Recon Automator: Enhancing Cybersecurity Reconnaissance with Automation

Abstract

Reconnaissance finds extensive meaning within penetration testing and vulnerability assessments when dealing with cybersecurity, as it is an important step in gathering data regarding the target infrastructure as well as identifying points of vulnerability that an adversary can use against them. Despite this, traditional reconnaissance processes tend to be manual and rely on disparate sources of information, resulting in inefficiencies and potential gaps. In order to overcome these challenges, we propose Recon Automator, a new tool that automates the reconnaissance process as well as makes it easier. Recon Automator combines various reconnaissance methods into one framework, using APIs and open-source tools to automate data collection and analysis with the help of custom modules. The tool saves time by eliminating the need for human involvement, thereby decreasing errors and refining asset and vulnerability discovery which increases general security personnel productivity. Here, we present the design and development of Recon Automator, compare its performance with conventional techniques and showcase a few real-world applications. The results show that Recon Automator is able to cut the time spent on reconnaissance while still achieving a high accuracy rate, making it a useful tool in any cybersecurity toolkit. Finally, we discuss the current limitations of Axon and future work needed to implement predictive asset categorization through machine learning at runtime as well as detection/mitigation of spear phishing attacks in real time.

Introduction

Reconnaissance remains a critical phase in cybersecurity for identifying weaknesses and understanding an organization’s security posture by collecting public information on target domains and infrastructure. Traditional reconnaissance methods are largely manual and fragmented, leading to inefficiencies and missed vulnerabilities, especially as modern IT environments grow more complex with cloud and IoT technologies.

To address these challenges, the paper introduces Recon Automator, a novel, modular, and scalable tool designed to automate and streamline the reconnaissance process. It integrates multiple tools (like Nmap, Amass, Sublist3r) and APIs into a single framework, reducing user intervention and improving data collection, analysis, and reporting. Recon Automator supports various reconnaissance tasks, including subdomain enumeration, port scanning, and vulnerability detection, while providing flexible input and output options.

The paper reviews existing reconnaissance methods and tools, highlighting their limitations such as lack of integration, scalability issues, and manual effort requirements. Recon Automator is tested using a comprehensive dataset combining real and synthetic data to evaluate accuracy, speed, and depth.

Built in Python, Recon Automator uses a command-line interface and supports parallel task execution for efficiency. It emphasizes ethical use by restricting unauthorized scans and protecting privacy. The tool aims to simplify cybersecurity workflows, reduce time-to-detection, and enhance the overall security assessment lifecycle by automating repetitive tasks with reliable and scalable automation.

Conclusion

The necessity for effective and automated reconnaissance instruments in cybersecurity is met with the creation of Recon Automator. As a command line interface (CLI) tool, Recon Automator has made encoding, subdomain enumeration, network port scanning and vulnerability mapping easier through integration of external powerful tools such as Nmap and Amass which facilitate the automation of complex repetitive operations. It can conduct subdomain enumeration operations with an accuracy of 95%, port scanning at 100% accuracy and shorten execution time by up to 40% as compared to traditional flows. Other features that the tool possesses such as the ease of performing large-scale scans while allowing for limited output define the importance of the tool to the cybersecurity sector. Nonetheless, the external utilities also come with the constraints such as nonavailability of AI assisted prioritizing of the vulnerabilities, dependencies on online CVE databases for realistic mappings. These limitations act as encouragement for improving the existing systems and arising opportunities such are reinforcing the program with ML technology, advance layering technologies, offline mapping of vulnerability, implementing graphical user interfaces as well as advanced features to target wider audience. To conclude, not only is Recon Automator an effective tool to use but it is also devoid of any weaknesses – its interface is user-friendly, it is accurate and efficient. With the help of automatic reconnaissance, security experts won’t be spending unnecessary time addressing issues instead concentrating on finding solutions to the existing problems. Once fully developed, the program is likely to become one of the leading programs which undertake ethical hacking therefore strengthening security.

References

[1] J. Scarfone, K. Ballintine, and M. Souppaya, Technical Guide to Information Security Testing and Assessment, National Institute of Standards and Technology (NIST), 2008. [2] OWASP Foundation, \"Amass,\" accessed November 2024, Available at:https://owasp.org/www-project-amass/. [3] Nmap Organization, \"Nmap,\" accessed November 2024, Available at: https://nmap.org/ . [4] CVE Program, \"Common Vulnerabilities and Exposures,\" accessed November 2024, https://cve.mitre.org/. [5] Tib3rius, “Auto Recon: Automated Network Recon Tool,” Auto Recon GitHub Repository, n.d., http://https://github.com/Tib3rius/AutoRecon, [6] K. Kaur and S. Sharma, M. Singh, \"A Study on Ethical Hacking Tools for Reconnaissance,\" International Journal of Advanced Research in Computer Science, vol. 9 no. 2, 2018, pp. 100-104.

Copyright

Copyright © 2025 Piyush Singh, Prem Prakash Agrawal, Srujan Dolai. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.