Bot attacks are turning from bad to worse. According to The State of Application Security report, 147% rise in bot attacks over the past year, with nine out of ten sites facing daily bot attacks.
These attacks can steal data, disrupt services, and even bring down entire platforms.
To address these risks, businesses should adopt effective techniques for bot protection.
In this article, we’ll explore the practical bot protection techniques that businesses can implement to defend against these malicious threats.
What is Bot Protection?
Bot protection refers to the measures and techniques designed to detect, mitigate, and prevent malicious activities carried out by bots on websites, applications, or other online platforms.
These bots can undertake diverse tasks, from legitimate functions such as search engine indexing to malicious activities like scraping content, initiating DDoS attacks, or perpetrating fraud.
Bots Are Becoming Complex Every Day
Bot developers continuously evolve their methods to bypass defense solutions. Bad bots often hide behind millions of IP addresses, quickly changing their IPs to avoid detection when one is blocked.
Attackers now use high-quality residential IPs, which have better reputations and are less likely to be flagged by traditional bot detection methods.
Sophisticated bots can forge their fingerprints to mimic human activity. They leverage automated frameworks and headless browsers to evade detection.
Fingerprints and behavioral signals are key factors in distinguishing bots from humans. However, bot developers increasingly use advanced methods and libraries to replicate human behavior, making detection even more challenging.
How Does Bot Protection Work?
Effective bot protection strategies employ a combination of detection, challenge, and response mechanisms to mitigate the risks posed by malicious bots.
Techniques for Bot Protection
IP Whitelisting/Blacklisting
Allows or denies access based on IP addresses. Whitelists permit traffic from trusted sources, while blacklists block traffic from known malicious IPs.
By using IP reputation databases, you can identify and block suspected bots based on their malicious activities.
Although effective for known threats, this method may not suffice against sophisticated bots that frequently change IP addresses to avoid detection.
Static Signature-Based Detection
This method identifies bad bots by examining header information and web requests for known signatures. By examining static attributes like user-agent strings and specific request patterns, security systems can recognize, and block known bot signatures.
While effective against recognized threats, this approach may struggle with new, evolving bots that don’t match existing signatures.
Behavioral-based Analysis
Real-time behavioral analysis establishes a baseline of normal user behavior and evaluates potential users against this baseline in real-time. Deviations are flagged, challenged, or blocked.
Bot Mitigation Solutions like AppTrana WAAP use extensive contextual data to analyze every user’s request, identify broader patterns, and generate behavioral signatures.
By examining numerous transactions, AppTrana WAAP can quickly detect and thwart bot attacks. Blocking actions are triggered only when behavior crosses a defined risk threshold, ensuring high accuracy and minimal false positives.
Challenge-Based Identification
This method differentiates human traffic from bots by testing the user’s ability to perform tasks such as running JavaScript, using cookies, and handling CAPTCHA elements. Bots often fail these tests, leading to their identification and blocking.
Challenge strategies aim to prevent bots from accessing protected resources while allowing legitimate users to proceed. This approach reduces the need for tuning to adjust for false positives or negatives.
Evaluating Bots with Bot Score
The bot score model is a multi-layered bot detection technique to identify sophisticated bots there by, providing an accurate assessment of each request without adding latency.
These scores usually range from 0 to 100. The score that determines what is a bot can be programmed. For example, a score of 60 or higher could suggest a 75% likelihood that the request is from a bot, while a score of 80 or higher could indicate a 99% probability that the request originates from a bot. These definitions typically change as per application behaviour.
In AppTrana WAAP’s user-defined bot policies, users can set specific Bot Score thresholds to tailor responses. For example, in the case of an e-commerce application, bot score could be liberal when you consider the number of urls visited by a single IP address, however, too many requests on a reset password page from a single IP within a short duration of time could mean that it could be an account takeover bot.
Such policies could be easily created on AppTrana WAAP using self-service rules. There is also the option of leveraging customer support to create these rules as AppTrana is a fully managed WAAP.
Advanced Bot Mitigation Actions
Boost your bot mitigation strategy with actions beyond dropping or blocking:
Feed Fake Data: Supply deceptive information to bots, diverting them from real data and potentially exposing their intentions.
Throttle: Reduce the response rate for potential bots, diminishing the effectiveness of bot attacks without affecting legitimate users.
JavaScript Challenges: Require JavaScript execution, a task many bots cannot perform, to verify the requester’s authenticity.
Custom Actions: Implement tailored measures to meet specific organizational needs, such as redirecting bots to honeypot pages or logging their activity for further analysis.
About AppTrana’s Bot Protection Capabilities
AI-powered bot management solutions like AppTrana WAAP integrate advanced techniques to offer comprehensive protection against bot attacks. These solutions begin by conducting behavioral analysis to detect anomalies in user behavior and distinguish between bots and humans. Once bots are detected, they are subject to automated mitigation measures such as rate-limiting, crypto challenges and CAPTCHA challenges.
To ensure accuracy, these security measures undergo thorough validation by AppTrana WAAP’s managed services team within the Bot Management Module, reducing the occurrence of false positives.
