Ethical Hacking through Foot Printing: A Machine Learning Strategy

Due to the increased reliance on technology in modern times, the likelihood of cyberattacks has increased. With the advent of remote working and internet business, the threat landscape has grown, making it harder for enterprises to defend their data and systems. As a result, robust cybersecurity measures, such as footprinting techniques, are more important than ever before. Footprinting can assist businesses in recognizing possible vulnerabilities in their networks and systems, enabling them to take preventative steps to bolster their security posture. Also, it can assist companies in identifying possible dangers and preventing attacks before they materialize. Footprinting is a crucial component of the pre-attack stage of a cyber-attack, as it helps attackers discover the most vulnerable locations in a network or computer system. This work explores the artificial intelligence for Footprinting and evaluates two widely-used classifiers, Decision Tree and Naive bayes, employing precision and recall parameters. The DT classifier has a higher success rate in recognising certain sorts of attacks, whereas the Naive-Bayesian classifiers has a greater rate of accuracy in detecting a wide variety of fraudulent activity. During the first stage of the research, both classifiers obtain outstanding recall and accuracy rates, with the DT classifiers achieving a recall and precision of 99% and the Naive-Bayesian method earning an average recall and precision of 98%. The results indicate that the efficacy of these classifier depends on the particular qualities of the data and the classification task's objectives. The study emphasises the footprinting potential of machine learning.