When I first started talking to IoT network managers about adding virus filters, I didn’t fully grasp how different the challenge is from securing a traditional office network. For the past seven years, my team and I have built virus filtering solutions specifically for IoT environments, and I’ve sat through countless conversations where operators say, “We just need something like the antivirus we run on our PCs.” Every time, I have to explain that this is one of the biggest mistakes they can make—because IoT networks aren’t just smaller or simpler versions of what most of us know. They are collections of hundreds, sometimes thousands, of devices that weren’t designed with security in mind, and their needs often conflict with what a standard virus filter expects. Virus Filter
Let me start with the most obvious hurdle: the sheer variety of devices on an IoT network. Last year, we worked with a manufacturing plant that had 1,200 devices connected across their floor. There were temperature sensors in a storage warehouse, motor controllers on assembly lines, cameras for quality control, and even simple RFID readers for inventory. Each of these devices is built by a different manufacturer, using different operating systems—some run a stripped-down Linux kernel, others have proprietary firmware that hasn’t been updated in five years, and a few don’t have any operating system at all, just basic circuit board code. A standard virus filter is built for Windows, macOS, or Linux desktops and servers; it expects files of a certain size, uses common protocols like HTTP or SMB, and has the processing power to scan incoming data in real time. But when you’re scanning a 100-byte data packet from a temperature sensor that only sends a single number every 10 minutes, a standard filter will either flag legitimate traffic as malicious (a false positive) or be so resource-heavy that it crashes the sensor.
This brings us to the second big challenge: resource constraints. Most IoT devices are low-power, low-memory, and low-processing-power by design. They aren’t meant to run complex software; they’re meant to do one specific job reliably, for years, on a small battery. We once tried to test our filter on a group of smart meters that were less than three inches wide. When we deployed a standard version of our software, the meters’ batteries died three times faster than normal, and some of them stopped sending data entirely. The problem was that the filter was using too much of the device’s limited CPU and memory. We had to go back to the drawing board, build a lightweight version that only scans for the specific threats that target smart meters—like firmware tampering, not general viruses—and optimize it to use less than 5% of the device’s processing power. This kind of optimization is impossible with standard virus filters, which are built for devices with far more resources.
Then there’s the issue of protocol diversity. IoT networks use a huge range of protocols that most standard virus filters don’t support. You have MQTT for messaging between devices, CoAP for sensor data, Modbus for industrial control systems, Zigbee and Z-Wave for home IoT, and LoRa for long-range, low-power communications. Each of these protocols is designed for a specific purpose, and they don’t work like HTTP or FTP that most filters are programmed to understand. For example, MQTT uses publish-subscribe messaging where data is sent in small, frequent packets, not large files. A standard filter that’s looking for malicious code in full files will miss threats hidden in MQTT payloads. We spent two years developing our MQTT-specific scanning engine, and even then, we had to collaborate with protocol experts to make sure we were parsing every packet correctly without disrupting the device’s communication. When a sensor fails to send its temperature data because a filter is incorrectly interpreting the MQTT packet, that’s a bigger problem than a minor virus threat—especially in a hospital or manufacturing setting where that data is critical.
False positives are another persistent challenge, and they can be just as damaging as false negatives (missing a real threat). In one case, a grocery chain we worked with installed our filter on their refrigeration monitoring IoT network. The filter flagged a legitimate firmware update from a leading sensor manufacturer as malicious, because the update contained a code snippet that our engine had only seen in a few known attacks. The store’s technicians, thinking the filter was wrong, disabled it—and a week later, the network was hit with a worm that spread through the firmware, corrupting sensor data and leading to spoiled inventory. That’s when we realized how important it is for our filters to understand the “normal” behavior of each device on the network. We now incorporate device behavior baselining into our solution—learning what a sensor’s data should look like, what kind of firmware updates it should receive, and how it should communicate with other devices. This reduces false positives by more than 90%, but it requires upfront work to set up the baselines, and it’s different for every IoT network.
Update management is another headache. Traditional antivirus filters get regular, frequent updates—sometimes several times a day—to detect new viruses. But IoT devices often can’t handle frequent updates. Many are deployed in remote locations, like oil rigs in the middle of the ocean or rural water treatment plants, where getting a technician on-site to update a device takes weeks or months. If our filter requires a new update, we can’t push it automatically to every device like we would on a PC. We have to design updates that are small, tested, and can be applied without disrupting the device’s operation, and we have to coordinate with network managers to deploy them during scheduled maintenance windows. This is a completely different model from standard filter updates, and it means we have to build our solution to be flexible and non-intrusive, rather than relying on frequent, automatic changes.
Interoperability is also a major pain point. IoT networks often combine devices from dozens of manufacturers, each with their own security standards. Some manufacturers don’t want their devices’ data scanned, because they claim it’s proprietary, and others have their own security protocols that conflict with our filter. For example, we had a project with a smart city that had traffic lights from one company, streetlights from another, and parking sensors from a third. The traffic light manufacturer had its own encryption protocol that our filter couldn’t parse without permission, which they refused to give, because they said it would void their product warranty. We had to work out a middle ground where we scanned only the network traffic that wasn’t encrypted with their proprietary protocol, but that left a gap. It took six months of negotiations and testing to get them to agree to let us access the necessary data. This kind of interoperability issue doesn’t happen with standard networks, where most devices use common protocols that are widely supported.
Finally, there’s the problem of legacy devices. Many IoT networks have devices that are so old, the manufacturer is out of business and there are no firmware updates available. These devices are often the most vulnerable, but they can’t run standard virus filters because their hardware is too weak. We’ve worked with several hospitals that have medical IoT devices that are 10 or 15 years old, still in use because replacing them costs millions of dollars. These devices have no way to update their firmware, so we have to build our filter to work alongside them, monitoring their traffic for suspicious behavior rather than scanning the device itself. For example, if a legacy heart rate monitor starts sending data to a new, unknown IP address, our filter can flag that as a potential threat, even though it can’t scan the device’s internal code. But this approach is less effective than scanning the device itself, and it requires constant monitoring to catch new, unknown threats.
Over the past seven years, my team has learned that implementing a virus filter in an IoT network isn’t just about installing software—it’s about understanding the unique needs of each device, the constraints of the network, and the criticality of the data being sent. We’ve made mistakes, built solutions that didn’t work, and spent countless hours troubleshooting with our clients. We’ve also learned that one size definitely does not fit all when it comes to IoT security. A filter that works for a warehouse full of temperature sensors will not work for a network of smart traffic lights, and a solution for a hospital’s medical devices will crash a manufacturing plant’s motor controllers.
If you’re managing an IoT network and struggling to find a virus filter that works, I can tell you from experience that it’s not because you’re doing something wrong. It’s because the challenge is unlike anything we’ve faced with traditional networks. The good news is that specialized solutions exist—solutions that are built for low-power devices, support a wide range of IoT protocols, and prioritize reliability over the high-speed scanning that standard filters offer.
If you’re looking to secure your IoT network and want to discuss how a purpose-built virus filter can fit your environment, don’t hesitate to reach out to our team. We’d be happy to walk you through our approach, share case studies from similar networks, and work with you to find a solution that meets your specific needs.
Multiple Stages References
- International Telecommunication Union. (2021). Security of Internet of Things (IoT) Networks: Challenges and Solutions. ITU-T Recommendation X.1254.
- Gartner, Inc. (2022). Top IoT Security Challenges for Industrial and Enterprise Networks. Gartner Research Report.
- National Institute of Standards and Technology. (2020). Guidelines for Securing the Internet of Things (IoT). NIST Special Publication 800-218.
- Internet Engineering Task Force. (2019). Protocols for Low-Power IoT Networks: Security Considerations. IETF RFC 8428.
- Verizon. (2023). Data Breach Investigations Report: IoT Threat Trends. Verizon Enterprise Solutions.
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