Think of bots as online troublemakers. On Twitter alone, 5% of monetizable daily active users are automated bots. The situation is no better on Instagram, where these automated accounts generate 20% of comments.
But it's not just a social media thing. These bots are also causing issues on websites, creating problems like cybersecurity threats and business fraud. From January to June 2023, half of internet traffic came from bots, and 30% was from bad bots. The cost of dealing with them, especially in terms of digital ad fraud, is a massive $100 billion.
What is Bot Detection?
Bots are a type of non-human small software that tries to act like humans. Bot detection is the process of identifying these bot activities among human users.
Good Bots And Bad Bots
The intention of “good” bots is to perform helpful tasks. For example:
Web crawlers
Chatbots
Monitoring bots
Twitter bots
Discord bots
Facebook Messenger bots
On the other hand, “bad” bots are the bots that purposely enact malicious and harmful tasks. They are often used for spamming, DDoS attacks, fake accounts, and credential stuffing.
How Does Bad Bot Detection Work?
Bad bot detection analyzes user behaviors, IP addresses, volume of activities, session durations, and even device fingerprints to spot suspicious bots.
Why is Bot Detection so Important?
Financial losses: Bots can commit fraudulent transactions, generate fake clicks on ads, and even scoop up limited-edition products.
Damaged reputation: Spamming, fake reviews, and the spread of misinformation by bots can harm your brand image.
Compromised security: Bots can steal sensitive data, attempt to use and manipulate stolen credentials, and even launch DDoS attacks.
Wasted resources: Bot traffic consumes valuable server resources and distorts website traffic data.
Critical Challenges of Bot Detection
Evolving bot capabilities: Modern bots mimic human behavior, spoof browser fingerprints, and switch IPs seamlessly, making them almost indistinguishable from legitimate users.
Limited resources: Evaluating and enhancing detection methods demands realistic data, yet public datasets are frequently limited and outdated.
Rapidly changing threat landscape: Bot developers constantly innovate, adapting techniques to bypass existing detection methods and launch new attacks on emerging technologies.
Lack of standardization: The definition of a 'bot' varies across industries and platforms, hindering the development of universally effective solutions.
Top 7 Strategies for Effective Bot Detection
1. CAPTCHAs
CAPTCHA identifies humans and bots by providing challenges such as simple puzzles, distorted images, or seemingly nonsensical characters.
With its low false positive rate, CAPTCHAs have been a widely adopted mechanism on most websites for several years. Also, it is a scalable solution and can be readily implemented on various websites and platforms.
<!DOCTYPE html><head> ... <!-- Include the reCAPTCHA API script --> <script src="https://www.google.com/recaptcha/api.js" async defer></script></head><body><form action="process_form.php" method="POST"> ... <div class="g-recaptcha" data-sitekey="YOUR_RECAPTCHA_SITE_KEY"></div> ...</form></body></html>
Actionable Tips:
Opt for user-friendly CAPTCHA types like checkbox-style or image-based challenges.
Ensure HTTPS usage for encrypted communication.
Prevent abuse by setting limits on CAPTCHA attempts.
Include alternative text for screen readers.
2. Traffic Monitoring
Traffic monitoring is a widely adopted method for bot identification that detects suspicious acts early and takes timely actions, such as blocking suspicious IPs or implementing a rate limiting. Moreover, this method is cost-effective, and implementation effort is minimal as most web servers automatically generate traffic logs.
By monitoring traffic, you can identify suspicious activities that indicate bot presence, such as:
Sudden spikes from specific IP addresses.
Repeated access attempts on sensitive pages.
Unusual browsing patterns and rapid clicks.
Access from known bot networks.
Actionable Tips:
Set up automated systems to detect sudden spikes in traffic from specific IP addresses.
# Example command to block an IP address (Linux)sudo iptables -A INPUT -s <IP_ADDRESS> -j DROPImplement rate limiting to curb excessive requests.
Review traffic logs regularly.
Keep a list of known bot networks and cross-check incoming traffic against this list.
3. Rate Limiting
Rate limiting is a technique used to control the volume of requests from a single user or IP address within a defined timeframe. It involves setting thresholds for the requests a user can make within a specific time window. If a user exceeds this limit, the server takes preventive actions, such as delaying or rejecting further requests.
Here is how to implement rate limiting with Python Flask-Limiter library:
from flask import Flaskfrom flask_limiter import Limiterfrom flask_limiter.util import get_remote_addressapp = Flask(name)# Set up rate limitinglimiter = Limiter( app, key_func=get_remote_address, default_limits=["5 per minute"] # Adjust as needed)# Define a route with rate limiting@app.route('/limited-resource')@limiter.limit("2 per minute") # Adjust as neededdef limited_resource(): return "This resource is rate-limited."if name == 'main': app.run(debug=True)
Actionable Tips:
Ensure that the rate limit is set by analyzing the website's normal traffic patterns.
Include burst allowances to manage legitimate traffic spikes.
Apply rate limiting at multiple levels (load balancer, web server, application server).
4. Honeypots
Honeypots in websites work as decoy systems to mimic real website elements that appeal to bots. These can be unused forms, hidden links, or pages invisible to human visitors. Bots reveal the presence and intentions of honeypots during engagement.
Take a look at the honeypot hidden inside the form below.
<form method="POST"> <label for="honeypot" style="display: none;">Leave this blank:</label> <input type="text" id="honeypot" name="honeypot" autocomplete="off"> <label for="message">Your message:</label> <textarea id="message" name="message"></textarea> <button type="submit">Send</button></form>
Here, we have a simple demonstration of handling that honeypot.
from flask import Flask, requestapp = Flask(__name__)@app.route("/", methods=["GET", "POST"])def index(): # Check for honeypot field if request.form.get("honeypot"): # Honeypot triggered, likely a bot print("Potential bot detected!") return "Invalid request", 400 # Process legitimate user requests if request.method == "POST": # Example: process form data message = request.form.get("message") print(f"Received message: {message}") return "Message received!" else: # Display homepage return "Welcome!"if name == "__main__": app.run(debug=True)
Actionable Tips:
Randomize field names and implement dynamic honeypots.
Track and analyze the IP addresses associated with the form submission.
Set time-based honeypots only visible for a specific period.
5. Blocking Bot Networks
Blocking bot networks involves implementing filters to block traffic originating from known bot networks or IP addresses. Security companies maintain these IP address blacklists, making it easier to implement filter mechanisms.
The Python code below demonstrates a simple approach to blocking bot networks.
from flask import Flask, request, jsonifyblacklist = { # List of known bot IPs or IP ranges "127.0.0.1": True, "192.168.1.1/24": True,}app = Flask(name)@app.route("/", methods=["GET", "POST"])def index(): # Extract client IP address client_ip = request.remote_addr # Check if IP is blacklisted if client_ip in blacklist: return jsonify({ "message": "Access denied.", "reason": "Your IP address is blacklisted." }), 403 # Process legitimate requests if request.method == "POST": print(f"Received request from {client_ip}") return jsonify({"message": "Request processed."}) else: return "Welcome!"if name == "main": app.run(debug=True)
Actionable Tips:
Maintain an up-to-date blacklist.
Keep a whitelist for the trusted users.
Use GeoIP blocking.
Implement dynamic blocking by analyzing traffic without only depending on static blacklists.
6. Behavioral Analysis
Behavioral analysis analyzes user interactions and movements to identify bots based on their deviations from typical human behavior. It focuses on navigation patterns, mouse movements, clicking patterns, dwell time, and form completion.
To implement behavioral analysis, you must define normal user behaviors and set thresholds for deviations. It is a somewhat advanced concept, and utilizing dedicated analytics tools for this purpose can be highly beneficial.
This code demonstrates a basic implementation of FingerprintJS for behavioral analysis.
from fingerprintjs import FingerprintJSInitialize FingerprintJS clientfpjs = FingerprintJS(api_key="YOUR_API_KEY")def is_bot(request): # Extract visitor fingerprint visitor_fingerprint = request.headers.get("User-Agent") + request.remote_addr # Analyze visitor fingerprint try: response = fpjs.get_visitor_data(visitor_fingerprint) except Exception as e: print(f"Error analyzing fingerprint: {e}") return False # Check for suspicious behavior if response.get("confidence") < 0.5: return True # High chance of being a bot if response.get("is_proxy") is True: return True # Using a proxy might indicate bot activity if response.get("session_duration") < 60: return True # Very short session duration could be a bot if response.get("page_visits") < 3: return True # Low number of page visits might be suspicious # Otherwise, likely not a bot return FalseExample usageif is_bot(request): print("Potential bot detected!")else: print("Likely a legitimate user.")
Actionable Tips:
Understand and define the normal user behavior of your website.
Set dynamic thresholds for different behavioral metrics.
Implement machine learning models to detect anomalies.
7. Web Application Firewall (WAF)
A Web Application Firewall (WAF) is a security solution designed to protect web applications from online threats like XSS SQL injection, CSRF, etc. It acts as a barrier between a web application and the internet, monitoring, filtering, and blocking traffic based on predetermined security rules.
Features of WAF
When evaluated against other security solutions, WAFs present distinct advantages contributing to widespread adoption:
Signature-based detection: Compares the incoming traffic patterns with the known attack signatures to identify and block malicious bots.
Heuristic analysis: Analyzes unusual behavior and patterns that deviate from normal user activities.
IP address filtering: WAFs can block malicious IP addresses associated with known bot networks.
Input validation: Examines user input in forms and fields to safeguard against malicious code injection or exploitation of vulnerabilities by bots.
Protocol Validation: Validates and enforces adherence to communication protocols.
File Type Blocking: WAFs can block the upload or download of specific file types to prevent malicious file uploads or downloads.
However, choosing the right WAF tool for your application is not easy since various tools have different features. That's where open source WAF tools like open-appsec come in.
How Does Open-Appsec Differ from Other WAFs?
Behavior-based analysis with Machine Learning: open-appsec ML engine analyzes HTTP traffic for attack indicators and evaluates the likelihood of malicious activity based on behavior, open-appsec does not rely on signatures at all. This allows open-appsec to detect true zero day attacks in addition to known attacks.
Rate Limit/DDoS Protection: open-appsec offers a rate limiting feature to control the amount of requests per timeframe and to avoid DDoS attacks.
API security: Stops malicious API access and abuse.
Integration into modern environments: Support NGINX Ingress Controller, NGINX Proxy Manager Integration, NGINX and Kong Gateway on Kubernetes, Linux Servers and Containers (Docker).
Bot prevention: open-appsec anti-bot protection follows a three-step procedure:
Inject scripts into web application pages, such as login pages.
Collect data about input patterns and canalize keystroke sequences, mouse moves, and finger touches.
If a bot artificially creates such patterns, open-appsec identifies them.
open-appsec is an open-source project that builds on machine learning to provide pre-emptive web app & API threat protection against OWASP-Top-10 and zero-day attacks. It simplifies maintenance as there is no threat signature upkeep and exception handling, like common in many WAF solutions.
To learn more about how open-appsec works, see this White Paper and the in-depth Video Tutorial. You can also experiment with deployment in the free Playground.
