By definition, cybersecurity is a highly volatile and ever-changing field. Ensuring an effective defense posture requires continuous threat monitoring, the development of defense strategies to address threats, and the implementation of new countermeasures. This challenge becomes even more complex when applied to the ever-evolving Internet of Things (IoT) domain. Traditional security controls simply don't work in an IoT environment that connects countless devices performing diverse tasks.
The Internet of Things (IoT) can be broadly understood as a network of computing devices equipped with sensors and IP addresses to communicate over the internet. The security of IoT devices is particularly challenging because their wide range of uses exceeds the scope of any single security solution. Furthermore, these devices are designed to be low-cost, low-power, and typically require only a simple password (if needed). This makes IoT devices extremely vulnerable to hacking.
How Artificial Intelligence Can Help IoT Network Security Through Data Analysis
If IoT devices excel at one thing, it's collecting data through countless sensors. Artificial intelligence (AI) can help IoT devices analyze unimaginable amounts of data in a very short time. The combination of AI and IoT can provide organizations with greater visibility and control, even those with numerous devices and sensors communicating via the internet. In other words, AI can transform the data collected by enterprises through IoT into valuable insights. This is especially important in protecting devices and networks from unauthorized access and infiltration attempts.
Security issues in the Internet of Things
Several factors make cybersecurity a challenging issue for IoT devices. The sheer scale and scope of the field are immense, with IoT comprising a diverse array of devices, each with its own operating system and security vulnerabilities. This heterogeneity makes IoT networks difficult to cover with a single defense system. Furthermore, due to their low design cost, IoT devices are typically low-power, energy-efficient, and lack built-in or minimal security frameworks. Moreover, each network consists of thousands or even millions of such devices feeding data over the internet, making the entire security proposition a virtual nightmare of incredible operational complexity. Even at a minimum, networks need to ensure regular updates to all operating systems and network applications, maintain an inventory of new assets, assess security risks, and detect potential targets. This is precisely why security professionals are turning to artificial intelligence when addressing IoT cybersecurity threats.
Artificial Intelligence in IoT Network Security
A fundamental step in building an IoT security framework is identifying all devices on the network. For large networks with millions of sensors and devices, this can be a daunting task. However, with artificial intelligence (AI), the discovery process becomes much easier, providing comprehensive and detailed information about the nature of the devices. Effective cybersecurity relies on identifying and monitoring every node in the network, and AI's identification and asset management capabilities make it highly effective in IoT cybersecurity.
Secondly, artificial intelligence can also aid in IoT cybersecurity through data analytics. AI doesn't get tired or fatigued and is more efficient than humans at continuously monitoring vast IoT networks for anomalies. Unfortunately, this also leads to many false alarms, as any anomaly can be interpreted as a potential vulnerability. However, this can be addressed by using machine learning and training AI to identify attack patterns. Unfortunately, our ability to model effective attack patterns is rather limited because real-world vulnerability data from actual attacks is rarely disclosed for privacy reasons, which restricts the quality of our analysis.
Applications of machine learning in the Internet of Things
Machine learning is extremely useful in identifying potential threats, discovering vulnerabilities in networks, identifying systemic IoT vulnerabilities (such as lack of or weak password protection on IoT devices), and addressing network configurations to build defenses. Machine learning works on massive cybersecurity datasets and IoT device profiles, making zero-day threats a concern for many companies. However, zero-day threats aside, machine learning has proven highly effective in combating DDoS attacks and improving the overall security posture of IoT networks. Leveraging its early threat identification capabilities, machine learning can also help manufacturers design more secure devices and release security patches in a timely and effective manner.
To further enhance network security for the Internet of Things (IoT), data from machine learning can also help IoT developers create more secure devices. By identifying vulnerabilities early, developers can release security patches or create new versions of devices to better protect users, where possible.
Because most IoT devices lack effective encryption and security frameworks, machine learning can efficiently provide adaptive and flexible IoT security at the network level. Furthermore, the cost outlook is more manageable for companies deploying IoT frameworks. The same approach can even be applied to home or smaller-scale IoT deployments to identify threats early and alert users to any anomalies in their networks.
