAccording to recent studies, employing several scanners does not always improve security posture because serious vulnerabilities may be obscured by overlapping results and false positives. Furthermore, automated technologies that help with correlation, prioritization, and visualization of the results are required in large-scale networks in addition to scanning for vulnerabilities.\u003c/p\u003e\n\u003cp\u003eTo address these challenges, this paper proposes \u003cstrong\u003eAVD-\u003c/strong\u003e\u003cstrong\u003eFW\u003c/strong\u003e is an executable and automated framework for risk-aware visualization and linked vulnerability detection. The suggested methodology, in contrast to conventional methods, emphasizes improved prioritizing using confidence-aware risk scores, linked vulnerability identification across several instruments, and the unification of disparate scanner outputs. The framework is tested on a big dataset of approved benchmark goals and was created with ethical and repeatable experimentation in mind.\u003c/p\u003e\n\u003cp\u003eThe contributions of this paper are:\u003c/p\u003e\n\u003cp\u003e1. A unified vulnerability abstraction model for tool-independent representation.\u003c/p\u003e\n\u003cp\u003e2. A correlation-based aggregation algorithm to reduce duplication and improve confidence.\u003c/p\u003e\n\u003cp\u003e3. A risk-aware vulnerability visualization approach for effective prioritization.\u003c/p\u003e\n\u003cp\u003e4. A large-scale experimental evaluation on 100 benchmark targets\u003c/p\u003e"},{"header":"II. Related Work","content":"\u003cp\u003eAutomated vulnerability assessment has been widely investigated in the literature. Various recent surveys have pointed out the limitations of traditional standalone vulnerability scanners in a large-scale setting and the need for automation and correlation. Cyber threat intelligence research has further emphasized the need for contextual analysis of security findings.\u003c/p\u003e\n\u003cp\u003eSeveral studies [1] [2] [3] have analyzed the aggregation and correlation of vulnerability scan results. The existing solutions are mainly concentrated on aggregating scanner results but lack end-to-end systems with executable and reproducible evaluation. In addition, most of the existing solutions[16][17]1[20] only depend on CVSS scores for prioritization, which may not represent the actual risk.\u003c/p\u003e\n\u003cp\u003eRecent studies [1] [4] [5] have analyzed confidence-aware and context-based vulnerability prioritization methods, showing better risk assessment performance than traditional severity-based methods. Vulnerable platforms such as [14][16]DVWA, OWASP Juice Shop, WebGoat, and Metasploitable are commonly recognized for ethical security testing and assessment.\u003c/p\u003e\n\u003cp\u003eDifferent from the existing solutions, [3][6][the proposed AVD-FW framework combines multi-tool vulnerability scanning, correlation-based aggregation, confidence-aware risk scoring, and large-scale empirical assessment in a single executable system\u003c/p\u003e"},{"header":"III. Proposed Framework and Methods","content":"\u003cp\u003e\u003cstrong\u003e3.1 Framework Architecture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe proposed AVD-FW framework architecture follows a modular and extensible architecture consisting of six core components: target management, tool integration, vulnerability normalization, correlation engine, risk analysis, and visualization. The framework orchestrates multiple vulnerability scanners through adapter modules and processes their outputs through a unified analysis pipeline.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Unified Vulnerability Abstraction Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo mitigate the heterogeneity in the output of scanners, the framework proposes a unified vulnerability abstraction model. Vulnerability attributes such as source identification, CVSS ranking, severity, affected endpoint, and vulnerability name are standardized by this approach. Vulnerability analysis and correlation between various scanning process are made easier by this level of abstraction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Correlation-Based Aggregation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe correlation engine is responsible for correlating and coordinating the vulnerabilities identified by multiple scanning process on the same destinations. The results from multiple scanning are combined into a single vulnerability, and the confidence levels are also updated. This reduces the redundancy of the results and makes them more reliable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Risk Scoring and Visualization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis methodology uses the combination of confidence levels and CVSS severity to calculate a final risk score. Analysts can give rank between vulnerabilities according to their effect and degree of confidence according to this risk-aware prioritizing technique. The framework offers a browser-based dashboard as well as structured console output.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Raw and Correlated Vulnerability Outputs:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The raw vulnerability data prepared by individual scanners and the results generated upon correlation are clearly distinguished by this approach. Correlation \u0026nbsp;findings are the final vulnerability results demonstrated for analysis, while its results are only for calculate the baseline.\u003c/p\u003e"},{"header":"IV. Experimental Setup","content":"\u003cp\u003e\u003cstrong\u003e.1 Dataset Description\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA corpus of 100 authentic targets launched from weak benchmark platforms like DVWA, OWASP Juice Shop, WebGoat, Mutillidae, and Metasploitable was used to assess the framework. To test the framework on a big scale in an ethical way, several clones of each platform were launched on various ports and IP addresses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Baseline Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe proposed framework was compared with standalone vulnerability scanners such as OWASP ZAP, Nuclei, and Nmap. Each vulnerability scanner was run independently under the same conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Automated Experimental Execution:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;To ensure consistency and reproducibility, the entire experimental process from vulnerability scanning to analysis is performed by a single automated test runner. This ensures that there is no human intervention in the process and that all results are obtained under the same conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eV. Evaluation Metrics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following metrics were used to evaluate framework performance:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eVulnerability Coverage (VC):\u003c/strong\u003e Average number of vulnerabilities detected per target.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDuplicate Reduction Rate (DRR):\u003c/strong\u003e Percentage reduction in duplicate findings after correlation.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eConfidence Improvement (CI):\u003c/strong\u003e Increase in average confidence score compared to baseline scanners.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSeverity Distribution Ratio (SDR):\u003c/strong\u003e Proportion of High and Critical vulnerabilities.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eBaseline Vulnerability Definition:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The baseline vulnerability count is the cumulative number of vulnerabilities reported independently by the integrated scanners prior to correlation. This is calculated automatically during framework execution. It is different from the hard-coded baseline in that it ensures that the duplicate reduction metrics are accurate representations of the scanner overlap and dataset size.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"VI. Results and Discussion","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBaseline Comparison of Vulnerability Detection Tools\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cimg width=\"327\" height=\"177\" src=\"data:image/png;base64,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\" v:shapes=\"_x0000_i1030\" alt=\"image\"\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDuplicate Reduction Using Correlation Engine\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\" width=\"326\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cimg width=\"288\" height=\"84\" src=\"data:image/png;base64,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\" v:shapes=\"_x0000_i1029\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe results show that AVD-FW has a better coverage of vulnerabilities and that the number of duplicate findings has been significantly reduced. The correlation-based aggregation mechanism resulted in a reduction of about 32% in duplicate reports.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult Generation and Visualization:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll tables and figures presented in this paper are generated automatically from consolidated vulnerability assessment results use the formulae\u003c/p\u003e\n\u003cp\u003eDuplicate Reduction Rate (%) = \u003cu\u003e(Before\u0026minus;After)\u003c/u\u003e\u0026times;100\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Before\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"147\" height=\"111\" src=\"data:image/png;base64,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\" v:shapes=\"Picture_x0020_1\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003e. This approach ensures consistency between numerical metrics and visual representations, and prevents manual post-processing or subjective interpretation of results.\u003c/p\u003e"},{"header":"VII. Limitations and Future Work","content":"\u003cp\u003eThe existing implementation is centered on vulnerability detection and aggregation and does not involve active exploitation and verification. Future work will include the use of machine learning to reduce false positives, model asset criticality dynamically, and integrate with DevSec Ops pipelines.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"VIII. Conclusion","content":"\u003cp\u003eThis developed framework's emphasis on automation, reproducibility, and assessment integrity clears that for suitable in large-scale vulnerability assessment studies in future.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u0026nbsp;AVD-FW, an automated methodological approach for correlated vulnerability discovery and risk-aware visualization, was proposed in this research. Through the utilization of several scanners' strengths and correlation-based aggregation, the framework lowers redundancy, boosts confidence, and improves vulnerability ranking. Large-scale evaluation on approved benchmark goals demonstrates the scalability and effectiveness of the suggested system. The suggested architecture offers a useful and replicable starting point for further automated vulnerability intelligence research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eM. Alenezi, K. Almustafa, and M. Alshayeb,\u0026ldquo;Towards intelligent vulnerability management: A systematic literature review,\u0026rdquo;IEEE Access, vol. 8, pp. 185858\u0026ndash;185879, 2020.\u003c/li\u003e\n \u003cli\u003eR. Behl and S. Behl,\u0026ldquo;Automation in cybersecurity: A comprehensive review,\u0026rdquo;\u003cbr\u003e\u003cem\u003eJournal of Information Security and Applications\u003c/em\u003e, vol. 55, 2020.\u003c/li\u003e\n \u003cli\u003eY. Li, J. Chen, and Z. Zhou,\u0026ldquo;Context-aware vulnerability risk assessment for enterprise systems,\u0026rdquo;\u003cem\u003eComputers \u0026amp; Security\u003c/em\u003e, vol. 104, 2021.\u003c/li\u003e\n \u003cli\u003ecd..cdA. Rahman, S. Verma, and P. Singh,\u0026ldquo;Visualization techniques for effective vulnerability assessment and risk analysis,\u0026rdquo;\u003cem\u003eFuture Internet\u003c/em\u003e, vol. 14, no. 9, 2022.\u003c/li\u003e\n \u003cli\u003eA. Sharma and R. Kumar, \u0026ldquo;Automated vulnerability assessment techniques for modern web applications: A survey,\u0026rdquo; \u003cem\u003eIEEE Access\u003c/em\u003e, vol. 8, pp. 219311\u0026ndash;219328, 2020.\u003c/li\u003e\n \u003cli\u003eS. Alqahtani, M. K. Khan, and A. Alzahrani, \u0026ldquo;A comprehensive analysis of web vulnerability scanners,\u0026rdquo; \u003cem\u003eJournal of Information Security and Applications\u003c/em\u003e, vol. 58, 2021.\u003c/li\u003e\n \u003cli\u003eM. Conti, T. Dargahi, and A. Dehghantanha, \u0026ldquo;Cyber threat intelligence: A systematic review,\u0026rdquo; \u003cem\u003eIEEE Communications Surveys \u0026amp; Tutorials\u003c/em\u003e, vol. 23, no. 4, pp. 2398\u0026ndash;2431, 2021.\u003c/li\u003e\n \u003cli\u003eR. G. Salles, R. Sadre, and A. Pras, \u0026ldquo;A framework for correlating vulnerability assessment results,\u0026rdquo; \u003cem\u003eComputers \u0026amp; Security\u003c/em\u003e, vol. 99, 2020.\u003c/li\u003e\n \u003cli\u003eY. Zhang, X. Chen, and H. Li, \u0026ldquo;Security vulnerability aggregation and correlation using multi-source scanning,\u0026rdquo; \u003cem\u003eFuture Generation Computer Systems\u003c/em\u003e, vol. 113, pp. 482\u0026ndash;493, 2020.\u003c/li\u003e\n \u003cli\u003e P. Kumar and M. Tripathi, \u0026ldquo;Correlation-based vulnerability management for large-scale networks,\u0026rdquo; \u003cem\u003eIEEE Access\u003c/em\u003e, vol. 9, pp. 13421\u0026ndash;13435, 2021.\u003c/li\u003e\n \u003cli\u003e FIRST.Org, \u0026ldquo;Common Vulnerability Scoring System (CVSS) v3.1,\u0026rdquo; 2019.\u003cbr\u003e\u003cem\u003e(still mandatory)\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e J. Chen, Y. Wang, and L. Sun, \u0026ldquo;Risk-aware vulnerability prioritization using contextual and confidence metrics,\u0026rdquo; \u003cem\u003eJournal of Network and Computer Applications\u003c/em\u003e, vol. 176, 2021.\u003c/li\u003e\n \u003cli\u003e S. Patil and R. Kulkarni, \u0026ldquo;Context-based vulnerability risk scoring for enterprise systems,\u0026rdquo; \u003cem\u003eComputers \u0026amp; Security\u003c/em\u003e, vol. 108, 2021.\u003c/li\u003e\n \u003cli\u003e A. K. Dewald and B. Thompson, \u0026ldquo;Damn Vulnerable Web Application (DVWA): A benchmark platform,\u0026rdquo; OWASP Project, 2021.\u003c/li\u003e\n \u003cli\u003e Rapid7, \u0026ldquo;Metasploitable: An intentionally vulnerable system for security testing,\u0026rdquo; 2022.\u003c/li\u003e\n \u003cli\u003e H. Abie and I. Balasingham, \u0026ldquo;Risk-based adaptive security framework for cyber-physical systems,\u0026rdquo; \u003cem\u003eIEEE Access\u003c/em\u003e, vol. 8, pp. 200210\u0026ndash;200224, 2020.\u003c/li\u003e\n \u003cli\u003e M. Hus\u0026aacute;k et al., \u0026ldquo;Survey of attack projection, prediction, and forecasting in cyber security,\u0026rdquo; \u003cem\u003eIEEE Communications Surveys \u0026amp; Tutorials\u003c/em\u003e, vol. 21, no. 1, pp. 640\u0026ndash;660, 2020.\u003c/li\u003e\n \u003cli\u003e K. Wang, S. Chen, and Q. Li, \u0026ldquo;Security data analytics for vulnerability management,\u0026rdquo; \u003cem\u003eACM Computing Surveys\u003c/em\u003e, vol. 54, no. 6, 2022.\u003c/li\u003e\n \u003cli\u003e A. Alzahrani and M. Sheikh, \u0026ldquo;Scalable vulnerability assessment in cloud-based infrastructures,\u0026rdquo; \u003cem\u003eIEEE Access\u003c/em\u003e, vol. 10, pp. 122311\u0026ndash;122324, 2022.\u003c/li\u003e\n \u003cli\u003e R. Kumar et al., \u0026ldquo;Automated security assessment frameworks for cloud-native applications,\u0026rdquo; \u003cem\u003eJournal of Cloud Computing\u003c/em\u003e, vol. 12, no. 1, 2023.\u003c/li\u003e\n \u003cli\u003e S. Verma and P. Singh, \u0026ldquo;Intelligent vulnerability correlation using hybrid analytical models,\u0026rdquo; \u003cem\u003eComputers \u0026amp; Security\u003c/em\u003e, vol. 123, 2023.\u003c/li\u003e\n \u003cli\u003e M. Alenezi et al., \u0026ldquo;Security assessment frameworks for modern DevSecOps pipelines,\u0026rdquo; \u003cem\u003eIEEE Access\u003c/em\u003e, vol. 11, pp. 78412\u0026ndash;78428, 2023.\u003c/li\u003e\n \u003cli\u003e A. Rahman and K. Roy, \u0026ldquo;Automated vulnerability intelligence and visualization techniques,\u0026rdquo; \u003cem\u003eFuture Internet\u003c/em\u003e, vol. 15, no. 8, 2023.\u003c/li\u003e\n \u003cli\u003e Y. Li and Z. Zhou, \u0026ldquo;Confidence-aware vulnerability assessment using multi-source security data,\u0026rdquo; \u003cem\u003eJournal of Information Security and Applications\u003c/em\u003e, vol. 74, 2024.\u003c/li\u003e\n \u003cli\u003e N. Ahmed et al., \u0026ldquo;Scalable and explainable vulnerability analytics for enterprise systems,\u0026rdquo; \u003cem\u003eIEEE Access\u003c/em\u003e, vol. 12, 2025 (Early Access).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Koneru Lakshmaiah Education Foundation","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Vulnerability Assessment, Security Automation, Vulnerability Correlation, Risk Scoring, Cybersecurity Framework","lastPublishedDoi":"10.21203/rs.3.rs-9398780/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9398780/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAutomated vulnerability scanning tools are popular for vulnerability assessment in web and networked applications. However, most of the existing tools are standalone and produce heterogeneous results with high redundancy and low prioritization, making the task of security analysts more complex. This paper proposes AVD-FW, an automated vulnerability detection and visualization framework that combines the results of various security scanning tools using a common vulnerability abstraction model. The proposed framework normalizes the results of the scanning tools, removes redundancy using correlation-based aggregation, and assigns confidence-based risk scores for effective prioritization. The proposed framework is evaluated using a controlled dataset of 100 authorized benchmark targets scanned from popular vulnerable platforms. Experimental results show that AVD-FW outperforms the existing standalone scanning tools like OWASP ZAP, Nuclei, and Nmap in terms of vulnerability detection coverage, removal of duplicate results, and confidence scores.\u003c/p\u003e","manuscriptTitle":"A Risk-Aware Automated Framework for Correlated Vulnerability Detection via Multi-Tool Security Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 08:26:47","doi":"10.21203/rs.3.rs-9398780/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"881ff35d-037d-4e6f-b14b-ff78bc5149fa","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66179296,"name":"Systems and Networking"}],"tags":[],"updatedAt":"2026-04-14T08:26:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 08:26:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9398780","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9398780","identity":"rs-9398780","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}