How Machine Learning contributes to better cybersecurity

3 min read

By stringing a variety of data together and analyzing them, Machine Learning helps companies to get the big picture of their cyber risks. It thus enables them to mitigate risks and respond decisively in the event of a threat.

As our world continues to evolve and transform, one of the more prominent changes we see is the rapid digitalization of processes and operations. From e-commerce shops to virtual events, many businesses are slowly shifting to the online landscape. This is no surprise considering the convenience, accessibility and affordability and efficiency that digitalization brings. Currently, the COVID-19 pandemic also serves as a catalyst in the accelerated growth and adoption of digital solutions due to people working from home and accessing the corporate network from remote.

Though digitalization brings with it many benefits, it also leads to a rise of cybersecurity threats. Personal information and sensitive materials are more likely to be compromised online than off. Therefore, it becomes crucial especially for businesses to collaborate with a cybersecurity company to safeguard business assets and private data. One of the techniques used to improve cybersecurity is through machine learning services.

Machine Learning: an Introduction

Today's machine learning is not the same as what it used to be because of new computing technologies. The idea behind machine learning is that systems can learn from data, identify patterns, and make decisions with little to no intervention from humans. When models are exposed to new data, they can independently adapt, because machine learning is an iterative process. Machine learning produces repeatable, reliable outcomes by learning from previous computations.

Contributing to Better Cybersecurity

Through analyzing patterns, learning from them to prevent similar attacks and responding to changing behavior, machine learning services in cybersecurity systems empower greater proactivity in preventing threats and responding to real-time attacks. It also helps organizations save time and resources by reducing the time spent on routine tasks. How these are possible is described further below.

Data Collection, Organization, and Structuring

An effective cybersecurity solution doesn’t just assess and analyze threats. They should collect, organize and structure information revolving around everything that happens, providing comprehensive details about the machines, applications, protocols, network sensors and more. Machine learning services assist with that. By stringing a variety of data together, organizations are able to better perceive situations with a big picture in mind. From there, the ability to construct different models and assess different behavioral elements is made possible. Algorithms can then be better leveraged to make decisions with regards to issuing alerts and responding to potential threats.

Classifying and Detecting Threats

Machine learning services and algorithms are especially useful when it comes to threat classification and detection. The goal of this function is to identify malicious patterns of behavior using big data sets of security events. When similar events are detected, the trained machine learning model is able to handle them automatically. Indicators of Compromise (IOCs), which help detect cybersecurity threats including data breaches and malware infections can be used to create the dataset for feeding a machine learning model. As such, malware behavior can be classified using machine learning algorithms based on IOC data to improve cybersecurity.

Task Automation

Automating repetitive and time-consuming tasks including triaging intelligence, malware analysis, log analysis and vulnerability assessments is an important benefit of cybersecurity that machine learning services provide. This enables a more seamless and efficient security workflow process and allows organizations to detect and remediate threats faster than they could do manually. Therefore, analysts, data scientists and developers are able to focus on other more urgent tasks that require their attention while experiencing overall increased productivity. In addition, when repetitive processes are automated, it allows clients to scale up or down without having to change manpower, which boosts greater cost-effectiveness as well.

Prediction of Changing Threats and Vulnerabilities

The tactics and tools of cybercriminals change constantly. With thousands of new threat variants occurring daily, it is almost impossible for humans to keep up with this speed of change. Machine learning services can help cybersecurity systems predict threats instead of simply preparing to respond to recent attacks. This involves scanning data outside the organization to determine whether criminals are likely to target a certain organization based on its business type or geographic location.

While many new cybersecurity attacks use old attack codes or exploit operating system weaknesses, machine learning aids in prevention by spotting these issues and recommending upgrades before they are exploited.

As cybersecurity threats continue to rise, machine learning has progressed to become the primary detection method for identifying and stopping malware attacks. As such, one of the best ways to protect your business is to integrate cybersecurity solutions with machine learning capabilities.

Consult the Experts

As a leading cybersecurity company, Adnovum has a proven track record in delivering proven solutions built on emerging technology.

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Published December 2, 2021