The Future of Machine Learning in Cybersecurity

Why has machine learning become so vital in cybersecurity? This article answers that and explores several challenges that are inherent when applying machine learning.

Machine learning (ML) is a commonly used term across nearly every sector of IT today. And while ML has frequently been used to make sense of big data—to improve business performance and processes and help make predictions—it has also proven priceless in other applications, including cybersecurity. This article will share reasons why ML has risen to such importance in cybersecurity, share some of the challenges of this particular application of the technology and describe the future that machine learning enables.

Why Machine Learning Has Become Vital for Cybersecurity

The need for machine learning has to do with complexity. Many organizations today possess a growing number of Internet of Things (IoT) devices that aren’t all known or managed by IT. All data and applications aren’t running on-premises, as hybrid and multicloud are the new normal. Users are no longer mostly in the office, as remote work is widely accepted.

Not all that long ago, it was common for enterprises to rely on signature-based detection for malware, static firewall rules for network traffic and access control lists (ACLs) to define security policies. In a world with more devices, in more places than ever, the old ways of detecting potential security risks fail to keep up with the scale, scope and complexity.

Machine learning is all about training models to learn automatically from large amounts of data, and from the learning, a system can then identify trends, spot anomalies, make recommendations and ultimately execute actions. In order to address all the new security challenges that organizations face, there is a clear need for machine learning. Only machine learning can address the increasing number of challenges in cybersecurity: scaling up security solutions, detecting unknown attacks and detecting advanced attacks, including polymorphic malware. Advanced malware can change forms to evade detection, and using a traditional signature-based approach makes it very difficult to detect such advanced attacks. ML turns out to be the best solution to combat it.

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