More than 40% of all web traffic comes from bots.
Bots are inherently tools, computer programs designed to automatically execute certain processes or tasks, so they aren’t necessarily good or bad.
Some of these bots are beneficial to our website and business: they crawl and index our websites for the search engines; they monitor and inform us about various things like stock prices, weather, and so on; and some are designed to protect your websites and servers. Typically these “good bots” are owned and operated by legitimate companies like Google, Facebook, Cloudflare, and so on.
There are, however, bad bots operated by cybercriminals for malicious purposes, from performing account takeover (ATO) attacks, malicious data scraping, data breaches, and DDoS (Distributed Denial of Service) attacks, among other forms of cybersecurity threats.
These bad bots account for more than a quarter of total internet traffic, and it is the presence of these bots that demands an adequate bot management strategy to protect our website and business from this wide variety of bot attacks.
What Is Bot Management?
Bot management refers to the act of detecting and managing malicious internet bot traffic. Typically it is about blocking malicious bots from accessing a website, application, or other internet resources while ensuring legitimate users and good bots can still access web properties.
However, blocking the malicious bots isn’t always the best approach for all situations (. There are various management and mitigation methods we can use to prevent malicious bots from negatively affecting our website and our business. We’ll explore this more further below.
So, bot management is essentially divided into two different layers:
- Identifying bot traffic and distinguishing malicious bots from good bots
- Deciding the best approach to mitigate the malicious bot traffic from achieving its objectives
Do You Need Bot Management?
All businesses and even individuals with an online presence should have an adequate bot management strategy to detect and manage malicious bots that can negatively affect the web properties in the following ways:
- Account Takeover
Cybercriminals often use malicious bots to perform brute force, credential stuffing, and other forms of account takeover attacks to gain access to users’ accounts and steal the confidential data within, as well as using the account to perform unauthorized purchases and launch other forms of attacks.
- DDoS
DDoS (Distributed Denial of Service) attacks utilize bots and botnets to launch a massive amount of requests to a website or online service, significantly slowing down the website or crashing it completely to make the web service unavailable for its intended users. DDoS attacks can cause long-term and even permanent damages to your reputation besides the financial repercussions.
- Data and Content Scraping
Bots can scrape price information from your site and leak it to your competitors. This can cause a massive loss in competitive advantage if your eCommerce site is in a price-sensitive niche (i.e ticketing). Bots may also scrape your original content and republish it elsewhere, creating duplicated content and other SEO performance issues.
- Inventory Hoarding and Scalping
Another common problem for eCommerce sites, cybercriminals can use bots to add a massive number of products to the shopping cart. They can either hold the item to prevent real shoppers from buying these in-demand products or can confirm the purchase and re-sell the products at a higher price (scalping). These automated bot attacks can be very serious for eCommerce sites, creating various issues from reduced sales and long-term reputational damages.
- Click Fraud
Cybercriminals can use bots to generate false clicks to skew advertising costs. If your site is an ad publisher, this might initially increase your revenue from advertising, but if you are not careful the ad network (i.e. Google Ads) can penalize and even ban your site in suspicion of click frauds.
- Spam and Phishing
Another common use of bots is to spam comment sections, form submissions, forums, and others with fraudulent links, and also to send emails with malicious attachments.
Bot Management: How To Detect Bot Traffic
At a glance, bot and botnet detection might seem relatively simple: the objective is to accurately detect malicious bots while ensuring we are blocking as few legitimate users and good bots as possible (avoiding false positives).
However, with today’s very sophisticated bots, the detection process will become more complex, more costly, and will also need more time. Meaning it’s possible that without a proper detection method and technology, you won’t be able to accurately detect the presence of these bots before it’s too late.
For less sophisticated bots, we can perform basic detection methods like IP and HTTP reader analysis to block known IPs and user agents of bad bots. We can also use device fingerprinting to analyze the presence of headless browser signatures.
However, with newer bots that have employed AI and machine learning technologies, an adequately powerful bot detection solution that also employs deep machine learning technologies is also required to analyze the bot’s interactions with the website (mouse movement, keystroke anomalies), as well as the correlation in intent signatures across devices.
Bot Mitigation Alternatives: How To Manage Bot Traffic Besides Blocking
Once the presence of malicious bots has been detected, blocking the bot might seem like the most obvious approach to bot management. However, there are other alternatives we should consider:
- Rate-limiting
An effective approach for combating persistent attackers that will simply return with another/modified bot when it’s blocked is rate-limiting or throttling. By replying to the bot’s requests at a slower pace, the hope is that the attacker will be discouraged and may move on to other targets.
- Feeding fake content to the bot
Similar to throttling in principle, in this approach, we attempt to keep the bot active and waste its resources. But instead of replying to its requests with real data, here we reply with fake content, for example redirecting the bot to another page with modified/fake content.
Conclusion
An effective bot management strategy should be able to uniquely identify malicious bots from legitimate users and good bots, which can be a challenge for the most sophisticated bots in circulation at the moment.
With that being said, it’s crucial to have an AI-powered bot management solution in place to consistently avoid false negatives (to ensure we’re accurately detecting all bad bots) and also false positives (avoid blocking legitimate users and beneficial bots.)
