Evaluating solutions for bot management
Why are solutions for bot management so essential?
Bad bots currently represent one of the most serious threats to companies. The traffic of malicious bots can reduce websites’ performance, link online inventory, compromise personal data and increase turnover/loss of customer returns. By targeting websites, mobile applications and APIs, bots can cause several business problems, such as account acquisition, DDoS application, web scraping, spam creation, skewed analysis and advertising fraud.
On the other hand, good bots help the web to grow and develop. They track websites’ pages to establish SERP rankings and keep them updated in real time with data. They also help consumers to find the best price for a product or to discover stolen content. Nowadays, the ability to distinguish between these two types of bots is essential for the optimal functioning of a company. According to the “Radware’s Web Application Security Report” 79% of companies cannot distinguish between good or malicious bots.
The increasing intensity of global traffic of bots and the serious increase of bots’ overall impact have made solutions for bot management crucial for business continuity and success. This is particularly true if we consider that more sophisticated malicious bots can now simulate human behavior and easily mislead conventional cybersecurity solutions or the management systems used to detect them.
Building and maintaining bot mitigation solutions internally, require large resources and continuous adjustments. There are few resources to help companies to evaluate such solutions and there is even less consensus on which features and capabilities of security, specialists should look for during the evaluation process of a solution.
Some evaluation criteria
To deal with highly sophisticated and automated cyber threats, it is required an in-depth analysis of bots’ tactics and intentions. According to "The forrester New Wave: Gestión de bot, Q3 2018 report" detection and response to attacks and threat investigation are the main differentiators. Solutions for bot management must determine the intention of automated traffic in real time to distinguish between good and bad bots.
In the selection process of a bot mitigation solution, companies must follow the following criteria and evaluate the following requirement to determine which solution best suits their unique needs.
Basic functions of bot management.
Companies must evaluate the range of possible response actions such as blocking, limitation, ability to outwit competitors by serving false data and the ability to take personalized actions based on signatures and types of bots.
Ability to detect bots with human behavior distributed on a large scale.
Traditional defenses do not reach the detection characteristics required to counteract such attacks. Dynamic IP attacks make IP-based mitigation useless. A frequency limitation system without any behavioral learning will result in rejecting real customers when an attack occurs. The creation of bots with highly sophisticated human characteristics in recent years, requires more advanced detection and response techniques, for example, fingerprints in devices and browsers, analysis of intention and behaviors, collective intelligence of bots and threat investigation, as well as further fundamental techniques.
A bot detection tool that continuously adapts to beat the most cunning scammers and competitors.
Any bot management system should achieve all the above in addition to collecting hundreds of parameters from users’ browsers and devices to uniquely identify and analyze the behavior. The deceptive capabilities of sophisticated bots must also match.
Accuracy is critical in bots’ detection, any solution must distinguish between good and bad bots and furthermore improve the user experience.
Preserving false positives to a minimum level to ensure that user’s experience is not affected, is important as well. Real users should never have to resolve a CAPTCHA or prove that they are not a bot. An enterprise-level bot detection tool must have self-learning and deep optimization capabilities to constantly identify and block bots despite their evolution as they change their characteristics to avoid detection by basic security systems.
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