Big Data Security Analytics: Et våben mod stigende cybersikkerhedsangreb?

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With the open-end digitalization of the business world, the risks of cyber attack to the companies have been heightened. These attacks can, however, be avoided by big data analysis. BARC’s ‘Big Data and Information Security’ research comprise of a deep analysis of the current deployment levels as well as the advantages of analytical security solutions by big data along with the challenges faced by them.


Increment in Cyber Security Threats

Information security made a drastic paradigm shift from the long-established perimeter protection tools towards monitoring and tracking down malicious activities within the corporate network. This is because the application of corporate security perimeter has long been disappeared mainly because of the increasing adoption of cloud and mobile services.

But there stands a reason for the withdrawal of traditional approaches to information security, and that is solely because of the massive rise in cyber attacks as well as the part played by nefarious insiders in causing large-scale security breaches.

Companies have to Amend Their Cyber Security Notion

Analytics work as an essential feature for making cyber resilience advantageous. In view of all the advancement and sophistication of cyber attacks, companies need to revise their cyber security policies and take a step further from absolute prevention towards the PDR concept – Prevent > Detect > Respond.

A cyber attacker needs a single successful attempt to gain access and cause havoc in an organization thus the need of amending one’s cyber security notion has spiked remarkably.

What Role does Big Data Analytics Play Here

Since improved detection is the integral element in this approach, so this is where big data analytics play its role. Detection must be swift and reliable enough to discriminate in the patterns of varying uses, to implement rapid analysis with close proximity to real time, and to sail through complex correlations obtained from a wide range of data coming from servers and application logs as well as from user activities and network events.

Thus such intricated analysis requires state-of-the-art analytical measures beyond the bounds of the usual rule based measures. Improved detected also requires the proficiency to run analysis on huge amounts of current as well as archival data. Therefore, we can say that this is where big data analytics hold its key importance. The duo of security and the current state of analytics helps to determine and boost cyber resilience.

Big Data Security Analytics: A New Generation of Security Tools

In the recent years, a new generation of security analytical tools has emerged with the security industry’s double-take to these challenges. These security analytical tools can collect, store, and analyze a large amount of data across the whole organization in actual time.

After the data has been augmented with additional context data as well as extrinsic threat intelligence, it is then analyzed via different correlation algorithms so as to detect deviations and recognize possible malicious activities.

These security analytical tools are quite different from the usual SIEM solutions and are likely to perform their tasks with proximity to real time thus they are capable of generating security alerts ranked by severity with respect to a risk prototype. Furthermore, these security alerts also include additional forensic details and allow quick detection and alleviation of cyber attacks.

How did Big Data Security Analytics Originate

Big data analytics is the reason of the biggest technological breakthrough.

The security industry has reached the peak which commodifies business intelligence algorithm for big-scale data processing, which was at first only available to large organizations only. Vendors can now build big data solutions which are able to collect, store, and analyze great amounts of data in real-time, by using the easily available Apache Hadoop and cheap hardware.

Integrating Data to Predict Malicious Activity

This generates the possibility to combine real-time and historical analysis as well as to determine new incidents that could be related to the ones already happened in the past.

The growing cyber attacks can be identified with much more efficiency, once the big data security analytics is combined with extrinsic security intelligence sources that are responsible for providing current information regarding the latest vulnerabilities.

The archival data can prominently simplify calibration to the normal order of activity of a given network, which can then be utilized to detect deviations. Existing solutions can automate calibration with minimum efforts required from the administrators.

Identifying Significant Incidents

The big data analytical algorithms can identify deviations and anomalies in the data which mostly indicates malicious activity or at the minimum some kind of suspicious activity.

The high-volumes of security data once filtered by big data security analytics can reduce the enormous flows of untreated security events to a controlled number of brief and categorized alerts. However, the archival data kept for later analysis can provide a forensic expert with details regarding the incident and also about its relationship to other previous anomalies of the past.

Automating Workflows

At the end, big data security analytics solutions supply various automated workflows for countering detected threats which might include eradicating identified malware attacks or submitting a dubious event to a managed security service for in-depth analysis.

The main elements for flourishing business in the future next are considered to be the automated controls for cyber security and detection of fraud.

Key Findings from the Big Data Security Analytics Report

The research provides an in-depth analysis of the level of awareness and the recent approaches in the field of information security as well as fraud detection in companies spread worldwide.

It describes the importance, future plans, and current state of big data security analytics and its dynamic actions about to be initiated across various sectors. The research also provides an overview of the different opportunities, advantages, and challenges with respect to the dynamic initiatives. Furthermore, it also provides an audit of the range of technologies currently available to focus on those challenges.