Over the years, data operations techniques have become more closely linked with cyber security strategies. One of the key techniques that take the best of both worlds is cyber security analytics.
Cyber security analytics solutions, such as UEBA, are a modern and extremely robust way for businesses to strengthen their cyber landscape. It enables security teams to be more cyber aware, more proactive, and gives them sight over all user activity.
Ultimately, UEBA empowers businesses to put advanced threat detection at the heart of both their data operations and cyber security practice.
What is cyber security analytics?
Cyber security analytics involves the process of collating, organising and analysing all relevant data for creating a cyber security strategy. It empowers security teams to be more proactive, and easily identify any cyber threats based on historical and current data.
What is UEBA?
UEBA is a common cyber security technique or solution – it stands for User and Entity Behaviour Analytics.
This approach to analytics focuses on creating a comprehensive picture of what a system, individual user, or device is doing on the company network. It involves tracking and logging cyber activity on a day-to-day basis to establish patterns of normal behaviour.
Some key metrics UEBA may record include:
- Whether a user, entity or device has access to certain assets
- When and from where, geographically or by device, a user or entity logs in
- An IP address’s activity and behaviour
Based on these key metrics and many others, UEBA security solutions and analysts will perform continual analysis to detect potential attacks.
How does user behaviour analytics work?
The way user and entity behaviour analytics solutions detect potential threats is by identifying abnormal behaviour.
All information a user or entity produces when on the company network is collected and plugged into a threat model. Based on this historical data, the model can establish a base line of activity and resultantly identify when activity is out of the ordinary.
Once the model has found any abnormalities, it will conduct an analysis to uncover more details and intelligence surrounding the event. For example, if it identifies unusual log in behaviour, the model will then gather surrounding data such as geography and time.
UEBA will automatically send an alert outlining the details of the unusual behaviour to the business’s security team, identifying it as a potential threat that needs to be evaluated.
How does UEBA utilise machine learning?
User and entity behaviour analytics procedures are underpinned by machine learning capabilities.
Utilising machine learning, UEBA collects all the information from all available systems. This technology can perform more intelligent analysis of massive amounts of data to detect patterns, relationships, and overall trends.
The machine learning functionality then creates an algorithm that represents how a user or entity has been accessing devices, using a network, and their overall patterns of normal activity. Once this is established, UEBA can now predict how a user likely act.
It’s when the user deviates from this established pattern that the security team will be alerted ‘suspicious’ or unusual behaviour.
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Why is using big data analytics for cyber security important?
Big data analytics is also employed as part of UEBA cyber security tools. Essentially, it’s the only way to collect and handle the vast amount of data needed to inform UEBA practices. Normal databases just don’t have the processing power.
Big data analytics and machine learning are then used in tandem. Once the big data gathers all the relevant information, it is plugged into machine learning algorithms to create the model that informs further investigation.
How is UEBA used to improve cyber threat detection?
In the past, threat detection and resultant preventative or defensive action was performed using only historical data. But with UEBA, most data is collected in real-time – historical data is only used to create a base line.
This means any new, unusual information or potential threat will be detected almost instantly. Empowered by these modern solutions security teams and analysts can conduct more proactive threat detection. Teams can work out potential attack patterns in advance, or identify threats before they have major impacts, and take immediate and informed action.
Implementing these more proactive approaches to cyber security analytics will help businesses mitigate attacks before they cause damage, and remain more cyber secure.
What are the benefits of UEBA?
A proactive approach to cyber security threats is one of the main benefits of UEBA. It improves response time and reduces the overall risk of attack, which is why many modern businesses are making proactive cyber strategies a priority.
But there are various other key benefits to implementing UEBA solutions:
- Machine learning means UEBA solutions are always getting more accurate
- False positives are reduced
- Can help evaluate whether an abnormality is malicious
- Helps analysts prioritise more critical attacks
- Detects real-time, forceful attacks
- Alerts users to changes in policies, permissions or user access
- Identifies insider attacks as well as external ones
- Alerts to behaviour without a business cause
How RiverSafe can help improve your threat detection
RiverSafe work with companies like Splunk and Exabeam to provide our clients with robust UEBA solutions. Our experts conduct a thorough audit of your digital landscape and assets, to assess where cyber-attacks will most likely arise and how best to detect and protect against them.
This in-depth analysis process enables us to provide more informed and tailored security consultations, helping you maximise your security operations, investigation and response capabilities.