Bot mitigation in cybersecurity

Not all web traffic comes from human users. According to Statista, almost half comes from bots. Some bots are helpful, like search engine crawlers, while others are malicious and used for attacks.

Bot mitigation is a series of techniques companies use to detect and block malicious bots before they can harm sites, apps or networks.

A bot is a piece of software programmed to perform tasks over the internet with little to no human involvement. Bots can range from simple scripts that read data to complex systems that mimic human behavior.

Non-human identities: The good bots

Not all bots are bad. Many are used for machine-to-machine communication, automation and security. Some examples of “good bots,” also known as non-human identities, are:

• Search engine crawlers that index web pages for search results
• Chatbots that assist customers on websites
• APIs and automation bots that handle data transfers and system integrations
• Security and monitoring bots that scan for vulnerabilities and unusual activity

Even though these bots serve useful purposes, they should still be monitored. If left unchecked and unsecured, cybercriminals can hijack their identities to inject malicious code, bypass security controls or scrape sensitive data.

Here are some of the most common types of bot-driven attacks:

1. Distributed Denial-of-Service (DDoS) attacks

In a DDoS attack, a botnet (group of bots) is used to flood a target server, website or application with overwhelming amounts of traffic. This overload can cause slowdowns or complete outages, effectively making the service inaccessible to real users.

2. Credential stuffing

In credential stuffing, bots use lists of usernames and passwords (typically obtained from data breaches) to gain access to user accounts. These attacks work because people often reuse the same password across multiple platforms.

3. Password spray

Password spraying bots try a commonly used password (like “password123” or “admin”) across many accounts, before moving on to the next password. This low-and-slow approach helps attackers avoid detection.

4. Automated phishing attacks

Bots can be programmed to send large volumes of phishing emails or messages. These emails trick users into revealing login credentials, financial details or other sensitive data. Some phishing bots can also create fake websites to capture user data and can even personalize phishing attempts based on information gathered from multiple sources.

Now that we know just how harmful the “bad” bots can be, let’s explore strategies to mitigate the threats they pose:

1. JavaScript challenges

JavaScript challenges are a common method used to detect bots. When someone visits a website, the server sends a JavaScript code snippet for their browser to execute. Legitimate browsers can run this code and return the correct response, while many bots (especially simple ones) cannot and are blocked. This method is often used on login pages, payment gateways and other sensitive areas to prevent automated attacks.

2. User Behavior Analytics (UBA)

User Behavior Analytics, also known as Behavior-Driven Governance (BDG), tracks user actions like logins, resource access and application usage to detect anomalies that may signal insider threats or bot activity. It establishes normal behavior patterns and flags deviations such as unusual login times or excessive access requests.

3. Device fingerprinting

Device fingerprinting collects information about a user’s device, such as browser type, operating system and screen resolution. This data is used to create a "fingerprint" that can identify returning users and filter out bots. Since malicious bots often lack the diversity of real devices, and exhibit inconsistencies in their fingerprints, they are easier to spot and block.

4. Privileged access management (PAM)

Privileged access management (PAM) uses authentication, behavioral verification and monitoring to restrict the access of authorized (good) bots to sensitive systems. For example, a PAM solution can securely store and rotate bot credentials to prevent unauthorized use. Similarly, it can monitor bot activity in real time to flag suspicious behavior such as excessive requests.

Use the following proactive detection strategies in addition to mitigation strategies to strengthen your defense against bots.

1. IP reputation monitoring

Numerous services and databases compile information about IP addresses that are associated with malicious activities, such as spamming or botnet command and control. IP reputation monitoring systems compare incoming traffic to these reputation lists to identify and block requests originating from suspicious IP addresses.

2. Traffic anomaly detection

In this technique, intelligent systems monitor traffic patterns to identify abnormal spikes or unusual activity. For example, a sudden surge in login attempts, excessive API calls or repeated failed transactions can indicate automated bot activity.

3. Behavioral analysis techniques

Bots struggle to replicate human-like interactions. Behavioral analysis examines factors like mouse movements, scrolling behavior and typing patterns to detect and block automated scripts. For example, a credential stuffing bot will input credentials at an unnatural speed or interact with web elements in a way that differs from real users.

AI and ML have revolutionized the field of cybersecurity, and bot mitigation is no exception. Here are some ways in which bots help with Ai in cybersecurity:

• ML models analyze traffic patterns and adapt to new bot behaviors without requiring manual rule updates.
• AI-driven security tools can block or challenge suspicious activity in real-time.
• ML helps identify botnets by spotting similarities in IP addresses, user agents and request sequences.
• Since AI and ML solutions can handle massive volumes of data, they are ideal for large-scale environments with high traffic.

Next, let’s look at some key bot security challenges and how to address them:

a. CAPTCHA bypass techniques

Modern bots are becoming more and more sophisticated at solving CAPTCHA challenges. Attackers:

• Use AI-driven solvers to recognize and complete CAPTCHA challenges
• Exploit flaws in the implementation or specific versions of CAPTCHA systems
• Misuse text-to-audio conversion features to bypass audio CAPTCHAs designed for accessibility

Solutions:

• Implement solutions like Google's reCAPTCHA v3, which analyzes user behavior in the background and provides a risk score
• Monitor CAPTCHA failure rates to detect automated abuse.
• Keep CAPTCHA systems updated to benefit from the latest security enhancements

b. Honeypot deployment strategies

Honeypots are hidden traps designed to catch bots, but attackers can detect and avoid them. Common evasion tactics include:

• Scanning for honeypot indicators, like hidden form fields
• Identifying and avoiding known bot traps through reconnaissance
• Using real browser automation tools to mimic human behavior

Solutions:

• Regularly rotate honeypot techniques to stay ahead of attackers
• Combine honeypots with behavioral analysis to flag evasive bots
• Use multiple layers of bot detection to prevent circumvention

To implement a comprehensive bot mitigation policy, adopt these best practices:

• Use rate limiting: Restrict the number of requests from a single IP to prevent bot-driven abuse.
• Deploy multi-layered authentication: Combine techniques like multi-factor authentication and risk-based authentication to block automated attacks.
• Implement allowlisting for known good bots: Explicitly allow access for legitimate bots, such as performance monitoring agents, after verifying their identity.
• Educate users and admins: Train teams to recognize signs of bot activity and suspicious traffic.
• Utilize threat intelligence feeds: Integrate with threat intelligence platforms to gain insights into known malicious botnets and attack campaigns.

As technology continues to evolve, we can expect the bot mitigation landscape to become even more complex in the future.

On the attacker's front, we can anticipate a significant increase in the sophistication and prevalence of AI. For example, AI models are already making it easier and faster to create bots that can mimic human behavior with remarkable accuracy. Expect AI-driven phishing bots, deepfake-powered social engineering attacks and self-learning malware to become more common.

On the other hand, the cybersecurity industry will leverage the same AI and ML technologies to stay ahead of these threats. Security teams will rely more on behavioral analytics, biometric authentication and zero-trust architectures to limit bot-driven threats.