Text classification NLP on Web Server logs - Automatic tools vs Manual requests

We need a NLP model to check if a user is performing http requests by using automated tools or just doing manual requests. The model should be able to compare any kind of date and if the interval between the two dates is less than 2/3 seconds, the model should label that token as AUTO, while if that interval is higher than 2/3 seconds the model should label that token as MANUAL. Moreover the model should also check for the client request User Agent and IP Address, if on two different requests the IP is the same but the user agent is different, probably the token should be considered as AUTO also if the dates interval is higher the 2/3 seconds. Also some user agents that contains the automated tool name, should increase the propability that a token will result in the AUTO label. The model should be able to classify dates, User Agent and IPs with both log fromats of Apache and Nginx

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Dataset:
I am open to using either a pre-existing dataset or a dataset that the freelancer can provide or collect.

Metrics:
The success of this project will be measured based on the classification accuracy.

Ideal Skills and Experience:
- Strong background in natural language processing (NLP) and text classification
- Experience in working with web server logs and HTTP requests
- Proficiency in using automatic tools and manual browser activities for text classification
- Familiarity with metrics such as precision, recall scores, and F1 score

If you have the required skills and experience, please submit your proposal.