User Behaviour Analytics - A Simple Guide

By Maria Hussain on 26th February, 2021
User Behavior Analytics (UBA) is the practice of using monitoring systems to track and collect data, followed by assessing and establishing the tendencies of an average user, in order to help companies and software owners know important details about their users. The two main areas where UBA is widely used today, are by companies looking to target their products to specific customers, and as a cybersecurity tool to single out potential threats and aberrations.
The first records of UBA come from the early 2000’s when it was in its most basic form, being used by marketing teams to predict the buying patterns of their customers and expand their consumer reach, and consequently their share in the market. Today, user behavior analytics is vastly used in e-commerce, social media and gaming, as well as by any organization that wants to protect their data- all in real time.
Some of the largest UBA vendors include Exabeam, Gurucul, Fortscale, Mixpanel, and Niara.
So how exactly does UBA work?
The workings of user behavior analytic systems are quite simple. They track and collect historical big data and merge it with current databases to curate a large amount of user-specific information. Then, they analyze and run the data through various algorithms to obtain repetitive patterns and tendencies, along with any anomalies or suspicious behavior; and provide all of it to the software/website owners in near-real time.
An example of this can be if user A clicked on product 1 on the homepage, and user B, C, and D did the exact same thing, they can come to the conclusion that product 1 is most likely going to be a best-seller. On the other hand, they can determine which user did what particular thing that they were not supposed to, for e.g. if user A performed multiple failed logins, the UBA system can classify this, in real-time, as someone pretending to be a user, trying to hack into their account.
Although user behavior analytics is the perfect tool to pinpoint patterns and aberrations from those patterns, there is not much it can do to fix them. Think of it as a well-trained dog barking at a stranger trying to break in; it will alert the owners of the user’s identity and location, but there has to be a separate system in place to actually prevent the threat from causing damage. The most UBA can do is lock out the threat or breach until the data is made secure again.
User behaviour analysis in sales and marketing:
Everyone sees advertisements regularly on all their social media platforms, mostly for products that relate to them. A lot of that is the work of user behavior analytic systems, who have noticed their inclination towards a certain style of marketing, or a brand, or the particular product or service they are looking for- and have provided that data to the sellers who match these criteria. In this way, sellers can know exactly which customer is most likely to buy which product, and can advertise accordingly.
On the other hand, it becomes a way for companies to receive feedback from their target audience without having to actively create surveys. UBA systems are able to know the pages opened, the time spent on each page, how many seconds of media consumed, the files accessed, emails sent and read, network activity, every click and every page closed. For sites like YouTube or Facebook, this data is extremely crucial to monitor inappropriate or spam activity; and for others like Amazon or e-commerce stores, this is an accurate and extensive report on what is working for their business and what might be driving their customers away.
How does user behaviour analysis provide cyber security?
UBA has evolved over time to become one of the leading sources of cybersecurity and fool-proof protection. For every person logging onto a particular website or application, the UBA system will create a log that tracks activity, permissions, peer interactions, etc. This way, they will have a pretty good picture of what the user is doing on their site.
Say, a hacker was trying to break through the firewall, or to log in with someone else’s account, or operating a user account in ways completely different from the account’s normal activity, the UBA system in place will send out alerts to notify the cybersecurity team; along with the name, location and other credentials of the malicious user. Not only does this allow the company to stop breaches in near-real time, it also provides a framework of each step the user took before the attack could happen. In the best case outcome, the company will be able to prevent security attacks before they even occur, by making sure that kind of activity is stopped from taking place.
It should be noted that UBA is truly remarkable against insider threats, like data theft brought about by an employee who was granted access to valuable data and information by the company itself. In these cases, the system may forewarn the company that this employee has been, for instance, accessing this data for longer than normal durations and at odd hours; and from there, the essential steps can be taken to assure that the potential damage is minimized.
The 4 main cybersecurity threats that user behavior analysis has proven valuable against, are as follows:
‣ Compromised user
‣ Compromised host or server
‣ Data theft
‣ Insider threats
User behaviour analytics and information event management (SIEM)
Most of the time, UBA and SIEM systems work together and can even interact with each other to create an almost invincible level of security. With SIEM focusing more on what is expected to happen in a breach, and UBA being centered on anything that should not happen, but is taking place; a company is able to cover more ground, and effectively dodge all kinds of cybersecurity attacks.
How does uba differentiate between normal errors and potential danger?
If the User Behavior Analysis tool was to notify and alarm the cybersecurity team on every little marker of error, it would be a giant mess with millions of false alarms everyday that will not only trouble the company and drown out actual threats, but also be an inconvenience for users who just made a human mistake.
To counter this, UBA has the ability to judge each deviation or error, in terms of how potentially dangerous it can be, and not provide false positives. This levelling of threats is obtained by giving the system as much data as possible, including third-party data, to study at length and create thousands of contingencies and their outcomes. Keep in mind that the UBA system is as good as the data fed to it. In the end, it is able to notify the company only in times of actual crises.
To conclude, it is safe to say that User Behavior Analysis is the future of cybersecurity, with its handling of big data, exceptional machine learning techniques, and the ever-expanding research and technology being utilized for its improvement.
For more interesting articles like these, keep following Alfabolt online!