The most valuable leads often land on your website without ever filling out a form or contacting your sales team. These anonymous visitors represent one of the biggest challenges—and opportunities—for B2B organizations: they research solutions, compare vendors, and make purchasing decisions with no way to connect or engage them.

Uncovering the detect anonymous internet traffic  of these visitors is key to transforming passive interest into active sales opportunities. This is accomplished by using a combination of IP tracking, behavioral tracking, and data enrichment techniques to identify the companies behind the anonymous traffic. Using this information, marketers can then create personalized outreach strategies to turn these leads into qualified leads and sales opportunities.

Detecting Tor and other forms of anonymous Internet traffic requires intelligent tools that can adapt to rapidly evolving technology. Traditional web application firewalls (WAF) use a set of rigidly defined conditions, such as IP addresses associated with known VPN providers, browser versions, and geographic locations, to identify visitors. While these systems are effective at preventing malicious traffic, they can also miss legitimate visitors because they do not understand the complex patterns of the Tor protocol.

Using Device Fingerprinting to Block Bots and Multi-Account Abuse

Supervised learning models are a popular tool for analyzing anonymized traffic because they can leverage machine-learning algorithms to learn to recognize these patterns. However, the efficacy of these supervised models depends on the quality of the data used to train them and the features that they are trained on. In addition, most supervised models are designed for classification tasks, which makes them susceptible to performance degradation over time due to a lack of new training data and non-relevant features.