How to identify bots and stop hacker attacks?

A bot is an automation tool that attackers use for a whole range of attacks, from DDoS to vulnerability scanning. The problem with bots is that they work fast, automated and on a completely different wavelength than the average user. 

How to identify them? How to prevent hacker attacks if you manage a website or online service? Here are a few ways with an explanation of each step.

User behavior analysis: filter suspicious actions immediately

Normal users are different, but they have similarities: they don't read 300 pages in 2 seconds or click 50 times in the same area. This is the basis of behavior analysis.

Bots can log in and visit thousands of pages without pausing, ignore complex interfaces, and bypass complex visual elements. 

Use behavioral indicators to detect anomalies. For example, Distil Networks' behavioral analysis system checks the speed and frequency of actions by identifying patterns typical of bots.

If you see a user who sends 100 requests per second or clicks on every link in milliseconds, you can be sure it's a bot. A human doesn't do that.

Fingerprinting: when uniqueness plays into your hands

Every user, whether human or bot, leaves a “digital fingerprint”. This is a combination of factors: browser, screen resolution, plugins, system settings, and more. The uniqueness of the settings helps identify repetitive or suspicious requests that may come from a bot.

If multiple requests come in with exactly the same configurations (same User-Agent headers, same screen resolution, and same plugins), it could signal a bot using a script with fixed settings.

CAPTCHA and other verification mechanisms

Even Google uses this good old-fashioned way of fighting bots. There is an important nuance - a simple CAPTCHA can be hacked or passed by bots with advanced image recognition algorithms. reCAPTCHA v3 analyzes user behavior before and after validation, assigning a “trust score”.

If a user solves complex images with accuracy and high speed - you have to wonder who is in front of you. Either artificial intelligence at the GPT-4 level, or hackers have added a neural network to their tools.

IP and geolocation analysis

When suspicious traffic comes in, the first thing to look at is the IP address. How many requests come from one IP? Does it exist in the database of known proxies? 

In the real world, users come from different IPs, but if there are hundreds of thousands of requests coming from the same IP in a short period of time, that's a red flag.

Use MaxMind or IP2Location databases to check if the IP matches real geolocation data.

Machine Learning: machine-level prediction

Unlike static rules (e.g., banning access from suspicious IPs), machine learning systems can analyze huge amounts of data and identify patterns that are difficult for humans to spot.

A machine learning model can analyze millions of queries and identify suspicious behavior, such as repeated cycles of actions that may indicate bots. Moreover, such models can “learn” from new threats and adapt to changes in hacker behavior.

Rate Limiting: rate limiting of requests

This is a simple but effective measure: limit the number of requests that can be sent in a certain period of time. Bots often send requests at a high rate, so imposing a limit helps reduce their activity.

Instead of allowing a single IP address to send 1000 requests per minute, set a limit of 100 requests. This will significantly slow down or stop the attack altogether.

Honeypots

Honeypots - hidden fields on the page that a normal user will not see and will not fill in. Bots, on the contrary, can accidentally fill in these fields and give themselves away. This method is effective against simple bots that cannot analyze the structure of the page.

A hidden form field can be used as bait: if a bot tries to fill it in, the system bans the IP.

Identifying bots and preventing hacker attacks is not a one-time task. It is a process that involves analysis, adaptation, and monitoring. Bots can disguise themselves, change IPs, and mimic user behavior, but using the methods described above, you can protect your project from threats.