Why use machine learning in IoT security strategy

Why use machine learning in IoT security strategy

IT teams struggle to develop adequate security strategies with the multitude of devices on ever-expanding corporate networks. Protecting IoT investments is critical to business survival and growth, but IoT security presents unique challenges.

A machine learning (ML) approach to IoT security can address some of these challenges. It solves the problem of identifying unknown devices on a network, ensuring they are included in the existing security framework, and makes IoT management easier for busy IT teams.

Machine learning in IoT security

IoT devices are often the weakest link in a corporate network, but they are infinitely useful for a business. Add in its scalability, and it’s not hard to see why companies continue to expand its use. Cybersecurity teams need additional technology to keep track of all devices and keep the network secure.

On a general level, ML can protect IoT by automating the scanning and management of IoT devices across the network. They can scan every device on the network, shutting down attacks automatically before IT teams know it. That’s what happened in 2018 with Microsoft’s Windows Defender software when shut down a trojan malware attack in 30 minutes.

Looking deeper, ML helps identify all devices on a network, including those that only connect intermittently. You can automate the implementation of a network segmentation strategy by automatically adding devices to the appropriate segment based on pre-established rules. IT teams are freed up to work on more valuable technology projects and manage the company’s overall cybersecurity strategy faster and more efficiently.

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Detailed look at machine learning in IoT security

ML helps IoT security teams make intelligent predictions and responses based on past behavior. For known vulnerabilities and attacks, such as distributed denial of service, compare current network behavior with behavior patterns from attack examples and take protective action.

Services such as AWS IoT Device Defender, Extreme Networks solutions, or Microsoft’s Azure Security Center for IoT offer ML capabilities for IoT security, including device-level anomaly detection and automated response to threats.

In Microsoft’s Windows Defender example, cloud-based and client-side ML systems automatically compare current network usage against 30 parallel security protection models. Some of those models use millions of factors to determine what is the positive or negative behavior of known attacks.

To protect against unknown vulnerabilities and zero-day attacks, ML models monitor IoT devices and network activity to detect unusual behavior in real time and take immediate protective action. Many ML systems are automatically updated daily to keep up with the changing threat landscape, making ML ideal for protecting complex networks. Instantly review the large digital footprint of an IoT fleet and compare fleet behavior to known threats and historical behavior. Only a network using ML systems can act so quickly to detect threats before they break into the core corporate network via IoT devices.

Advantages of machine learning in IoT security

ML’s main advantage in IoT security is the speed with which it scans, detects, and protects devices and networks. You can bring modern security models and frameworks to all networks, including those still using legacy technologies and IoT devices. Here’s a closer look at two advantages of ML.

Find and identify all IoT devices on a network

Given how expansive and complex an IoT fleet can be, IT teams may not be aware of all the IoT devices currently on their network, especially those that connect intermittently or use legacy protocols to send or receive data. They are “hidden” from a security perspective until activated or targeted by an attack.

ML can identify IoT devices on a network because it automatically scans and compares the historical behavior of the network. For example, an ML model can detect a potential hidden device if it knows that network traffic is increasing at a particular location on a particular day each month. IT administrators can then send a team to physically verify the location to verify the device and incorporate it into future security plans.

More efficiently add IoT devices to network segments

Creating network segments is only part of the task; IT teams must add devices to segments for them to work properly. That’s a challenge with the sheer number of IoT devices on a network. Combining ML with network segmentation makes it easier and more efficient.

Teams can configure segments and edge device rules to get started, and ML models will then automatically monitor, scan, and protect devices accordingly. As devices come online, ML systems automatically place them in the appropriate security group based on those rules. This frees up IT staff to work on more valuable technology activities and strategies, while keeping security relevant and up-to-date for IoT devices.

ML’s main advantage in IoT security is the speed with which it scans, detects, and protects devices and networks.

Disadvantages of ML in IoT security

ML can identify and even communicate with legacy IoT devices; however, if they are too old or out of date, they are vulnerable to attack. The ML system must be configured to identify legacy devices and then alert IT administrators when the devices no longer connect. Otherwise, devices may simply become an entry in a “previously connected” report that might not be detected in time to prevent an attack.

Similarly, the variety of IoT devices in a fleet can make it difficult for ML to stay current. Depending on the ML service used, the model may update its device compatibility lists on a schedule that does not coincide with the ever-changing threat landscape. The devices that ML can scan and protect today may not be the same tomorrow. ML is only as good as the security systems and models that support it.

Many IoT devices require ultra-reliable, low-latency communication, such as sensitive surgical devices, assembly line production systems, and traffic monitoring systems. Organizations or individuals typically use these devices 24/7, which means that ML protocols cannot be configured to run outside of business hours; there are none. An ML-initiated scanning or monitoring protocol can consume precious bandwidth for these devices, making them too slow or even inoperable during that time. IT teams need to understand the role and use of IoT devices when implementing their ML strategy. Typical setup may not work.

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