4 advanced IoT security best practices to strengthen your defense

With more specialized security practices, cybersecurity experts can strengthen their IoT deployment defenses and gain advantages in the scalability, adaptability, and easier management of IoT devices.

Security measures such as cryptography, segmentation, and machine learning (ML) algorithms focused on threat detection can complicate security strategies, but they offer additional layers of protection for IoT deployments.

The IoT presents particular security challenges due to the lack of built-in security in many devices and the large attack surface. The number and geographic distribution of deployed IoT devices makes it easy for security teams to ignore new devices added to the network. Data from edge sensors is transferred over the network to gateways, centralized servers or the cloud, giving attackers more access points. Data storage and power constraints make it even more difficult to release live updates to fix vulnerabilities.

IT administrators can take their IoT protection to the next level with these four IoT security best practices.

1. Zero confidence

The zero-trust security model, when applied to IoT, means that the network will not automatically trust any device or user who tries to connect to it. Each device or user must have their identity verified and authenticated each time they log in. Traditional security models establish users and devices that the network automatically trusts every time they log on after the initial verification.

IoT devices are tempting targets for attackers. With a traditional trust model, a hacked IoT device can easily lead to full network access. Zero trust policies provide an additional measure of protection against breaches through IoT devices and are better suited to complex and growing IoT deployments.

For zero trust models to work in IoT deployments, security teams need to know all the devices on their network. Teams should review and revise the security model with changes to the network to avoid new vulnerabilities.

2. Machine learning

IT administrators may find it impossible to manage IoT without automation due to the massive number of IoT devices often in a deployment. Organizations can implement ML to monitor these devices and detect security threats.

When security experts apply ML to device management, they can automate device discovery and identification to ensure that every device is included in their security policy. ML also facilitates the deployment of security measures, such as the deployment of network segmentation based on established rules.

During attacks on the network, speed is critical. ML can give businesses the edge they need to stop breaches before they can spread through patterns of known and unknown vulnerabilities. The automated analysis and threat detection models compare the behavior of the known network to the models of known attacks and stop. Sometimes models can take protective action before security teams are aware of an attack, such as unknown threats. ML models monitor network behavior for any unusual activity.

3. Cryptography

While IT administrators are familiar with data encryption as a good security practice, cryptography for the IoT can get complicated. Cryptography protects the communication channels with an encoding to make them indecipherable by anyone except those who have the codes.

IoT devices are limited in resources, which means that some encryption and decryption methods will not work. IoT devices do not have the necessary processing and storage and may require lightweight encryption methods. Security professionals should know the limits of their IoT devices and the cryptographic protocols that will work for their deployment. Security teams can choose symmetric-key, public-key, or asymmetric-key encryption algorithms.

Not all organizations automatically implement cryptography as a security measure. Cryptography can block the visibility necessary for network analysis and troubleshooting. Cryptography also requires some expertise to manage and configure.

4. Segmentation

IoT device segmentation and microsegmentation divides networks into device groups with policies that limit device access to data and applications. Segmentation serves the same purpose as zero trust: to prevent the spread of attacks from moving across a network. However, segmentation offers the additional benefit of reducing network congestion.

Segmentation uses hardware to secure client-to-server traffic, while microsegmentation uses software to separate data streams from server-to-server traffic at the device level. With microsegmentation, IT admins don’t have to reassign policies when moving a device to a different segment.

Microsegmentation isolates devices and applications to prevent attackers or malware from spreading over a network.

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