AI solutions for IoT security: How Artificial Intelligence protects low-resource devices

IoT devices are created to perform specific functions, so their technical specs are naturally quite limited. They are unlike smartphones or tablet computers that come with powerful processors and large data storage. Putting traditional security mechanisms like encryption and intrusion detection systems on these devices is impractical. Installing full-fledged security solutions in them is out of the question, let alone AI-powered systems.

However, this does not mean that AI cannot be used to secure IoT devices or entire IoT ecosystems. Here’s a rundown of how AI solutions for IoT are harnessing the benefits of AI to protect IoT and other low-resource devices, including actuators, sensors, wearables, and microcontrollers.

Establishing safe activity baselines to detect anomalies

One of the top security AI solutions for IoT is anomaly detection which is not solely based on rules and threat signatures. AI can look into behaviors to detect potential threats even without prior knowledge about such threats. Advanced security solutions that use artificial intelligence can scan network activity and the behavior of devices to establish a baseline of regular or safe activity. With this benchmark of safe activities and behaviors, it becomes easier to spot malicious activities and respond to them accordingly.

AI collects data on device behavior, environmental conditions, network traffic, and other relevant aspects that can be deemed threats or attacks. The data is then processed through an anomaly detection algorithm to look for malicious behavior or indicators of attacks such as unusual data movements, increased requests for more permissions or escalated privileges, and attempts to access data that is not necessary to a function.

One or two instances of unusual behavior may not be actual threats, so it is important to detect patterns or features. If these benign instances are treated as threats, the result may be excessive false positives, which can negatively impact incident response. This mindfulness of false positives or inaccurate security alerts is particularly important when overseeing IoT security because of the number of devices involved.

Manually setting baselines for safe activity is impractical and may be virtually impossible in some cases. It is unlikely for human security analysts to sufficiently cover all activities in an organization’s network, especially when there is an endlessly expanding number of IoT devices involved. It would be extremely difficult to create the corresponding rules or parameters that would distinguish safe from harmful or malicious activities. AI-assisted anomaly detection is arguably the only viable option.

Automatic and continuous monitoring

Nowadays, it is inexpedient and self-harming to refuse to use new tools and technologies to address cyber threats. Effective IoT security requires constant monitoring, which is only practicable with automation and artificial intelligence. AI-powered security solutions can continuously monitor low-resource devices without the need to install clients in them. They can observe various activities and analyze the resulting data to detect potential threats.

One example of an IoT device activity that may only be promptly detected and addressed through automation and continuous monitoring is the violation of communication patterns. The examination of logs may reveal the violations, but it may already be too late to stop the problem if organizations only perform log analysis periodically. Continuous monitoring ensures the timely detection and remediation of security issues.

Automatic and continuous monitoring is also important in detecting the unusual presence of several events together like massive file encryption and deletion, which can be indicative of an ongoing ransomware attack. Similarly, it is important to continuously monitor activities to spot the abnormal absence of certain events like update request failures, the atypical value of a variable detection (which can entail an unusual connection to internal IPC), new combinations of several variables, and command execution sequence anomalies.

Proactive threat detection and response

Artificial intelligence makes IoT security monitoring proactive in three main ways: getting useful insights, preventing emerging issues from festering, and ensuring that red flags are addressed appropriately.

The first is about being able to gain meaningful insights amid a deluge of security alerts and events information. Dealing with IoT devices usually means the possibility of getting swamped with tons of irrelevant information, which slows down the analysis and detection of security incidents. AI allows organizations to correlate data and extract only the information that is pertinent to establishing a solid security posture. AI-powered security tools can even present actionable insights to make it easy to address threats.

AI is also useful in stopping egressing threats from worsening into more serious problems. For example, when devices malfunction, conventional cybersecurity systems may not regard the incident as a potential threat. With AI, the details of the malfunction are automatically obtained and the possible impact to the system is laid out. This enables organizations to address issues that may appear trivial but could be harbingers of severe attacks.

On the other hand, AI significantly helps in revealing events or prioritizing crucial alerts that are often ignored in conventional security solutions. For example, cases of repeated authentication attempts and unusual changes in IP addresses can happen to IoT addresses without network administrators noticing them. These can mean possible security breaches that fly right under the nose of device users and IT teams. AI-backed security solutions can detect these instances and make it clear to the security team that they should not be routinely dismissed or ignored.

Artificial intelligence contributes significantly to creating proactiveness in dealing with threats involving devices that often get little attention when it comes to cybersecurity. It facilitates active threat mitigation to maximize security and network integrity.

In summary

As IoT adoption expands exponentially, it is important to emphasize the need to secure these low-resource devices. They can pose serious risks, especially as they are often being treated with little regard for cybersecurity. These devices tend to be added, removed, or re-added to networks without efforts to ensure that they are safe. As such, it is crucial to adopt a security strategy that makes it possible to impose security controls on low-resource devices that are incapable of running the usual defensive solutions in them.

This is where artificial intelligence provides an effective and easy-to-implement solution. AI-powered cybersecurity platforms can provide automatic and continuous anomaly detection, behavior analysis, and the means to proactively address threats. Integrating AI solutions into the fabric of IoT security enables the building of a more resilient and secure IoT ecosystem.

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Peter Davidson works as a senior business associate helping brands and start ups to make efficient business decisions and plan proper business strategies. He is a big gadget freak who loves to share his views on latest technologies and applications.