Bot traffic is exploding across the web. Bots now account for over 47% of all requests to websites according to Imperva research. The majority perform beneficial tasks like search engine crawling. However, malicious bots grew over 25% last year to become 30% of traffic. Site owners must implement robust bot management or risk serious business disruption.
Understanding the Bot Landscape
Bots are software applications that perform automated, repetitive tasks much faster than humans. With this speed and scale, bots can be used for good or ill:
- Search engine crawlers – Index web pages to serve results
- Price comparison scrapers – Gather data to help consumers
- Site monitors – Check uptime and performance
- Chatbots – Provide customer service support
- Spam bots – Scrape emails and spread spam
- Vulnerability scanners – Probe sites for security flaws
- Scrapers – Steal copyrighted content
- DDoS bots – Overload and take down sites
Bot Traffic is Exploding
Bot traffic has rapidly accelerated as bots get easier to deploy:
Major sites like Google receive over 90% of their traffic from bots crawling content and services. However, most of this volume is from benign bots critical to business functions.
The real concern is malicious bots growing to threaten online business, privacy, and security.
Bot Detection Techniques
Sites use various bot detection techniques to identify and manage automatied traffic:
Analyze properties like OS, time zone, fonts, etc. that form a unique browser fingerprint. Bots often don‘t spoof enough elements to impersonate humans.
# Script to extract browser fingerprint import fingerprintjs from ‘fingerprintjs‘ fingerprint = await fingerprintjs.load() result = await fingerprint.get() print(result.visitorId)
Monitor how visitors interact with your site. Things like rapid clicks/inputs, repetitive patterns, and lack of cursor movements signal bots.
Maintain blacklists of IP blocks known to originate bad bot activity. Immediately block requests from listed IPs.
Challenge users with tests like deciphering distorted text or identifying images. Easy for humans but difficult for bots.
Train AI models on traffic patterns from real humans vs. bots. Models can effectively recognize subtle bot behavioral signals.
Latest Bot Detection Challenges
Sophisticated bots are now mimicking human behaviors to appear legitimate:
- Natural mouse movements
- Variable keyboard inputs
- Multi-step interactions
- Randomized behaviors
Simple techniques like fingerprints and CAPTCHAs no longer suffice. The most advanced bots leverage AI to learn human patterns.
To keep up, defenders require constantly updated machine learning models trained on newly discovered bot tells. The detection arms race demands ever more powerful AI.
Real-World Bot Attack Examples
In 2020, hackers used bots to brute force employee login portals and gained access to personal data of over 100 million customers.
Los Angeles Times DDoS
The newspaper‘s site was taken offline by a 1.3Tbps DDoS botnet attack in 2020 after publishing an unfavorable story.
This infamous IoT botnet infected over 500,000 devices in 2018. It was used to launch massive DDoS attacks taking down sites like Github.
Bot Prevention Tips
Site owners have several options to secure sites against malicious bots:
- WAFs – Web application firewalls filter bot traffic based on rules.
- Rate Limiting – Limit requests per IP to prevent abuse.
- Honeypots – Decoy traps to detect and learn from bot probes.
- Bot Management Services – CDNs and cloud providers offer advanced bot defenses combining IP reputation, fingerprinting, CAPTCHAs, and AI.
Top services include Cloudflare, Akamai, Imperva, PerimeterX, and DataDome.
Ensuring Legitimate Access
However, overzealous bot defenses risk blocking beneficial bots like:
- Search engine crawlers – Locking out Googlebot kills your search rankings.
- Scrapers – Many aggregate public data for research.
- Site monitors – Need access to regularly check uptime.
To avoid erroneous blocks, legitimate bots simulate organic user actions:
- Proxy rotation – Swap IPs to avoid rate limits.
- Header spoofing – Mimic real browser fingerprints.
- Mouse movements – Use a headless browser with randomized cursors.
- Purpose-built tools – Leverage scrapers designed to disguise bots.
Looking Ahead at the Bot Arms Race
As bot operators stay one step ahead of defenders, the impacts of collateral damage continue to grow. Until a decisive high-tech solution emerges, the cat and mouse game will rage on.
Businesses must remain vigilant – constantly updating defenses while enabling legitimate bots vital to web functionality. With a balanced approach, we can work toward a bot-filled but not bot-ruined future online.