Automation isn’t always a bad thing; in fact, much of the good and necessary online functionality that you rely on is made possible by bots. However, many bots are malicious, and attackers are building bad bots that are increasingly difficult to filter, leading to an uptick in successful attacks.
Traditional bot detection has struggled to keep up, but newer solutions that incorporate AI and machine learning have been more successful. By increasing their adaptability and using behavioral analysis, these bot detection solutions have a higher success rate. Blocking bots without blocking legitimate traffic is essential, and it is made easier with tools that offer behavioral analysis to differentiate between a typical human pattern and a bot pattern.
Why is Behavioral Analysis Needed for Bot Detection?
Depending on which report you read, between 40 and 50% of all internet traffic is bots. Some of these bots are good and necessary, like the web crawlers sent out by search engines. Other bots, however, are not so benign, and your web applications and websites need to be protected from them.
Malicious bot activity can lead to many different attacks. To combat them, many organizations are implementing bot detection solutions to distinguish between legitimate traffic and malicious or illegitimate traffic.
Bot detection has, unfortunately, become a bit trickier lately. To stay ahead of security teams, malicious actors have built more evasive bots that are challenging for security tools to identify and block. With AI becoming more accessible, many bots are powered by AI and machine learning, making them even more formidable.
This poses a major problem for your security. Being able to differentiate between legitimate human users and malicious or unwanted automated traffic is imperative as you must be able to keep unwanted bots away from your data and infrastructure without blocking your customers and valued traffic.
To solve this problem, some advanced bot detection solutions have begun implementing behavior analysis and machine learning to improve pattern recognition. In behavioral analysis, bot detection tools analyze the way a user interacts with your application. There are behavior patterns typical to human users, and there are patterns typical to bots. By differentiating between the two, bot detectors can block the activity that matches bot behavior.
Without behavioral analysis, bot detection is still effective, but it is not as accurate, and increasingly sophisticated bots are able to evade it. The advantage of behavioral analysis is that it does not rely on known attack patterns to identify bots. Instead, it compares real-time user activity against a baseline of typical, legitimate activity. Deviations from the baseline are the first sign of an imminent attack.
How Bot Detection Identifies Bots
Attackers don’t stay behind for long. Although behavioral analysis has been effective at detecting bot traffic, security professionals have to be consistently improving bot detection tools. As bots are becoming increasingly sophisticated and human-like, it becomes more challenging to detect them.
For example, a typical human user might land on a website’s home page, spend about twenty seconds there, and then navigate to the next page. A bot, on the other hand, is likely to move between pages far more quickly. It may fill out forms on your site, but it will do this more quickly than a human and may complete the forms inaccurately and repetitively, as in a credential stuffing attack.
Advanced bot detection uses machine learning and AI to analyze behavior like this to identify and block bot traffic. The advantage of these tools is that they are able to independently adapt over time. Instead of requiring security professionals to constantly update stored attack patterns, AI-powered bot detection learns from the bots it detects and becomes more attuned to bot activity over time.
When AI, machine learning, and behavioral analysis are combined in a single bot detection solution, the results are highly accurate and effective at filtering illegitimate traffic. Behavioral analysis is informed by machine learning, which means the detection tools are well-equipped to keep up with even the most evasive malicious bots.
Protecting Against Malicious Bots
Behavioral analysis and AI come with a cost, and some organizations choose not to invest in these solutions. However, there are also costs to going without an effective bot detection solution. Bots drive several types of attacks, including:
- DDoS. Powered by increasingly large botnets, a DDoS attack is preventable with the right tools. Once this type of attack begins, though, stopping it is very challenging. Most of the time, these attacks monopolize your host’s resources, which limits your mitigation options. By the time a DDoS attack ends, many organizations suffer tens to hundreds of thousands of dollars in losses, depending on their size and amount of normal traffic.
- Credential stuffing. When malicious actors compromise one organization’s data, they sometimes acquire lists of credentials. They can then use these credentials to try to gain access to your network. Repeatedly inputting possible credential combinations is a credential-stuffing attack, and it can be sped up significantly with bots.
- Web scraping. Whether your competitors want to extract information about your products and prices or steal your company secrets, they can leverage bots in a web scraping attack to accomplish it. In web scraping attacks, malicious bots extract data from your website or application and export it to the competitor.
These and other attacks can be detrimental to your organization. While each attack has a different impact, each one can cause revenue losses and damage your reputation among your customers. The attacks can also reduce the number of new customers interested in your company.
To minimize your risk of bot-driven attacks, invest in a bot detection solution that can identify and block advanced, human-like bots. The best solutions will include automated monitoring, modulatory control over detection and blocking rules, bot fingerprinting, and API protection.
While malicious bots can often evade more traditional bot detection tools and tactics, solutions informed by AI and machine learning, have a much better chance of correctly identifying even the most evasive bots. With the right tools augmenting the detection capabilities, you can customize your approach and ensure that the right traffic still comes through.
