Bot Protection: Attack Examples & 8 Ways to Defend Your Network

• Types of Bots
• Common Bot Attack Techniques
• 10 Examples of Famous Bot Attacks

• Bot Protection and Mitigation Techniques and Strategies
• Bot Protection with Radware

Good Bots

Good bots perform useful and legitimate tasks that improve efficiency and user experience. These include:

• Search engine crawlers: Index web pages to improve search visibility.
• Monitoring bots: Track website uptime, security, and performance.
• Data analysis bots: Used for web scraping, data mining, and sentiment analysis with the consent of the website owner.

Bad Bots

Bad bots are designed for malicious purposes, often leading to security threats and resource misuse. Examples include:

• Scraper bots: Steal data by extracting website content without permission.
• Spam bots: Submit fake information in forms and comment sections.
• Attack bots: Execute credential stuffing, DoS attacks, and card fraud attempts.

Organizations must differentiate between these bots to allow beneficial automation while blocking harmful activity.

In a denial-of-service (DoS) attack, bots overwhelm a server, network, or website with excessive requests, causing slowdowns or outages. Attackers may use a single bot (DoS) or a distributed network of compromised devices (DDoS) to flood a target with traffic.

The attack works by consuming the target’s bandwidth, processing power, or connection limits, making it difficult for legitimate users to access the service. Some bots use randomization techniques to evade simple blocking mechanisms, while others leverage application-layer attacks, such as repeatedly sending computationally expensive database queries to exhaust resources.

Web scraping involves bots extracting data from websites without permission. These bots systematically scan web pages, copy content, and collect valuable information, such as pricing data, product descriptions, or proprietary research. Competitors, fraudsters, or data resellers often use web scraping to gain an unfair advantage or repurpose stolen data.

Scraper bots operate by sending automated HTTP requests to a website and parsing the returned HTML or API responses. More advanced bots can execute JavaScript, mimic human behavior, and cycle through IP addresses to bypass basic detection. Some scrapers even use headless browsers to avoid detection mechanisms that block traditional bots.

Bots can automate form submissions to flood websites with fake data, disrupting normal operations and enabling fraudulent activities. These attacks often target registration forms, comment sections, or customer inquiry forms to submit spam messages, create fake accounts, or manipulate online reviews.

The attack works by programming bots to repeatedly fill out forms with predefined or randomized data. Some bots use scripts to bypass validation checks, while others employ CAPTCHA-solving services to get past security barriers. Fake accounts created this way are often used for spam, fraud, or social engineering attacks.

Credential stuffing is an automated attack where bots use stolen or breached username-password pairs to gain unauthorized access to user accounts. Attackers obtain these credentials from past data breaches and test them across multiple websites, exploiting the fact that many people reuse passwords.

The attack works by automating login attempts with large lists of stolen credentials. Bots submit these credentials at scale, often rotating through different IP addresses or using proxy networks to avoid detection. When a match is found, attackers gain access to the compromised account, which they can then exploit for financial fraud, data theft, or resale on dark web marketplaces.

Carding is a type of fraud where bots test stolen or generated credit card details to identify valid accounts for unauthorized purchases. Attackers use botnets or scripts to automate payment attempts on e-commerce sites, often testing thousands of card numbers in a short period.

The attack works by rapidly submitting card details—such as card number, expiration date, and CVV—on payment gateways. Bots often exploit weak security configurations, such as websites that do not limit failed transactions. When a successful charge occurs, attackers confirm that the card is valid and either use it for fraudulent purchases or sell the information to other criminals.

The basic way to identify bad bots is device and browser fingerprinting—collecting and analyze unique attributes of a user's device to create an identifier that helps distinguish bots from humans. Unlike cookies, which can be deleted or blocked, fingerprinting relies on persistent characteristics such as screen resolution, installed fonts, operating system, browser version, and hardware configurations.

Advanced fingerprinting methods also track inconsistencies in browser behavior. A bot may attempt to spoof its user agent to appear as a legitimate browser, but other attributes, such as canvas rendering or WebGL properties, may reveal discrepancies. Some bots rapidly switch identities to evade detection, but a strong fingerprinting system can correlate multiple sessions to identify and block suspicious activity.

Rate limiting restricts the number of requests a user or IP can send within a specified time frame, preventing excessive traffic from overwhelming a system. Static rate limiting applies fixed thresholds, but adaptive rate limiting dynamically adjusts limits based on traffic behavior. This technique is particularly effective against credential stuffing, web scraping, and carding attacks.

For example, if a login page typically receives 50 requests per second but suddenly spikes to 500, an adaptive rate limiter can detect the anomaly and throttle incoming traffic. Similarly, if an IP address sends an unusually high number of API requests within a short period, the system can slow down responses or block access.

Traditional CAPTCHAs, such as image recognition and text-based challenges, have become less effective due to AI-driven bots that can solve them with high accuracy. Newer alternatives provide better security with minimal user friction.

• Behavioral CAPTCHAs: Analyze user interactions, such as mouse movements and scrolling patterns, to determine whether an action is human-driven.
• Invisible CAPTCHAs: Work in the background by tracking user behavior over time, only presenting a challenge when suspicious activity is detected.
• Proof-of-work challenges: Require computational effort to complete, making automated attacks costly and impractical while remaining unnoticeable to regular users.

Behavioral analysis focuses on tracking user interactions to differentiate between human and bot activity. This technique examines various behavioral patterns, such as mouse movements, keystrokes, scrolling speed, and session duration. Unlike humans, bots often exhibit unnatural behavior, such as instant form completions, precise cursor movements, or high-frequency requests in short intervals.

Anomaly detection improves behavioral analysis by identifying deviations from normal user activity. It relies on statistical modeling, heuristics, and real-time monitoring to flag suspicious behaviors, such as rapid consecutive logins from different geolocations or excessive page requests from a single IP.

Machine learning (ML) models improve bot detection by identifying subtle differences between human and automated interactions. These models analyze vast amounts of traffic data to learn the characteristics of genuine users and bots, allowing them to detect evolving attack patterns that rule-based systems might miss.

Supervised learning models rely on labeled datasets of known bot and human interactions to classify traffic accurately. Unsupervised models use clustering and anomaly detection techniques to identify suspicious activity without predefined labels. Reinforcement learning can also be used to adapt security measures, adjusting defenses based on changing bot behavior.

AI-driven bot protection can automate threat response, enabling security teams to block, challenge, or monitor suspected bots in real time. This approach improves detection accuracy and reduces false positives compared to traditional security methods.

Honeypots are decoy systems to lure and detect bots without affecting real users. These traps take various forms, including hidden form fields, fake links, and dummy API endpoints that are invisible to humans but attractive to automated scripts.

For example, a website may include an additional input field in a login form that is hidden from human users via CSS. A bot that auto-fills all form fields, including the hidden one, reveals itself and can be blocked. Similarly, trap URLs that appear legitimate to web scrapers can redirect malicious bots to sandbox environments where their behavior can be analyzed.

Real-time monitoring tools track incoming traffic, analyze request patterns, and detect anomalies as they occur. These tools use dashboards, alerts, and logging systems to provide visibility into bot activity, helping security teams respond quickly to emerging threats. Live analytics help prevent automated attacks before they cause damage.

Key indicators of bot activity include sudden traffic spikes, repeated failed login attempts, unusually short session durations, and access requests from high-risk IP addresses or known botnets. By integrating real-time monitoring with security information and event management (SIEM) systems, organizations can correlate bot activity across their infrastructure.

APIs are prime targets for bot attacks, making strong security measures essential. Attackers often exploit APIs for data scraping, account takeovers, or automated fraud.

To protect APIs, organizations should implement:

• Authentication methods: Such as OAuth, API keys, and mutual TLS to restrict access to trusted clients.
• Rate limiting and throttling: To prevent abuse from excessive requests.

• Bot scoring and anomaly detection: To analyze request patterns and block suspicious activity.
• Web application firewalls (WAFs) and API gateways: To filter out malicious traffic before it reaches backend systems.

Additionally, APIs should enforce proper access controls, including least-privilege permissions, to minimize potential attack vectors. Security teams should regularly audit API traffic logs and use AI-driven solutions to detect evolving threats in real time.