Radware, Ltd.

Kaalbi Technologies Private Limited

Rakesh Thatha

Neeraj Kumar Gupta

Harisankar Haridas

Sonal Lalchand Oswal

Mohit Rajput

Zaid Imam

Pavan Thatha

Jyoti Kakatkar

Aman Rawka

Sonal Laichand Oswal

The method and system for performing a completely automated public Turing test to tell computers and humans apart (CAPTCHA). The method comprises receiving by a server a request from a service server to initiate a CAPTCHA of a user node; generating an image of a path; generating at least one target to be placed upon the path; generating an object to be placed at a start point of the path; sending the generated path, the at least one generated target, and the object as a CAPTCHA challenge to the user node for display thereon; receiving, by the server, data respective of motion of the object with respect to the path and the at least one generated target; and issuing, based on analysis of the data received data, a pass indication upon determination that the user node is operative by a human.

System and method for classifying human and non-human traffic using behavior authenticity

A system and method for detecting and blocking bots are presented. The method includes receiving unlabeled data regarding a visitor of a web source, grouping the received unlabeled data with similar characteristics into a group of data, detecting, based on the group of data, at least one anomaly, and determining, based on the at least one detected anomaly, several visitors to be blacklisted.

System and method for detecting bots based on iterative clustering and feedback-driven adaptive learning techniques

A system of method of detecting bots are presented. The method includes receiving access patterns of a visitor accessing a protected web property, encoding each of the access patterns into a fixed length feature vector, determining an offline-trained model based on past data, generating an anomaly score based on the fixed length feature vector and an offline-trained model, and determining the visitor to be a bot, when the generated anomaly score associated with the visitor reaches a predetermined threshold.

System and method for detecting bots using semi-supervised deep learning techniques

A system and method for detecting bots. The method includes receiving a request to access a server, the request is being received from a client device, and responsive to the request, causing the client device to download a script code file to the client device. The script code file, when executed, collects a profile, and the profile includes a plurality of parameters. The method also includes receiving the created profile, generating a score based on the plurality of parameters to identify a bot, and initiating a mitigation action based on the identified bot.

System and method for detecting bots based on anomaly detection of JavaScript or mobile app profile information

A system and method for bot detection utilizing storage variables are presented. The storage variables generated is used to analyze user behavior and distinguish human traffic from bot traffic. The system for detecting bot traffic using storage variables includes a client application, a computer network, a bot detector, a bot computer, a storage variable generator, and a server. The client device enables a user to access information through the client application. The storage variable generator is configured to generates a plurality of storage variables including counter storage variable. The bot detector analyses the presence of bots in incoming traffic.

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Bengaluru, India

Rakesh Thatha