As addition to the above mentioned answers and comments.
If it's an abuse, how about an abuse-report to the ISP.
I'm not sure if the below document is what you are looking for. But I doubt someone can freely use such tools. I hope it helps.
A kind of Data mining could also be helpful, there are some documents online, perhaps a bit old.
In this chapter we have presented a comprehensive discussion the Web personalization
process viewed as an application of data mining which must therefore be supported
during the various phases of a typical data mining cycle. We have discussed a host of
activities and techniques used at different stages of this cycle, including the prepro-
cessing and integration of data from multiple sources, and pattern discovery techniques
that are applied to this data. We have also presented a number of specific recommen-
dation algorithms for combining the discovered knowledge with the current status of a
user’s activity in a Web site to provide personalized content to a user. The approaches
we have detailed show how pattern discovery techniques such as clustering, association
rule mining, and sequential pattern discovery, and probabilistic models performed on
Web usage collaborative data, can be leveraged effectively as an integrated part of a
Web personalization system.
While a research into personalization has led to a number of effective algorithms and
commercial success stories, a number of challenges and open questions still remain.
A key part of the personalization process is the generation of user models. The
most commonly used user models are still rather simplistic, representing the user as a
vector of ratings or using a set of keywords. Even where more multi-dimensional or
ontological information has been available, the data is generally mapped onto a single
user-item table which is more amenable for most data mining and machine learning
techniques. To provide the most useful and effective recommendations, personalization
systems need to incorporate more expressive models. Some of the discussion on the
integration of semantic knowledge and ontologies in the mining process suggests that
some strides have been made in this direction. However, most of this work has not,
as of yet, resulted in true and tested approaches that can become the basis of the next
generation personalization systems.
Another important and difficult of challenge is the modeling of user context. In par-
ticular profiles commonly used today lack in their ability to model user context and
dynamics. Users access different items for different reasons and under different con-
texts. The modeling of context and its use within recommendation generation needs to
be explored further. Also, user interests and needs change with time. Identifying these
changes and adapting to them is a key goal of personalization. However, very little re-
search effort has been expended the evolution of user patterns over time and their impact
on recommendations. This is in part due to the trade-offs between expressiveness of the
profiles and scalability with respect to the number of active users.
Solutions to these important challenges are likely to lead to the creation of the next-
generation of more effective and useful Web personalization and recommender systems
that can be deployed in increasingly more complex Web-based environments.
from here ore search google for "data mining for ip user profile" or perhaps "web usage mining".