In the past couple of years, cyber risks associated with cybercrimes and hackers are on the increase. Hackers are increasingly focusing on websites and application’s main communication channels and the fundamental building blocks are application programming interfaces or APIs these days, almost all applications have them. Over 20,000 public APIs are accessible from various websites and applications, claims a third-party report and we are sure that it is still a conservative number.

It is not an exaggeration to suggest that APIs are essential to the proper operation of the hundreds of billions of dollars in online commerce. With so much at stake, it can be assumed that APIs would be the most vulnerable and have the highest security risk. And rightly so, Gartner had suggested that in 2022, API attacks will become the most common attack vector. 

What is an API?

Web APIs let developers easily connect with applications without having to create specialised code or have a thorough grasp of the applications’ architecture. They expose the functionality of applications to the outside world. Access to some APIs requires developers to register for an API key. Since the company exposing the API does not intend to discourage use, many APIs are completely open. The important thing to remember is that APIs should be open and simple to use to facilitate interaction with and consumption of information and data that an organisation wants the public to have access to.

APIs are frequently used by e-commerce companies for both internal and external reasons. An e-commerce vendor may, for instance, have a single API with price and product data that offers data for the business’ website, mobile application, widgets for affiliate networks, third-party reseller websites, and good bots like search engine spiders for Google Shopping.

An application must be able to actively determine if an API is good, harmful, or unknown in order to securely protect it. Requests can be made in both good and harmful ways. The right handling of each API request must be decided dynamically in real-time rather than according to a set of fixed rules since API assaults change so frequently.

Why It’s so Hard to Spot and Stop API Bot Attacks?

APIs can operate as a direct conduit into certain resources and operations, as opposed to queries that must pass via browsers or native app agents. As a result, they are particularly appealing as a vector for attacks like carding, credential stuffing, ATO, scraping, and others. Because there are many fewer indicators that an API call is malicious than a standard browser request, APIs are also more difficult to protect against using conventional techniques.

More precisely, when using API attacks, bots make the same information requests they would through a browser attack, but they omit information on the device type, cookies, browser agent, or version, as well as other details that might help detect bot attacks.

 Since API attacks are typically fully virtual, it is simple to spin them up, spin them down, and relocate them from one cloud provider to another while using a changing set of IP addresses and proxy networks to conceal their identity. Due to these factors, the resources needed to launch API attacks are also far lower than those needed to launch browser assaults.

Common browser bot assaults employ “headless” browsers, which can run JavaScript and are command-line-executed, to imitate human behaviour. APIs enable attackers to exploit generally accessible, fundamental, and less costly features since headless browsers are often more expensive to use in assaults.

APIs frequently provide attackers with more direct access to the foundational components of a programme. It typically signifies that the attacker is one step away from gaining access to highly important assets when an e-commerce firm employs a uniform API to offer pricing information or log-in credentials across online and mobile applications.

The result? API attacks can be far more difficult to detect and are easier to mount with fewer resources.

How to Stop API Attacks?

Unfortunately, real-time API attack blocking cannot be accomplished using conventional techniques for preventing online assaults. Web Application Firewalls (WAFs) employ static techniques such as rate-limiting API calls, denying requests from unknown protocols and searching for attack signatures. WAFs frequently choose between permitting malicious traffic and blocking legal traffic. Modern WAFs and signature-based detection techniques are readily avoided by newer API bots. You need a novel defensive approach that relies on machine learning, complex behaviour modelling, and a continuous real-time feedback loop to defeat API bots. It is referred to as “Collect, Detect, Mitigate, Learn.”

• Build the models based on the signals collected.

To detect API bot actions in runtime, the first step is to gather behavioural, network, and other fingerprints from regular users as a baseline. These include cues from the actions of actual users, information got from their Web API activity, cookie analysis (and its absence), and cues from mobile apps like mobile IDs and application tokens.

You must also search for network signals, such as network response times and patterns, network fingerprinting, and proof of obfuscation methods, such as the use of proxy networks, when searching for direct APIs. To determine the possibility that a call is originating from a trustworthy person or trustworthy bot rather than a dangerous bot, these signals should be used in conjunction with internal and external reputation feeds. Finally, you must incorporate feedback loops that are application specific.

Building solid models of the types of API traffic that are good, poor, and unknown can be done using all of this data. These models must be adaptable and able to include data in real-time in order to stop dynamic and ever-evolving API assaults.

• Analyse API request signals to detect bots

The model continually processes the signals emitted by each API request to identify fraudulent API bots. Advanced machine learning and behavioural analytics designed to react in real time and at web scale will be required. Each API call will receive a risk score from the detection model after it continuously compares behaviours and signals to those of real users. This enables security teams and website and application administrators to detect abnormalities and produce precise confidence intervals for API calls.

• Mitigate Bad Bots Instantly

Your system should stop a malicious request when it is identified at a high confidence interval before the request reaches the API and extracts any data from it and the choice must be made in milliseconds.

Additionally, you may take actions to access the API for additional data. For instance, “honey pots” can display information that is obscure to average consumers. Only malicious APIs would be able to access them and view them.

• Learn Continuously, Update Constantly

You must continually update models of what undesirable API behaviour looks like for this process to be effective. The accuracy and identification of bots can only be improved over time in this way. Only dynamic models that include data in real-time and update the model to account for each new result can accomplish this. This is the area of continuous machine learning systems, which, until a few years ago, required too much computer power and were challenging to implement as real-time feedback loops.

Conclusion

Protecting online apps will demand considerably more agility and speed than what conventional security measures can provide as API threats develop and adapt at an accelerated rate. Additionally, a far more dynamic model with continuous learning is needed to accurately detect and prevent API attacks before they occur. Machine learning and a flexible, adaptive technique that can handle real-time detection and mitigation without consumers even noticing are the only ways to accomplish this efficiently.