Despite the best efforts of security teams everywhere, bots are an increasingly frustrating challenge. Web traffic can be difficult to manage at the best of times, but the growing use of bots (and the increasing sophistication and subtlety of those bots) means that illegitimate traffic is harder to stop than it used to be. Some bots are built with artificial intelligence (AI), which enables them to behave very similarly to legitimate users and slip past your defenses.
This puts your organization at risk of DDoS attacks, account takeovers, data scraping, and other attacks. Fortunately, there are tools you can use to help manage traffic and identify even the slipperiest of AI-powered bots. Advanced bot protection solutions use AI and machine learning to detect, filter, and block bad traffic without sacrificing your legitimate customers’ ability to access your website or application. This automated solution uses the technology that bots are built on to combat them.
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The Growing Bot Management Challenge
Malicious bots can be programmed to do just about anything, but some common uses include compromising credentials (either by credential stuffing or brute force), ad click fraud, DDoS attacks, and website scraping. Most of the time, a bot attack will be expensive and frustrating, but if administrative credentials are compromised, the stakes could be higher. A bot that can access your sensitive data and controls can severely impact your security and business continuity. It could also damage your relationship with your customers. A data scraping attack might mean their data is sold on the dark web, for example.
To combat these attacks, many organizations rely on CAPTCHA and other traditional bot management strategies; however, they are no longer enough to address new challenges. Sophisticated bot attacks are overwhelming traditional filters, and AI-powered bots can now solve a CAPTCHA faster than a (probably irritated) customer. As a result, websites and web applications have been struggling to effectively limit things like account takeover attacks, scraping, and carding.
These attacks can be expensive for organizations, often costing hundreds of thousands of dollars due to downtime and lost revenue. Additionally, bot mitigation strategies can get in the way of business operations. Traditional strategies tend to hamper legitimate traffic as well as bots, and many customers will sooner give up on your website before they will make a third try at your CAPTCHA. However, organizations can transition their bot management strategy to a solution that incorporates machine learning and artificial intelligence to reduce costs and improve bot detection.
Uses of Machine Learning for Bot Management
Attackers have been able to leverage technological developments to improve bot attacks and evade detection. Although this can be frustrating, your organization can use the same tools to mitigate attacks and detect bots. Machine learning and artificial intelligence are integral to a few very effective strategies:
- Device Fingerprinting. By analyzing a user’s hardware and software components and network information, AI-powered bot detection will create a profile, or fingerprint, of that user’s device. Using this fingerprint, the detection software can track that device’s activity and flag it if the rate of requests is abnormally high. Device fingerprints are typically retained, so if a device is compromised, the software will compare unusual activity to baseline and block that device from communicating with the server.
- Behavioral Analysis. When a bot attempts to access your site or app, it generally does not behave like a human user. Instead of progressing through information or calls to action, it often clicks on the same thing multiple times or spends very little time on each page. While a user might take a few seconds to at least read the first sentence of the page, the bot will move on much more quickly. By analyzing behavior in traffic, bot management solutions can discern which patterns most reflect a human and which most reflect a bot.
- Anomaly Detection. Bots designed to do a particular thing tend to act in particular ways. For example, a scraper bot works its way through a website systematically to collect all of the information it seeks. Machine learning can digest patterns like this, and the software will automatically detect and deter them during an attack.
Keeping Up with the Bots
Although it can be tempting to leave traditional bot detection and attack prevention measures in place, increasingly sophisticated bots pose a high risk to your organization. To effectively prevent compromising your infrastructure, use a bot management tool that leverages artificial intelligence and machine learning. Effective bot management tools should cover your website, APIs, web applications, and all other access points.
Automated attacks, or OWASP threats, take advantage of vulnerabilities at these access points. Bot management software should provide visibility into these attacks through automated endpoint monitoring and alerts. Look for discovery and classification capabilities, and verify that any tool you consider will be scalable as your organization grows.
One of the most important components of these tools is the machine learning capability. With machine learning, bot management tools can effectively learn the patterns and behaviors typical of bad bots and then respond appropriately, without substantial oversight from you or your team. Tools like this are proactive and highly adaptable, which is important for reducing your response time. By using this kind of tool, you can move past rule-based filters and CAPTCHAs, which will vastly improve your odds of keeping up with automated attacks.