Akamai Technologies, Inc.

Prajakta Bhurke

David Senecal

Tu Vuong

Sreenath Kurupati

Sridhar V Machiroutu

A server interacts with a bot detection service to provide bot detection as a requesting client interacts with the server. In an asynchronous mode, the server injects into a page a data collection script configured to record interactions at the requesting client, to collect sensor data about the interactions, and to send the collected sensor data to the server. After the client receives the page, the sensor data is collected and forwarded to the server through a series of posts. The server forwards the posts to the detection service. During this data collection, the server also may receive a request from the client for a protected endpoint. When this occurs, and in a synchronous mode, the server issues a query to the detection service to obtain a threat score based in part on the collected sensor data that has been received and forwarded by the server. Based on the threat score returned, the server then determines whether the request for the endpoint should be forwarded onward for handling.

Low touch integration of a bot detection service in association with a content delivery network

Content delivery network (CDN)-based bot detection service with stop and reset protocols

Content delivery network (CDN) edge server-based bot detection with session cookie support handling

This disclosure describes a technique to slow down or block creation of automated attack scripts by configuring a detector to discriminate whether particular attack-like activity is a true attack, or simply a hacker 'testing' his or her automated attack script, and then permitting any such test script to continue working (attacking) the site, albeit on a limited basis. In this manner, the hacker receives an indication that his or her automated attack script is already working. Thereafter, when the detector later detects a launch of an actual attack based on or otherwise associated with the automated attack script (previously under test), the attack fails either because the script was not a working script in the first instance, or because information learned about the script is used to adjust the site as necessary to then prepare adequately for a true attack.

Mitigating automated attacks in a computer network environment

This disclosure describes a bot detection system that distinguishes bot transactions from human transactions. The system utilizes an anomaly-based filter process to reduce the number of false positives as determined by the system. The filter process includes maintaining a database of anomaly patterns, wherein the patterns are encoded as anomaly pattern strings. As anomalies are detected, they are encoded in the anomaly pattern strings, and the database is updated by maintaining counts on the occurrences of the strings. When a particular pattern string as reflected in the database has a count that exceeds a threshold, the string is determined to be associated with a bot as opposed to a human user.

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Santa Barbara, CA, United States of America

Prajakta Bhurke