Predictive Analytics and Machine Learning and its role in Cybersecurity

October 2, 2019

Cybersecurity experts are constantly exploring techniques to manage new and emerging security threats. Despite a number of new and sophisticated tools, the IT security industry is well aware that a data breach can occur at any time.

Security solutions utilising machine learning have become a popular option for cybersecurity, reducing time spent on detecting potential threats, yet security continues to be a considerable concern. Some industry specialists are suggesting that predictive analytics could be an alternative option to tackle the challenges facing cybersecurity.

Predictive analytics is a rapidly evolving technique that enables businesses to understand what could happen and use insights that previously were not available. This tool is emerging within cybersecurity and allowing to predict potential cyber-attacks, enabling them to prepare the required systems to protect their business against attacks.

Many cybersecurity specialists are combining these systems with machine learning into their core security offerings. The question many experts are asking is how specifically predictive analytics can help in supporting potential cyber-attacks.


How Predictive Analytics can support Cybersecurity

Businesses today need to be capable of analysing data, identifying trends and errors as quickly as possible. Using predictive analytics enables businesses to identify incidents and find patterns to suggest what has worked and what hasn’t with their business. Whenever something out of the ordinary occurs, analysts can quickly step in and assess the scenario. Real-time data can be generated by using predictive analytics, identifying common attack scenarios and techniques to defend against these cases in advance.

Security teams are facing a number of challenges managing large volumes of data. Measuring and understanding big data is a complex task, particularly as big data sources can come from a number of varying databases and systems. Before any of this can be measured, the data needs to be collected and parsed, and this is where businesses need a system to enable this all to work together. Luckily, predictive analytics solutions are well suited to big data. In reality, the higher the number of inputs available, the more insights that can be generated providing an accurate number of predictions.

Combining predictive analytics with machine learning can support analysts in gaining important insights related to potential threats. Machine Learning can reduce the pressure on analysts in other tasks of categorising information and filtering through data streams. ML will also reduce human error that is inevitable due to the large volume of information required for processing. IN reality, predictive analytics combined with ML can only really work with established big data streams. 

An active defense system requires blocking any potential threat at source. Businesses need to deliver a data source into their predictive analytics solution. Launching a domain reputation tool can support potential malicious attacks. Through this application, predictive solutions can be introduced to determine the reputation of domains and the associated site. An API supported by a constantly updated database assures customers that they are gathering accurate domain details.

Predictive Analytics is being viewed as the next big thing within cybersecurity. Businesses that want to stop unknown threats from impacting their business should strongly consider the implementation of predictive analytical systems.


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