With so many bots on applications for both legitimate and malicious purposes, the question arises of how to stop the bad bots without impacting the work of those that are legitimate. While AppSec teams can pay close attention to their logs to uncover traffic spikes that may likely be bots, uncovering whether the traffic is from legitimate or malicious bots can prove difficult without the proper tooling. Additionally, in-house approaches to bot management can leave security vulnerabilities because sophisticated bots are designed to appear just as human users. To escape these drawbacks, bot management software was created.
Bot Management is a type of layer-7 security software that organizations can implement to protect their applications from malicious bot traffic. The software offers detection, mitigation, and monitoring capabilities organizations use to protect their digital assets and maintain a secure online environment.
Bot detection is the process of identifying bots from human users on a website and differentiating between the legitimate and malicious bot activity detected. It is the most challenging function of any bot management software because sophisticated bots are capable of almost identically disguising themselves as human traffic. To detect bots, bot management software can use a number of detection methods outlined below.
Bot management software utilizes a variety of detection methods that may also differentiate between legitimate and malicious traffic. Detection methods are used in conjunction to avoid security gaps that may arise if used individually:
CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is a widely used bot detection method that presents challenges to users to prove they’re human. It typically requires users to complete tasks like recognizing distorted characters, solving puzzles, or selecting specific images. Simplistic bots struggle to pass these tests accurately, while humans can easily complete them.
This method analyzes user behavior patterns to distinguish between human users and bots. It looks for unusual or suspicious activities that bots tend to exhibit, such as rapid page requests, uniform browsing patterns, or unusual click patterns. Behavioral analysis can also involve factors like session duration, mouse movements, typing speed, navigation patterns, and more.
IP analysis involves tracking and analyzing the IP addresses associated with incoming requests. It helps identify suspicious IP addresses or ranges that are known for malicious activities or exhibit bot-like behavior. IP reputation databases or blacklists are often used to flag or block requests originating from suspicious IPs.
User-Agent analysis examines the user-agent string included in the HTTP request header to determine the client software or device used to access the website or application. Bot traffic may have unique or identifiable user-agent patterns, allowing detection systems to flag requests from known bot user-agents or identify abnormal or suspicious user-agents.
Machine learning and artificial intelligence (AI) techniques can be employed to train models that can recognize patterns and characteristics associated with bots. These models can learn from large volumes of data and can detect anomalies or bot-like behaviors from combinations of request headers, user interactions, mouse movements, or navigation patterns.
Device fingerprinting involves collecting and analyzing device-specific information, such as browser attributes, operating system details, screen resolution, installed plugins, or timezone settings. These device-specific attributes can help identify suspicious or unique device configurations associated with bots.
A sophisticated bot may be capable of passing one or a few of these detection methods, but by combining them as part of an organization’s overarching bot management strategy, organizations can detect the vast majority of malicious bot traffic on their applications.
Once a bot is detected, the focus shifts to mitigation. Mitigation is the process of filtering malicious bots from normal traffic to avoid their potential attacks. This can mean outright blocking the IP that the bots are coming from, or sending their traffic to another part of the application to avoid instances where a false positive prevents a sale or desired action. Most bot management products also allow for nuanced rulesets to be applied to bot traffic, including rate-limiting and combinations of rules being met before blocking. This allows organizations to increase decision confidence and ensure that legitimate traffic isn’t impacted by a blocking decision.
Monitoring refers to the bot management’s observability tools that give insights into bots on your application. It gives holistic views into bot traffic, trends, types and other details that AppSec can use to understand their traffic better. This is often where broad security strokes can be taken such as bot block/allow lists and bot policies too.
Bot Management protects applications from malicious bots while intelligently distinguishing and enabling legitimate ones. Through its detection, mitigation, and monitoring capabilities, organizations can maintain a secure environment and gain insights into their application’s traffic.
Fastly’s Next-Gen WAF offers built-in bot management capabilities to protect your applications from malicious bots while enabling legitimate ones. Prevent bad bots from performing malicious actions against your websites and APIs by identifying and mitigating them before they can negatively impact your bottom line or user experience. Learn more about the Next-Gen WAF and its bot management capabilities.