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