Implement Machine Learning to Secure Your IoT Network

By Zac Amos, Features Editor, ReHack

Connected devices pose unique security risks and are prone to bot attacks, requiring a tool capable of quickly processing a large amount of information. A machine learning model can rapidly react to attacks, making it an ideal choice for protection. An organization can implement it to secure its IoT network through threat detection, recognition and response.

Vulnerabilities of an IoT Network

More organizations are incorporating IoT devices, and concerns over network security grow because that brings additional vulnerabilities. It expands their connectivity at the cost of creating new surface areas for attackers to target. Implementing a security model is complex because the devices often lack basic security features.

Cybercriminals can exploit the connectivity with distributed denial of service (DDoS) attacks by overwhelming them with excess requests. For example, a single DDoS attack from 2022 lasted only 30 seconds but created 17.2 million requests and came from over 20,000 bots. Integrating IoT devices for businesses can be beneficial, but they need enhanced security.

1. Threat Monitoring

Most machine learning algorithms make predictions without long-term reasoning or real-world interactions, so they’re taught through the supervised learning of a data set. It can be fed information on previous cyberattacks or the current IoT network to establish how it informs its decisions. It can then identify patterns and make logical conclusions.

Many organizations use machine learning models to monitor and detect attacks because they can train on the huge amount of data IoT devices produce. They collect information in real time, so actions are well-informed and accurate. ML pays attention to anomalies and can send alerts if it finds a threat while monitoring.

2. Secure Data Collection

Previously, data leakage of sensitive consumer or user information was a growing concern with IoT and machine learning. However, that’s no longer the case with federated learning. It is a machine learning technique where algorithms are trained to access IoT device data without exchanging information, meaning there isn’t a central data set it uses to store any.

Federated learning allows machine learning to occur securely because the data is unidentifiable and doesn’t need to be accessed directly. Its decentralized nature ensures its protection. In addition, its actions are more secure since the model is informed by safe data collection.

3. Incident Response

Machine learning can secure IoT networks through incident response. It sends alerts once an attack occurs and creates defensive patches without human input or intervention. Since it reacts in real time, it can respond to a threat much faster than a human would.

4. Threat Recognition

A machine learning algorithm analyzes data sets and identifies patterns to rapidly detect a cyberattack. Once it recognizes actions similar to the data, it classifies it as a threat. Since it can quickly identify potential cyberattacks, the response time for dealing with them will be much faster.

5. Patching and Updating

IoT devices are often unprotected, making it easy for attacks to access them. People don’t tend to treat them as a security threat since they’re regular machines, but they’re more open to cyberattacks when not patched or updated properly. Machine learning can also ensure continuous security for an IoT network because it can address weaknesses as soon as it detects them.

A predictive model can use past data to determine the best solutions for each vulnerability without human input. Essentially, it mimics decision-making and fixes each one using its extensive knowledge. It can continue working in the background to repair security gaps before they’re a known issue or leave the final decision up to the cybersecurity team.

6. Risk Assessment

A machine learning model offers insights into a network’s security based on the data it collects. While it can use past information to inform its current decisions, it also can enhance traditional risk assessment by collecting real-time data from IoT devices.

Machine learning can be embedded at the edge of an IoT network to offer predictive and intelligent risk analysis for devices. It continuously assesses everything and can warn of concerning changes. The result is heightened security since it provides awareness about the current state of the IoT network.

7. Risk Prediction

Since machine learning is capable of rapid analysis, it can detect patterns and make inferences at speeds people cannot match. It’s not constrained by human processing limits and doesn’t require much time to think or analyze.

Cybersecurity threats constantly evolve, so accurate manual risk prediction would take too long. Around 99% of cyberattacks are created by making minor alterations to previous attacks to create something new that appears nonthreatening. Therefore, they’re treated as harmless traffic through an IoT network. A machine learning model can combat this with risk prediction.

With continuous data collection, it can learn the preferences of attackers and align them with potential system vulnerabilities to find likely targets. It can then logically conclude when the next attack will occur. Ultimately, it can improve the resiliency of an IoT network against attacks.

Secure IoT Networks with Machine Learning

Connected devices are prone to bot attacks that quickly overwhelm them, so a rapid detection and response tool is necessary. Machine learning models can accurately predict threats, patch vulnerabilities automatically and respond to incidents without human intervention. They can secure IoT networks in multiple ways to enhance security.

About the Author

Zac Amos is the Features Editor at ReHack, where he covers cybersecurity and the tech industry. For more of his content, follow him on Twitter or LinkedIn.