ShieldSquare is now Radware Bot Manager

ShieldSquare is now Radware Bot Manager

What is Bot Detection?

machine learnging bot detection tools

Researchers have estimated that roughly 26% of all internet traffic consists of bots, and that 79% of organizations cannot distinguish between good and bad bots. Good bots help carry out useful tasks, such as data collection and analysis, finding good deals on products and services, alerting when a website is down, helping find jobs, and in general automating tasks that are repetitive and involve parsing through many sources of data. On the other hand, bad bots are used by cybercriminals and other bad players to scrape pricing and other data, takeover user accounts and commit payment fraud, carry out denial of service and denial of inventory attacks, among other malicious activities. While older generations of bots are relatively simple to detect, newer generations are programmed to mimic the behavior of real users to evade basic security measures, making them very hard to detect and mitigate.

The constant evolution of bots and the increasing severity of the damage they cause to online businesses, organizations and consumers has made bot management one of the fastest-growing areas of internet security. The sophistication of fourth generation bots and the serious threats they pose have encouraged webmasters and security experts to adopt bot management solutions to protect their businesses.

How does a Bot Detection solution work?

An anti-bot solution should detect and manage every kind of bot ─ good as well as bad ─ based on organizational needs. It must work in real-time to identify bot activity and take measures against malicious bots, such as outright blocking, showing a CAPTCHA, feeding fake data, and so on. An anti-bot solution like Radware Bot Manager combines cutting edge technology with our researchers’ ingenuity to develop robust algorithms to detect, analyze and categorize bot patterns and signatures. We also leverage multiple methodologies including unique device fingerprinting, dynamic Turing tests, user behavior analysis, and JavaScript challenges. Our detection engine deploys various forms of machine learning (ML) to train algorithms based on known patterns and historical data in order to detect new bot patterns. Proprietary semi-supervised ML techniques help us detect the intent of every visitor to recognize malicious activities even without definitive bot signatures. A bot management solution should ideally produce a negligible number of false positives, so that humans are never mistaken for bots. On the other hand, false negatives (in which a bot is mistaken for a human) can lead to serious harm to websites, apps, and APIs. Bot managers should also be able to integrate with a wide variety of website infrastructure to suit the deployment needs of its users.

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