Defending IoT devices against security risks is critical, but the first step is detecting potential threats. If devices aren’t continuously monitored, significant damage could be done before a breach is discovered. This is where anomaly detection comes in; part of a complete approach to IoT security that empowers companies to defend, detect and react in the face of cyberthreats.
What is anomaly detection and why is it important?
Anomaly detection identifies activity that deviates from what is considered normal for an IoT device. That could be unusually high, or more frequent, data transmission. If a temperature sensor, for example, is programmed to report twice a day and suddenly starts sending data every hour, it is likely something is wrong.
In this example, the IoT device might not have been hacked, it could simply have a malfunction. Either way, you will want to know about it and as quickly as possible. If there has been a breach, you will need to identify and isolate it to stop further damage.
IoT security begins with defending devices, networks and systems but it is incomplete without the capability to detect anything out of the ordinary, and react when that happens.
A device suddenly appearing to communicate from another country would be another anomaly that could indicate trouble. If you don’t have visibility into your IoT devices and their traffic, you wouldn’t know if they had been compromised. Hackers could use the devices they access to launch attacks on other connected targets, or to take control of your IoT device.
Remember, IoT devices generally sit outside your enterprise’s perimeter, in unmanned environments where they can be significantly more vulnerable. For a cybercriminal, they could be an entry point into your enterprise’s systems for the purpose of data theft or to launch a ransomware attack. For all these reasons and more, IoT devices must be secured and monitored.
How does anomaly detection in the IoT work?
All companies have IT security risks to counteract. Companies need to recognise that crime units are organized crime syndicates, and are investing heavily, in artificial intelligence and machine learning. They are using the same tools that you as an organization are using.
Despite the damage that can be done, IBM Security/Ponemon Institute reported it takes a staggering 212 days on average to detect a data breach.
To turn this situation around, companies must monitor their connected devices and know what ‘normal’ looks like so they can spot anything that could indicate trouble. Once a weakness has been exposed, it can be further exploited. It takes constant vigilance not to succumb to the dangers.
Anomaly detection gives you visibility into your IoT devices and solutions and flags any activity that needs investigation. You can then act accordingly, for example by throttling bandwidth to stop the questionable device communicating into the network, or by isolating the device within a restricted zone.
Anomaly detection engines are device-agnostic and work together with an artificial intelligence (AI) program which analyses the data feed and scores any potential threats. Being AI-based, the program ‘learns’ what is normal according to thresholds set by the business rules that instruct the program.
What happens next can be automated or not, again according to the business rules. Direct action could be triggered to, for example, isolate a perceived threat. Alternatively, it could be sent for review; you may need this flexibility because a SIM may increase or cease communication for very genuine reasons.
The AI engine can also analyse identified anomalies to pinpoint types of attack. These could be distributed denial-of-service (DDoS),man-in-the-middle (MiTM) attacks, or device takeovers. Being service-based, anomaly and threat detection is fully scalable to match the size and scope of your IoT solution.
How anomaly detection fits into 360-degree IoT security
You should think about anomaly detection at the product or solution design phase. Too often, IoT security is an afterthought when solutions are already in the field.
This is a mistake, because the best outcomes result when companies are prepared, both in preventing attacks, but also in detecting and reacting to them should they happen.
Building automation into your solution’s security measures also helps you with cost management and time to act, by reducing dependence on labour-intensive manual tasks.
Anomaly detection is one piece of a whole IoT security picture, which Wireless Logic describes through its Security Framework. This was introduced as a 360-degree model, made up of a range of technology capabilities, standards and best practices which work together to defend, detect and react to cyber threats.