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 cyber security. This article will share reasons why ML has risen to such importance in cyber security, 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 Cyber Security?
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 cyber security: 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.
What Makes Machine Learning Different in Cyber Security?
Machine learning is well understood and widely deployed across many areas. Among the most popular are image processing for recognition and natural language processing (NLP) to help understand what a human or a piece of text is saying.
Cyber security is different from other use cases for machine learning in some respects.
Leveraging machine learning in cyber security carries its own challenges and requirements. We will discuss three unique challenges for applying ML to cyber security and three common but more severe challenges in cyber security.
How Machine Learning Enables the Future of Cyber security?
Machine learning supports modern cyber security solutions in a number of different ways. Individually, each one is valuable, and together they are game-changing for maintaining a strong security posture in a dynamic threat landscape.
Identification and profiling: With new devices getting connected to enterprise networks all the time, it’s not easy for an IT organization to be aware of them all. Machine learning can be used to identify and profile devices on a network. That profile can determine the different features and behaviors of a given device.
Automated anomaly detection: Using machine learning to rapidly identify known bad behaviors is a great use case for security. After first profiling devices and understanding regular activities, machine learning knows what’s normal and what’s not.
Zero-day detection: With traditional security, a bad action has to be seen at least once for it to be identified as a bad action. That’s the way that legacy signature-based malware detection works. Machine learning can intelligently identify previously unknown forms of malware and attacks to help protect organizations from potential zero-day attacks.
Insights at scale: With data and application in many different locations, being able to identify trends across large volumes of devices is just not humanly possible. Machine learning can do what humans cannot, enabling automation for insights at scale.
Policy recommendations: The process of building security policies is often a very manual effort that has no shortage of challenges. With an understanding of what devices are present and what is normal behavior, machine learning can help to provide policy recommendations for security devices, including firewalls. Instead of having to manually navigate around different conflicting access control lists for different devices and network segments, machine learning can make specific recommendations that work in an automated approach.
With more devices and threats coming online every day, and human security resources in scarce supply, only machine learning can sort complicated situations and scenarios at scale to enable organizations to meet the challenge of cyber security now and in the years to come.