Unique device fingerprinting for each device

Device Fingerprinting is the measurement of browser, software and connection attributes in order to generate a risk profile of a device, in real-time. ShieldSquare collects multiple diverse data inputs from where the page is accessed to compute a unique fingerprint for each device.

Using device fingerprinting, ShieldSquare engine can detect a bot operator’s device even when they change their identities.

unique device finger-print scanning

Bot Identity Dynamic Turning Tests

Dynamic Turing tests to uncover bot identity

When a website or mobile application receives a request to display a page, the embedded API and JS collect multiple parameters (like browser details) about the accessing entity.

Based on the received data, turing tests are constructed in real-time to evaluate the accessing entity’s capabilities and behavior, to uncover bot identity.

IP Tracking tests

ShieldSquare does Network forensics of the received request and identifies if the requests coming from Tor / Proxy IPs.

Multiple parameters are analysed including IP address, extracted IP geo location details, ISP information, IP owner, connection type, etc., and determines if the access is from genuine users or bots.

IP Tracking Test
User Behavior analyzes

User behavior analysis

The behavior of a user on a web page or mobile app will be significantly different from the behavior of an automated bot. Typical users of a website or mobile app have a behavioral characteristic in terms of number of pages visited per session, time spent on each page, frequency of repeat visits, and so on.

A user model is constructed based on historical data that can be checked for anomalies and deviation through which bot activity can be unearthed with accuracy.

Collective bot intelligence

Collective Bot intelligence gathered across sites will be utilized to identify bots, flag them and share the intelligence with other websites to ensure that bots and scrapers are identified and necessary actions taken against them.

Data from 3rd party fraud intelligence directories and services are also gathered to keep track of flagged IPs and devices to remediate the impact of malicious bot networks.

Collective Bot Intelligence Report

Machine Learning Bot Detection

Machine Learning for efficient bot detection

The shieldsquare platform delivers sophisticated machine learning algorithms using the vast history of the users, their behavior, and meta-data to accurately and proactively detect and prevent new generation of attacks by malicious bots.

ShieldSquare machine learning algorithms get smarter everyday by learning from new data and new clients, and identifies more bot patterns in real time.

Staying One Step ahead of the bots

Our technology is constantly empowered by the intelligence of our skillful data science team promptly satisfying all bot mitigation needs. Ensuring zero complacency towards bots impacting your business, our bot detection algorithm is tailored by our experts to suit your business and industry specific needs.

Bots are evolving to be more sophisticated and are developing evasion techniques to bypass inspection, even by advanced algorithms. Our data science team manually analyzes the data for anomalies to ensure new bots are caught with zero false positives.

One step-ahead of Bots
Powered by Think201