Three Major Stumbling Blocks to Employing ML for Cybersecurity


Today, Machine Learning (ML) is a commonly used word in almost every area of IT. Additionally, ML has proven invaluable in a variety of applications, including cybersecurity. It is frequently used to make sense of big data, enhance business performance and processes, and aid in prediction.

Complexity is the driving force behind the demand for Machine Learning (ML). Many organizations now own an increasing number of Internet of Things (IoT) devices that IT is not aware of or managing. Since hybrid and multi-cloud are the new standards, not all data and applications are running locally. Due to the widespread acceptance of remote work, users are no longer primarily in offices.

ML is well-understood and frequently used in a variety of contexts. The most common ones are Natural Language Processing (NLP), which helps to understand what a person or a piece of text is saying, and image processing for object recognition.

In several ways, cybersecurity is unique from other ML use cases. Utilizing machine learning for cybersecurity has its own requirements and obstacles. The following three issues will make it difficult to use ML in cybersecurity.

Also Read: How Enterprises Can Address Cyber Risks in Business Applications

ML security

Due to the crucial role that cybersecurity plays in every industry, it is more important than ever for firms to ensure that the ML they use for cybersecurity is secure on its own. Machine learning aims to increase security’s scalability and efficiency in order to reduce human costs and stop unidentified attacks. Machine learning makes it simple to ramp up to billions of devices, which is challenging to do with manual labor. And that kind of scale is what companies actually need to protect themselves from the evolving threat environment. In many critical infrastructures, ML is essential for identifying anonymous attacks.

The substantially stricter accuracy standards

For instance, if a system misidentifies a dog as a cat while processing images for a business, it might be unpleasant but probably won’t have a life-or-death effect. The impact of the incorrect classification can be severe if a machine learning system mistakes a fraudulent data packet for a valid one, resulting in an attack against a hospital and its gadgets.

Organizations observe numerous data packets passing through firewalls every day. Businesses can mistakenly block enormous amounts of typical traffic even if only 0.1% of the data is misclassified by machine learning. This would have a detrimental consequence on the company. It makes sense that some firms were apprehensive at the beginning of machine learning that the models wouldn’t be as precise as human security researchers.

To train a machine learning model to achieve the same level of accuracy as a highly trained human requires a lot of time and data. But since they cannot grow, people are currently one of the most in-demand resources in the IT industry.

Also Read: How Enterprises can strengthen their Security Architecture with XDR

Firms depend on ML to scale out cybersecurity solutions effectively. Additionally, because ML can establish baseline behaviors and identify any irregularities that depart from them, it can assist in detecting unidentified attacks that are challenging for humans to identify.

Talent shortage

For ML to be effective in any industry, enterprises must combine domain expertise with ML knowledge. It is difficult to locate professionals who are knowledgeable in both machine learning and security; rather, either machine learning or security alone lacks skill. Firms have discovered that it is crucial to ensure that ML data scientists and security researchers collaborate, despite the fact that they don’t share a common language, employ distinct methodologies, and think and act in different ways. It is critical that they understand how to cooperate with one another. Applying machine learning to cybersecurity effectively depends on collaboration between these two groups.

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