Inasmuch as cyberattacks have consistently been on the increase, it behooves every partaker in the information security industry to constantly be on the knowhow of relevant tools and techniques that can be deployed to tackle the menace and that makes a case for machine learning-based UBA. Your principal duty as an information security professional, is to understand what’s going on in your environment and this will lead to sinking huge amounts of resources and tools that may include log management, security information and event management (SIEM), and even security operational intelligence.
While all these measures may not have been adequate in making you actually comprehend what’s happening, security user behavioral analytics (UBA) has come to play the role. User behavior analytics is basically employing monitoring systems to track, collect, and assess user data and activities.
You can deploy these technologies for the analysis of historical data logs which can include network and authentication logs collected and stored in log management and SIEM systems. This will enable you to identify patterns of traffic caused by user behaviors, that could be normal or malicious.
The essence of UBA systems is fundamentally intended to provide cybersecurity teams with actionable insights. Understandably, the systems are not meant to take action based on their findings, however, they can be configured to automatically resolve the difficulty of authenticating users who exhibit atypical behavior.
What’s user behavioral analytics?
Since InfoSec professionals are at their wits’ end on how to tackle the problems that confront them in security architectures, it has become imperative that you must seek out means to uncover information that truly indicates the potential for a real attack. You can, however, make do with analytics tools to enhance making sense of the large volume of data that SIEM, IDS/IPS, system logs, and other tools accumulate.
The working of user behavioral analytics tools is dependent on the use of a specialized type of security analytics that focuses on the behavior of systems and individuals using them. This is made possible by spotlighting cases in which abnormal behavior is underway.
As the behavior may or may not portend a problem, it’s left for InfoSec pros to investigate and come up with a resolution. UBA primarily focuses on users as compared with other forms of security analytics that may take into cognition events or alerts.
While this difference may seem inconsequential, it’s highly important. It’s very possible for an event to make sense at a particular point in time but nefarious in another. A case in point could be that of an employee who has authentic access to a system but as a result of disengagement or reassignment at midnight on April 14 becomes abnormal on June 12.
Deploying user behavior analytics
Upon the deployment of UBA, you have positioned your brand to be able to collect various types of data, such as user roles and titles, including access, accounts, and permissions; your users’ activities and geographical locations; as well as security alerts. The various data you need can be collated from your users’ past and current activities.
Your analysis should be based on resources used, duration of sessions, degree of connectivity, and peer group activity to compare to anomalous behavior. Updates should occur automatically at any time a new alteration is made on the data, which could be promotions or new permissions.
The working of UBA systems does not revolve around reporting all security aberrations as being risky, rather you will be promptly informed of any behavior’s potential impact. When you have instances of behavior involving less-sensitive resources, your user behavioral analytics system receives a low impact score.
You will, on the other hand, receive a higher impact score if it involves more sensitive resources, like personally identifiable information. This enables your information security personnel to place more priority on what to focus on while the UBA system automatically restricts or increases the difficulty of authentication for the user that has anomalous behavior tendencies.
Where machine learning algorithms come in to play here is to enable your UBA systems to reduce false positives and ensure you have clearer and more accurate actionable risk intelligence for you, cybersecurity teams, to work with.
Machine learning in user behavioral analytics
Marketing teams are known to have come up with behavior analysis systems as tools that were meant to help marketing teams analyze and predict customer buying patterns. However, technological advancements have transformed user behavior analytics tools to have more advanced profiling and exception monitoring capabilities than SIEM systems.
They have, therefore, found uses in two main areas. The very first functional role of UBA tools is being used to ascertain a baseline of normal activities peculiar to your organization and its individual users, while the second, is to recognize departures from normal.
UBA uses big data and machine learning algorithms to appraise these aberrations in near-real-time. You should, however, not contemplate on using user behavior analytics for one user as this may not be useful for finding malicious activity, you should rather deploy it on a large scale to detect malware or other potential cybersecurity threats, such as data exfiltration, internal threats, and compromised endpoints.
Upgrading UBA to UEBA
According to the analyst firm Gartner in a market guide that was published in 2015, it was revealed that the user and entity behavior analytics (UEBA) technologies have not just the same features as the conventional UBA but also come with some supplementary peculiarities of tracking the user activity, as well as the activities of devices, applications, servers, and data. What this boils down to is that you will have the capabilities of not only analyzing your user behavior data but you can also combine user behavior data with behavior data from entities.
Where UBA is designed to track insider threats, UEBA has the added advantage of using machine learning to detect for all types of anomalies that could represent threats. You can even deploy UEBA in conjunction with SIEM technologies for better analysis and the eventual comprehension of the information gathered.
User behavioral analytics tools may not presently be well-positioned to take defensive action themselves as they are only still providing security operators with the insight to determine whether any action is needed, it’s, however, expected that in no distance time we shall have tools integrated with firewalls, routers, VPN, and other defensive systems to enable automated response.
A parting note
While it’s a worthwhile venture to gather data, it’s highly imperative that you invest in tools that make sense of that data and that can find those tell-tale signs that you are on the verge of vulnerabilities. UBA tools will promptly and effectively raise red flags concerning questionable behavior, especially when machine learning has been integrated about users, systems, and devices.
The final outcome is that InfoSec professionals will have a much-needed direction in determining if a breach has occurred requiring urgent attention.
