There are
many cybersecurity threats that businesses have to be aware of in 2024. Whether
it’s phishing, malware, ransomware, or social engineering, the methods of
cybercriminals have evolved and modernised, to the point where even
cybersecurity precautions from ten years ago have become largely
obsolete.
One of the
most crucial areas that has developed exponentially in recent years is API
abuse – specifically, utilising bots to exploit vulnerabilities in APIs,
manipulate data, or launch various forms of malicious attacks. In order to gain
bot protection, it has
become crucial to implement modern solutions where every request – whether
that’s to a website, a mobile app, or an API – is analysed and dealt with. But
how exactly is this done?
Rate Limiting
There are
many ways in which this can be achieved. When it comes to a traffic bot – bots
that are used to generate artificial traffic to a website – rate limiting has
become one of the most prevalent precautions amongst businesses. This involves
monitoring the number of requests coming from a single IP address over a
specified time frame, and working to classify this behaviour as potentially
bot-driven. For example, if an IP address tries to access a page more than one
hundred times in a minute, the site can temporarily block that address, or
require the user to solve a more advanced CAPTCHA before granting further
access.
Challenge-Response Tests
Speaking
of CAPTCHA, this is a common precaution that has evolved in recent years. You
will likely be familiar with traditional CAPTCHAs, whereby users are asked to
select images based on certain criteria, or complete question-based tasks. But
over the last few years, modern CAPTCHAs have been leveraged to catch out more
advanced bots – ones capable of mimicking human behaviour and bypassing
traditional CAPTCHA systems. A good example of this is ‘reCAPTCHA’, which
uses a risk analysis engine to assess user behaviour and determine quickly
whether they are human – without putting them through a text, image, or
question-based test.
Judging Behaviour
This is
known as behavioural analysis. While bots have been modernised and optimised
maliciously in recent years, they still simulate linear or mechanical patterns
– so while a human might pause or hesitate before clicking on a link, a bot
will move directly to the target with minimal variation. By tracking mouse
movements, keystroke dynamics, and navigation patterns, however, web
applications can employ algorithms that analyse human patterns and flag any
suspicious behaviour indicative of bot activity.
ML and AI
Machine
learning and artificial
intelligence technologies are being progressively used more by
cyber criminals, so in order to fight them, cybersecurity organisations must
apply their own AI initiatives. ML algorithms, of course, can be trained using
historical data to identify patterns that separate human users from bots. By
feeding these models a combination of known bot behaviour and legitimate user
behaviour, they can then learn to predict the likelihood of future requests –
and, what's more, they can learn to detect any updates or changes in the bots
themselves, adjusting the identification criteria in real-time to allow for
more robust and resilient detection.
Reporting Feedback
Another
way in which bots are identified is through allowing actual users to give
feedback. Especially through forums, blogs, or social media platforms, users
are now encouraged to report suspicious activity – such as spam
comments or rapid-fire submissions – and
subsequently open investigations into potential bot activity. While advanced
cybersecurity solutions will always be the most effective strategy, it’s
important to remember that users, too, can play a crucial role in the
mitigation of bot activity. Through keeping them up to date with the latest security
trends, crowdsourcing intelligence,
monitoring activity in real-time, and building community engagement, it's
possible to get more users invested in the security of their online
environment, and ultimately formulate a first line of defence.
Conclusion
These are
just a few ways in which bots get identified in 2024, as the landscape of
cybersecurity continues to evolve, it’s likely that we will see even more
solutions being implemented across organisations. One promising area, for
instance, is the use of
distributed ledger technology. Gaining traction in the cybersecurity
landscape, this technology can provide transparent and immutable records of
user actions that help to verify their authenticity, making it much harder for
bots to impersonate legitimate users and evade detection.
For now,
however, it’s crucial that every business does as much as it can to update
their bot management system and ensure they are not caught out. This isn’t just
about protecting sensitive data or preventing
financial loss, of course, it’s about maintaining
customer trust and safeguarding brand reputation. If a system breach is caused
by bot-driven attacks – and it's discovered a business did not adopt a
proactive approach – the reputational damage can easily be too great to
recover from. With this in mind, every bot must be taken seriously, recognised,
and dealt with before they can make such a devastating impact.
