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Automated Detection of Security Misconfigurations Using Intelligent Tools
Subject area: Science,Engineering and Technology · Area of research: Cybersecurity and Security Analysis
Abstract
Background: Security misconfigurations in web applications, configuration files, and network services can lead to security risks and exposure of sensitive information. Manually identifying these issues can be time-consuming, especially when there are various types of inputs to check. This paper introduces a web-based system that helps in automating the detection of potential security misconfigurations by using intelligent tools. Users can upload files, enter web links, or provide network port information for analysis. The system processes the submitted input, scans for possible security issues, and categorizes the findings into Low, Medium, and High risk levels. It also offers explanations, recommendations, and corrective actions to help users understand and resolve the identified issues. The system is built using Python and Flask, along with tools and libraries such as Nmap, Requests, Pandas, Scikit-learn, and OpenCV. The main goal is to simplify security analysis and reduce the effort required for manual checks. Methods: The proposed system accepts configuration files, web URLs, and network port information for analysis. The submitted inputs are validated and processed using Python and Flask. Security scanning tools and predefined rules are used to identify potential misconfigurations. Detected issues are categorized into Low, Medium, and High risk levels, and recommendations along with possible corrective actions are provided to help users address them. Results: The system is designed to identify potential security misconfigurations in uploaded files, web URLs, and network port information. It aims to present the detected issues in a clear and organized manner by categorizing them into Low, Medium, and High risk levels. The system also provides explanations, security recommendations, and possible corrective actions to assist users in understanding the identified issues. By combining different security checks into a single web interface, the system aims to simplify the analysis process and reduce the effort needed for manual checking. Further testing is required to evaluate its detection accuracy and overall performance. Conclusion: The proposed system offers a common platform for analyzing potential security misconfigurations using intelligent tools. It integrates input validation, security scanning, risk classification, and recommendation generation into a single workflow. The system aims to help users identify possible security weaknesses and understand how to address them. Its web-based interface is designed to make security analysis more accessible and easier to manage. Future improvements could include adding more detection rules, supporting additional configuration types, and evaluating performance using appropriate test datasets.
References
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[2] Johnson A, Dempsey K, Ross R, Gupta S, Bailey D. Guide for Security-Focused Configuration Management of Information Systems. NIST Special Publication 800-128. Gaithersburg, MD: NIST. https://doi.org/10.6028/NIST.SP.800-128
[3] Lyon G. Nmap Network Scanning: The Official Nmap Project Guide to Network Discovery and Security Scanning. Sunnyvale, CA: Insecure.Com LLC; 2009. Available from: https://nmap.org/book/
[4] OWASP Foundation. Web Security Testing Guide [Internet]. Available from: https://owasp.org/www-project-web-security-testing-guide/
[5] OWASP Foundation. OWASP Top Ten Web Application Security Risks [Internet]. Available from: https://owasp.org/www-project-top-ten/
[6] Center for Internet Security. CIS Critical Security Controls v8.1 [Internet]. Available from: https://www.cisecurity.org/controls/cis-controls-list
[7] National Institute of Standards and Technology. Cybersecurity Framework (CSF) 2.0 [Internet]. Available from: https://www.nist.gov/cyberframework
[8] National Institute of Standards and Technology. Security and Privacy Controls for Information Systems and Organizations. NIST SP 800-53, Revision 5. Gaithersburg, MD: NIST; 2020. Available from: https://csrc.nist.gov/pubs/sp/800/53/r5/upd1/final
[9] National Institute of Standards and Technology. National Vulnerability Database [Internet]. Available from: https://nvd.nist.gov/
[10] OWASP Foundation. OWASP Application Security Verification Standard [Internet]. Available from: https://owasp.org/www-project-application-security-verification-standard/
[11] MITRE Corporation. Common Vulnerabilities and Exposures (CVE) [Internet]. Available from: https://www.cve.org/
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[13] Python Software Foundation. Python Documentation [Internet]. Available from: https://docs.python.org/3/
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How to cite this paper
@article{1724440,
author = {M. Akshata, Rabiya Samreen, Rachana P, Sayeema Iram, Reshma R},
title = {Automated Detection of Security Misconfigurations Using Intelligent Tools},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {4},
pages = {5096-5103},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724440.pdf},
abstract = {Background: Security misconfigurations in web applications, configuration files, and network services can lead to security risks and exposure of sensitive information. Manually identifying these issues can be time-consuming, especially when there are various types of inputs to check. This paper introduces a web-based system that helps in automating the detection of potential security misconfigurations by using intelligent tools. Users can upload files, enter web links, or provide network port information for analysis. The system processes the submitted input, scans for possible security issues, and categorizes the findings into Low, Medium, and High risk levels. It also offers explanations, recommendations, and corrective actions to help users understand and resolve the identified issues. The system is built using Python and Flask, along with tools and libraries such as Nmap, Requests, Pandas, Scikit-learn, and OpenCV. The main goal is to simplify security analysis and reduce the effort required for manual checks.
Methods: The proposed system accepts configuration files, web URLs, and network port information for analysis. The submitted inputs are validated and processed using Python and Flask. Security scanning tools and predefined rules are used to identify potential misconfigurations. Detected issues are categorized into Low, Medium, and High risk levels, and recommendations along with possible corrective actions are provided to help users address them.
Results: The system is designed to identify potential security misconfigurations in uploaded files, web URLs, and network port information. It aims to present the detected issues in a clear and organized manner by categorizing them into Low, Medium, and High risk levels. The system also provides explanations, security recommendations, and possible corrective actions to assist users in understanding the identified issues. By combining different security checks into a single web interface, the system aims to simplify the analysis process and reduce the effort needed for manual checking. Further testing is required to evaluate its detection accuracy and overall performance.
Conclusion: The proposed system offers a common platform for analyzing potential security misconfigurations using intelligent tools. It integrates input validation, security scanning, risk classification, and recommendation generation into a single workflow. The system aims to help users identify possible security weaknesses and understand how to address them. Its web-based interface is designed to make security analysis more accessible and easier to manage. Future improvements could include adding more detection rules, supporting additional configuration types, and evaluating performance using appropriate test datasets.},
month = {October},
}