Bot Management

A Seamless Synergy IntelliFend’s VisitorTag and AccuBot technologies form a comprehensive bot management system designed to detect, analyze, and manage human users, good bots, and bad bots with precision. VisitorTag’s Role in Human User Detection VisitorTag specializes in identifying genuine users through behavioral analysis. . It creates unique, persistent identifiers for each device by leveraging with hardware attributes, browser configurations, and session telemetry, it allows you to track specific devices and correlate their behavior with user-specific signals like user IDs, email addresses and ad campaign IDs. VisitorTag goes beyond traditional device fingerprinting. It combines session-based risk insights with anti-spoofing technology, ensuring precise tracking so you can distinguish between genuine users, bots, and bad actors. By leveraging both stateful and stateless methods to create unique identifiers, VisitorTag allows you to maintain accurate, persistent device tracking without the disruptions such as private browsing, cookie clearing, version updates or other changes often cause with competing solutions. AccuBot’s Role in Comprehensive Bot Management While VisitorTag focuses on human user detection, AccuBot broadens the scope by detecting all types of bot activity. Using a combination of server-side and client-side data, AccuBot assigns risk scores to every session, enabling real-time decisions to allow legitimate users, throttle suspicious traffic, or block malicious bots. How the Technologies Work Together Data Collection: VisitorTag gathers data from multiple layers, including network signals (IP addresses, HTTP headers), browser characteristics (operating systems, plugins), and interaction metrics (mouse movements, scrolling speeds). Real-Time Behavioral Insights: VisitorTag analyzes this data to identify anomalies indicative of bots. Integration with AccuBot: The behavioral insights from VisitorTag are fed into AccuBot’s detection engine, where they are combined with additional bot-related signals. This creates a robust, multi-faceted risk assessment for each session. Decision-Making: Based on the risk score, AccuBot determines the appropriate action—allowing, throttling, or blocking traffic in real time. What Distinguishes IntelliFend’s Solution IntelliFend’s VisitorTag and AccuBot technologies deliver: Accuracy: Combining behavioral insights and machine learning ensures minimal false positives and negatives. Real-Time Action: Edge capabilities process data closer to the user, ensuring low latency. Control: Businesses can customize traffic management policies, choosing which bots to allow and which to block. Real-World Impact In one deployment, IntelliFend used VisitorTag to detect fraudulent account creation attempts on an e-commerce platform. Behavioral anomalies, such as multiple accounts created from the same device,  flagged early, preventing financial losses and preserving the platform’s integrity. Meanwhile, AccuBot identified bot-driven traffic surges, ensuring uninterrupted access for genuine users. Contact Us for a Demo Ready to see how IntelliFend’s VisitorTag Tracking Technology and AccuBot Detection Engine can transform your security strategy? Contact us today to book a demo and experience the future of bot management. Check out our previous blog: Why Human User Detection is Key to Effective Bot Management

By Product Marketing As the cyberthreat landscape grows more complex and cybercriminals take advantage of emerging technologies and new vulnerabilities, enterprises must tread carefully as they confront the challenge of protecting their platforms from evolving bot threats while maintaining smooth access for legitimate users. Real customers drive real revenue These advanced bots can cleverly imitate human behavior to slip past conventional defenses, posing serious challenges for enterprises. They disrupt customer experiences, place a strain on resources, and ultimately have a detrimental effect on revenue. As such, enterprises must ensure that they have the ability to accurately distinguish legitimate users from bots. Whether it’s a customer signing up for a service, making a purchase, or engaging with a platform, ensuring these interactions are frictionless is key to maintaining trust and driving growth. On the other hand, bots generate fraudulent traffic that wastes bandwidth, drains server resources, and skews analytics, diverting attention and investment from real opportunities. Furthermore, bots often flood platforms with non-customer requests, overloading systems and driving up infrastructure costs. If left unchecked, this activity not only erodes performance but also inflates operational expenses. Accurate human user detection allows enterprises to allocate resources efficiently, ensuring they are focused on supporting real customers rather than processing illegitimate traffic. As cybercriminals continue to deploy more sophisticated bot strategies, enterprises need more than basic security measures like CAPTCHAs or IP blocking. What is needed is an intelligent, adaptive approach that adds an extra layer of defense to traditional defenses to reliably identify human users. The Challenge of Distinguishing Human Users Detecting human users is far from straightforward. While many organizations rely on tools such as Web Application Firewalls (WAFs) and DDoS protection systems, these technologies are not designed to differentiate humans from bots. WAFs: Primarily protect against OWASP Top 10 threats such as SQL injection and cross-site scripting. They focus on known attack vectors but lack the ability to analyze user behavior in real time. DDoS Protection Systems: Designed to identify and mitigate volumetric attacks. These systems may detect high traffic loads from malicious bots but are not equipped to assess the nuances of individual user interactions. These limitations leave organizations vulnerable to advanced bots that can mimic human behavior, bypassing traditional defenses and creating challenges in safeguarding applications, APIs, and resources. Common Bot Detection Techniques There are several mainstream techniques that are used to detect malicious bot actiivty. Human User Accuracy: How effectively the technique distinguishes between human users and bots. User Experience (UX): How seamless and frictionless the technique is for legitimate users. Below, we explore common detection techniques, their pros and cons, and compare them to Visitor Behavior Tracking—an advanced approach designed to adapt to modern bot challenges. Detection Solutions A variety of detection techniques are commonly used to identify human users, each with its strengths and limitations. Basic CAPTCHAs, for instance, require users to solve simple challenges, such as identifying images or entering text. While effective against basic bots, advanced bots equipped with machine learning or human solvers can easily bypass CAPTCHAs, making this method increasingly unreliable. Furthermore, CAPTCHAs often frustrate legitimate users, particularly those on mobile devices or with accessibility needs, leading to poor user experience. Static behavioral analysis is another method that relies on fixed patterns, such as mouse movement speed or click timing, to detect bots. Although minimally disruptive for users, this approach struggles against bots capable of mimicking human-like behaviors, leading to inaccuracies over time. Similarly, User-Agent analysis examines HTTP headers to identify bots, but this technique is easily spoofed by bots, rendering it insufficient as a standalone solution. Cookie-based tracking is slightly more robust, as it monitors user behavior across sessions. However, it is vulnerable to cookie deletion, private browsing, and manipulation by bots, limiting its reliability in modern environments. Reputation-based systems offer another approach, leveraging historical data to classify users or IP addresses as legitimate or malicious. While these systems can be effective for known threats, they often fall short when faced with new or unknown bot profiles. Machine learning is also frequently employed, using predictive algorithms to distinguish bots from humans. However, the accuracy of this method heavily depends on the quality of the training data. Poorly trained models can misclassify users, creating both false positives and false negatives. Blocking Solutions Blocking solutions are often used as a first line of defense against bots, but their simplicity can sometimes lead to unintended consequences. For instance, simple IP blocking aims to prevent access from known malicious IP addresses. However, this approach is easily circumvented by bots using rotating IPs or proxies, and it risks blocking legitimate users who share IP ranges with bad actors. As a result, its effectiveness is limited, especially against sophisticated bot networks. Geolocation checks are another blocking method, designed to restrict traffic from specific geographic regions. While potentially useful for targeting region-specific threats, this technique has significant drawbacks. Bots often use VPNs or proxies to mask their true location, rendering geolocation checks ineffective. Moreover, these checks can inadvertently exclude legitimate users from the targeted regions, leading to poor user experience and reduced accessibility. Rate Limiting Solutions Rate limiting is a commonly implemented solution to control traffic volumes by restricting the number of requests a single IP or session can make within a specific timeframe. While this technique can reduce the overall activity of bots, it often sacrifices user experience in the process. Legitimate users generating high levels of traffic—such as those navigating quickly through an application or conducting large transactions—may find themselves inadvertently blocked. This blunt approach lacks the nuance needed to address the sophisticated behavior of modern bots while ensuring seamless access for genuine users. Comparisons of Detection, Blocking, Behavioral and Rate Limiting Solutions and their Accuracy and UX in Bot Management Group Technique Human User Detection Accuracy User Experience Detection Solutions Basic CAPTCHA Low Poor Static Behavioral Analysis Low to Moderate Moderate User-Agent Analysis Low Excellent Cookie-Based Tracking Moderate Good Reputation-Based Systems Moderate Good Over-Reliance on Machine Learning Varies (Ben: can rate it as good?) Moderate

With the convenience of online ticketing comes a new set of challenges, particularly in the world of high-demand events. Ticket scalping has evolved, fueled by advanced bots that snapping up tickets the moment they go on sale, leaving genuine fans with few options and very often frustrated. Recent incidents, such as struggles for Coldplay tickets in Hong Kong and India, and Jay Chou concert tickets in Taiwan, as well as the 2023 Taylor Swift ticket fiasco in the U.S., reveal how bot-driven scalping is reshaping the market. As bots become more sophisticated, businesses must adopt advanced tools to ensure fair access. How Scalping Has Transformed Ticket scalping isn’t new, but with AI, bot has elevated it to unprecedented levels. Bots, or ticket scalpers, now use automated processes to claim hundreds or thousands of tickets within seconds, reselling them at inflated prices that often leave regular fans out in the cold. Scalpers use a variety of tactics to gain an advantage in ticket purchasing. By creating numerous profiles—sometimes with fake or stolen identities—they can bypass ticket limits intended to prevent bulk buying. Programmed to act the instant tickets go on sale, bots rapidly fill out forms and complete checkout faster than any human, securing tickets in seconds. In addition, scalpers exploit presale periods by purchasing memberships or credentials meant for loyal fans, allowing them early access to tickets before they’re available to the general public. Many scalpers also engage in speculative buying, acquiring tickets for events they expect to become highly popular and then reselling them at significant mark-ups when demand peaks. Ticket sales platforms can protect against bots that quickly grab tickets by employing bot management strategies like rate limiting, IP blocklisting and throttling, as a last resort, CAPTCHA challenges. However, these methods often compromise user experience as a trade-off. Why WAF Alone Falls Short in Identifying Legit Users in Ticketing While traditional web application security is vital for general website security, they lack the specialized capabilities needed to combat advanced bot-driven ticket scalping. Web Application Firewalls (WAFs), for example, are primarily designed to block common cyber threats such as SQL injection and cross-site scripting (XSS) attacks, but they fall short when faced with the complex, high-speed tactics that scalping bots employ. Today’s scalpers use sophisticated bots capable of mimicking human behavior to bypass WAF protections. These bots can create multiple fake profiles, rotate IP addresses and use advanced automation to fill out forms and complete purchases faster than any human. This level of complexity goes beyond what standard WAFs are designed to manage, allowing scalpers to secure tickets at high-demand events within seconds while genuine customers miss out. Besides, with many ticketing platforms hosting applications in cloud environments, the demand for scalability and quick adaptation is crucial. Basic cybersecurity solutions often struggle to scale efficiently in these environments, leaving ticketing platforms vulnerable during high-traffic events. This is where IntelliFend comes in. And Why IntelliFend Is Essential to Bridge the Gap? IntelliFend is a cost effective bot management solution designed to safeguard your websites, applications, and APIs from unwanted bot traffic. It bridges the security gaps left by WAFs, offering advanced protection against bot activities. Leveraging multi-layered AI and machine learning, IntelliFend accurately detects and classifies traffic, distinguishing between legitimate users, good bots, and scalpers. Whether you’re running a ticket sales platform or an online store, IntelliFend seamlessly integrates with your AWS infrastructure to provide robust security without compromising performance. Human Detection: Precision and Seamless Protection IntelliFend excels in human detection through its advanced AccuBot Detection Engine, which combines multi-layered analysis of client-side and server-side signals with AI and machine learning to accurately classify traffic as human, automated, or good bots. Enhanced by VisitorTag tracking technology, IntelliFend uses detailed data such as FingerprintID, cookies, and behavioral patterns to ensure precise identification. This sophisticated approach minimizes false positives, avoids disruptive CAPTCHAs, and delivers seamless protection, making it ideal for high-demand ticketing platforms. Get Started Today IntelliFend is designed to seamlessly complement any existing CDN or WAF, providing advanced bot protection that goes beyond the limitations of traditional cybersecurity tools. Whether you’re running an online store, managing a ticketing platform, or securing high-demand services, IntelliFend’s AI-driven solution ensures robust, scalable defense against unwanted bot activity—without compromising user experience. For AWS users, IntelliFend integrates effortlessly with your cloud environment, offering flexible deployment options to fit your infrastructure needs. Protect your platform while maintaining peak performance with IntelliFend’s seamless AWS integration. Ready to take the next step? Contact us at [email protected] to see how IntelliFend can secure your platform today.

A common misconception is that WAFs alone are sufficient to combat the diverse and sophisticated threats posed by bots. In this blog, we will explore why relying solely on WAFs is inadequate for managing bot attacks and how a dedicated bot management solution can provide the necessary insights and controls.